Performance evaluation of a radar-on-chip system for obstacle detection strategies for urban air mobility (UAM) applications

Menichino, Aniello; Inverno, Michele; Del Core, Giuseppe; Di Vito, Vittorio; Ponte, Salvatore; Ariante, Gennaro; Abategiovanni, Federica

doi:10.1108/JTAS-01-2025-0007

Purpose

The transition to sustainable urban air mobility (UAM) is critical for decongesting ground traffic and reducing urban CO₂ emissions through the use of electric aerial vehicles. However, this sustainability potential is contingent on solving the foundational challenge of safe, autonomous operations in dense urban airspace. This paper, therefore, analyses a “radar-on-chip” millimeter-wave Frequency Modulated Continuous Wave (FMCW) radar sensor (Texas Instruments IWR1642) as a core component for a detect-and-avoid (DAA) system, evaluating its performance as a key enabler for the safe and viable implementation of sustainable UAM.

Design/methodology/approach

Experimental results demonstrate that the IWR1642 is both precise and reliable in tracking moving objects, whether their motion is radial or transverse. Data processing was performed using the mmWave Demo Visualizer software, which utilizes the radar's basic detection algorithm. Using Geographical Positioning System (GPS) positions as reference, the radar's tracking of moving targets was found to be highly consistent, with only minor percentage errors during the data acquisition process. The radar was successively mounted onboard an unmanned aerial vehicle (UAV) to simulate autonomous operations in UAM scenarios, designing and developing a dedicated structure to house the sensor. The IWR1642 radar sensor demonstrated potential suitability for use in future UAM applications, providing accurate and reliable detection capability under a variety of conditions.

Findings

This work outlines the studies about possible solutions, identified by authors that could support beyond line of sight (BVLOS) operations in a medium- and low-risk environment, in particular, during UAM missions. UAM is considered a potential technology for future urban and suburban transportation, for both goods and passengers. By exploiting the vertical dimension, it decongests roads that are unable to support current city traffic while reducing CO₂ emissions into the atmosphere, thereby moving toward a more eco-sustainable (through the use of exclusively electric vehicles) and multimodal transport system, in line with the concept of smart cities. This manuscript focuses on the operational limitations of the radar-on-chip system, as opposed to its functional constraints, emphasizing both the characterization phase and its performance during operational applications, such as in-flight testing. Based on the obtained results, the authors believe that the highlighted operational limitations could be mitigated by integrating additional sensors, such as light detection and ranging, to complement the radar-on-chip system.

Originality/value

This work represents a preliminary step toward the development of a robust obstacle detection system to be integrated onboard UAVs for UAM operations. UAS predominantly operate at very low altitudes (VLL, <500 feet), where the density of obstacles and human activity is significantly higher. Many of these aircraft function autonomously or in self-flying modes, necessitating rigorous safety measures to ensure mission reliability and operational safety. In this context, the ability to accurately perceive the surrounding environment is critical for UAM missions, particularly during BVLOS operations or fully autonomous missions. This study aims to develop a reliable obstacle detection system utilizing low-cost technologies.

Acronyms

AAM: Advanced Air Mobility
ADC: Analog-to-Digital Conversion
AoA: Angle of Arrival
ASSURED UAM: Acceptance Safety and Sustainability Recommendations for Efficient Deployment of UAM (European project)
BIST: Built-In Self-Test
BVLOS: Beyond Visual Line of Sight
CAN: Controller Area Network
CIRA: Italian Aerospace Research Centre
CTR: Controlled Traffic Region
DAA: Detect and Avoid
DEP: Distributed Electric Propulsion
DSP: Digital Signal Processor
DSS: Digital Signal Subsystem
FFT: Fast Fourier Transform
FMCW: Frequency Modulated Continuous Wave
GCS: Ground Control Station
GNSS: Global Navigation Satellite System
GPS: Global Positioning System
INS: Inertial Navigation System
JTAG: Joint Test Action Group
LNA: Low-Noise Amplifiers
MIMO: Multiple Input Multiple Output
ML: Machine Learning
MSS: Master Subsystem
PA: Power Amplifier
RCS: Radar Cross-Section
SBC: Single Board Computer
SNR: Signal-to-Noise Ratio
UAM: Urban Air Mobility
UART: Universal Asynchronous Receiver-Transmitter
UAS: Unmanned Aircraft System
UAV: Unmanned Aerial Vehicles
USB: Universal Serial Bus
VLL: Very Low Level
VTOL: Vertical Take-off and Landing
VLOS: Visual Line of Sight

Introduction

Urban air mobility (UAM) has been proposed as potential solution for urban transport challenges, for both goods delivery and passenger transport. This new air transportation modality could be effective in reducing traffic congestion during daily commutes around cities while also promoting transport decarbonization and more sustainable mobility due to the use of exclusively electric vehicles. UAM is defined as a new concept based on the use of vertical take-off and landing (VTOL) vehicles for air transportation of people and goods in urban and suburban areas (ASSURED UAM project team, 2021; Ariante and Del Core, 2025). Indeed, UAM aims to adopt DEP to be mounted on VTOL aircraft that will be able to carry a few passengers, from 2 up to 7, reducing CO₂ emission and noise level (Menichino et al., 2022; Di Vito et al., 2023).

However, existing technologies and regulations only allow the implementation of the UAM concept with conventional helicopters. Nevertheless, based on publications and recent projects of the last decade, UAM is expected to be the future of the metropolitan urban mobility (Almaita et al., 2019). The development phase for UAM vehicles is well underway, with numerous companies having successfully built and flight-tested full-scale prototypes over the past decade (ASSURED UAM project team, 2021). Most UAM prototypes are designed with more than four rotors and propellers, utilize electric propulsion systems and can carry between two and five passengers. These prototypes exhibit flight characteristics more like helicopters than fixed-wing aircraft, operating at relatively low altitudes and in proximity to buildings. The general public is accustomed to the safety standards associated with large transport aircraft and regional fixed-wing aircraft used in commercial air transport (Goyal et al., 2018; Hasan, 2019).

UAM vehicles will have the best chances for full-scale implementation, provided that they are focused on safety, smart operations and connected under the supervision of a centralized platform. Safety, of course, always needs to be the priority, so any UAM vehicle needs to be outfitted with power redundancy and backup systems. Furthermore, UAM vehicles have to be conceived as “smart” vehicles with high autonomy level, which not only obviates the need for an onboard pilot and the associated costs but also enhances safety and makes the vehicle more controllable (Cohen et al., 2021; Ariante and Papa, 2024). An important role in the integration of UAM in the city context will be played by obstacle detection and avoidance systems. Thanks to the presence of those technologies, drones and unmanned aerial vehicles (UAVs) in general will be able to integrate perfectly into urban space, avoiding collisions with other aircraft, buildings, people and other obstacles (Menichino et al., 2023a, b). These systems, therefore, guarantee the safety of remote piloting, both under visual line of sight (VLOS) and beyond line of sight (BVLOS) operations, thanks to the presence of numerous sensors working at high frequencies (30–300 GHz, millimeter waves). These systems are currently being developed using different typologies of sensors (Yasin et al., 2020) (active or passive or combined) working in all operative conditions and overcoming the limits due to individual sensor usage.

These technologies are referred to as detect-and-avoid (DAA) systems that are designed to ensure safe flight of manned aircraft by detecting other aircraft in the airspace, identifying potential collision hazards and executing necessary maneuvers to avoid collisions with intruding aircraft (Yu et al., 2015; Besada et al., 2021). Despite the presence of transponders or radars in manned aircraft, mid-air collision avoidance is predominantly reliant on the human pilot's eyesight, as noted by the Federal Aviation Administration (FAA). It is well recognized that both human judgment and technological systems play crucial roles in the DAA processes for manned aircraft. In the context of UASs, DAA encompasses methods for detection and avoidance of collision with other aircraft or obstacles (Abro et al., 2022). This can be achieved either through onboard systems or with the involvement of a ground station (Ariante et al., 2022).

Typically, DAA sensor data include bearing angles (azimuth and elevation), range and relative velocity. A single sensor type often excels in measuring one specific data type. The complete relative trajectory is then calculated based on multiple sensors or additional data measurements. Commonly used DAA technologies for sensing include visual cameras, Global Navigation Satellite System (GNSS) sensors, Inertial Navigation System (INS) systems, thermal cameras, Light Detection and Ranging (LiDAR), radar and sonar (Moore, 2019; Ariante et al., 2019, 2020; Zhang and Hsu, 2018; Papa et al., 2018; De Haag et al., 2016; Alsalam et al., 2017).

However, UAVs predominantly operate in complex indoor, urban and confined environments where GNSS signals may be unavailable, and 5G connectivity might be insufficient for reliable data transmission. Consequently, the integration of on-board sensors, such as radar and other sensing technologies, becomes essential to ensure safe navigation (Lies et al., 2020; Siewert et al., 2020). The effectiveness of sensing and path-planning algorithms is strongly influenced by the computational capabilities of the UAV, the precision of its sensors and the degree of knowledge about the surrounding environment. Moreover, machine learning (ML) and neural network techniques have demonstrated significant advancements in various emerging fields, including robotic prediction and UAV operations (Back et al., 2020; Pedro et al., 2021; Lai and Huang, 2020; Fraga-Lamas et al., 2019).

The Texas Instruments (TI) IWR1642 is a millimeter-wave on-chip Frequency Modulated Continuous Wave (FMCW) Doppler radar, commonly used in the automotive industry. The novelty of this work lies in testing and characterizing the sensor for the development of a robust obstacle detection system tailored for UAM applications. Specifically, after the sensor's characterization, a dedicated structure was designed and developed to house the radar, as well as to accommodate additional sensors for future enhancements.

The primary objective of this study is to characterize the sensor's behavior under dynamic conditions. A preliminary investigation, based on static tests, was previously conducted by the authors to verify the sensor's maximum range, accuracy and precision (Menichino et al., 2023a, b). This work examines the dynamic behavior of the sensor in various scenarios (indoor and outdoor) and different chirp configurations, culminating in flight tests with the sensor mounted on board a UAV. The paper is organized as follows. Section 2 presents the main parameters of the FMCW radar and basic concepts of radar operation, and Section 3 provides a detailed description of the hardware components used. Section 4 presents the simulations and results obtained from ground-based campaigns. An in-depth error analysis is carried out in Section 5, followed by an illustration of the flight tests in Section 6. Concluding remarks and future developments are discussed in Section 7.

Chirp parameters, range and velocity measurements

In millimeter-wave FMCW radar sensors, the transmitted signal is a linear chirp, i.e. a sinusoidal tone with linear frequency modulation, defined by the start frequency $f_{c}$ ⁠, the bandwidth (B), the frequency slope S and the duration $T_{c}$ (Figure 1(a)). The time-domain expression of a linear chirp (also known as quadratic-phase signal) is:

x (t) = \sin [2 π (f_{c} t + S \frac{t^{2}}{2}) + ϕ_{0}]

(1)

for $t \in$ [0, Ramp End Time]; $ϕ_{0}$ is the initial phase (i.e. at t = 0).

A single chirp can provide information about the object's range R by mixing (multiplying) the transmitted signal and the received echo (a chirp delayed by $τ = 2 R / c$ ⁠, where $c = 3 \cdot 10^{8}$ m/s is the speed of light in free space and, with good approximation, in air) and obtaining a “beat signal,” i.e. a sinusoidal tone with constant frequency Sτ (beat, or intermediate, frequency – IF) and initial phase equal to $2 π f_{c} τ$ (Figure 1(b)), or $4 π R / λ$ ⁠, since $f_{c} / c = 1 / λ$ ⁠, where λ is the wavelength. If multiple targets are present at different ranges, multiple chirps will be received, each delayed by a different amount of time proportional to their range to the sensors. The total received signal will contain different IF signals whose frequencies are measured via Fourier transform processing (the “range Fast Fourier Transform (FFT)”), deriving the targets’ ranges.

Figure 1

A chirp cycle time schematic showing T X timing, A D C sampling, frequency ramp, and T X and R X chirp plots”.

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The schematic diagram shows a multi-panel layout describing chirp timing and frequency behavior”. On the left, a horizontal timeline, labeled “a”, shows “Chirp Cycle Time” with a double-headed arrow spanning the full width”. Six vertical dashed lines are present labeled, from left to right, “Turn Off T X”, “Turn On T X”, “Ramp Start”, “Start A D C Sampling”, “End A D C Sampling”, and “Ramp End”. A wavy signal labeled “Frequency Start” begins at the far left, declines, constant, rises steeply, and ends at the far right. Below this between “Start A D C Sampling” and “End A D C Sampling”, a dotted horizontal line appears with a short curvy upward arrow indicating angle labeled “Frequency Slope”. A double-headed arrow labeled “Idle Time” spans “Turn Off T X” and “Ramp Start”. A right arrow labeled “A D C Valid Start Time” spans “Ramp Start” and “Start A D C Sampling”. A right arrow labeled “A D C Sampling Time” spans “Start A D C Sampling” and “End A D C Sampling”. A right arrow labeled “Ramp End Time” spans “Ramp Start” and “Ramp End, aligned with the numeric text “0” appears at the start of the ramp”. A left arrow labeled “T X Start Time” spans “Ramp Start” and “Turn on T X”. A double-headed arrow at the bottom labeled “Transmitter is O N” spans “T X Start Time” and “Ramp End”. On the right, two vertically stacked plots, is labeled “b”. The vertical axis is labeled “f”, and the left-side label reads “S subscript tau”. The horizontal axis is labeled “t”. In the upper plot, a rising diagonal line is labeled “T X chirp”, and a second rising diagonal line offset to the right is labeled “R X chirp”. A horizontal double-headed arrow between two vertical dashed lines is labeled “T subscript c”. In the lower plot, a left horizontal arrow is labeled “I F signal”. A double-headed horizontal arrow near the origin is labeled “tau” in both.

(a) Linear chirp signal profile and (b) beat frequency (IF signal), equal to Sτ. Source: https://www.ti.com/lit/an/swra553a/swra553a.pdf?ts=1690741218091

The configurable chirp parameters are.

Initial frequency (⁠ $f_{c}$ ⁠), which indicates the initial instantaneous frequency of the chirp. For IWR1642, it is equal to 77 GHz.
Slope (S) of the linear instantaneous frequency variation. For IWR1642, the maximum slope value is 100 MHz/μs. For a 40-μs chirp, the maximum chirp bandwidth is 4 GHz (from 77 to 81 GHz).
Idle time, i.e. the time between the end of one chirp and the start of the next one in a frame (approximately 7 μs for IWR1642).
TX start time, indicating the beginning of signal transmission.
Analog-to-digital conversion (ADC) valid start time, i.e. the time at which sampling begins.
Ramp end time, i.e. the time at which the linear frequency modulation stops. It is given by the sum of ADC valid start time, ADC sampling time and extra time after the end of the sampling.
Number of samples $N_{s}$ obtained by the ADC in the sampling interval.
Sampling frequency (⁠ $f_{A D C}$ ⁠) of the IF signal, given by the ratio between the number of samples and the sampling time.
Chirp cycle time (⁠ $T_{c}$ ⁠), i.e. the total duration of a chirp, given by the sum of the ramp end time and the idle time.

Detecting the velocity of a target requires the transmission of a sequence of N equally spaced chirps (a “frame”), with $N \geq 2$ ⁠. If N = 2 (single moving target at radial speed $v$ ⁠), the phase difference Δϕ between the two transmitted chirps will correspond to a range variation due to the motion of the object of $v T_{c}$ ⁠, i.e. $Δ ϕ = 4 π v T_{c} / λ$ ⁠, and the target radial speed is given by:

v = \frac{λ Δ ϕ}{4 π T_{c}}

(2)

If multiple moving objects with different speeds and the same range are present, Δϕ will contain phase contributions from each target, and N equally spaced chirps (a frame) are transmitted. The speeds are derived by FFT of the N echoes (the “Doppler FFT”).

Range resolution (⁠ $d_{r e s}$ ⁠) refers to the smallest distance between two objects (with the same bearing and assumed equally reflective) at which the radar can distinguish between them. As is well known, $d_{r e s}$ is related to the chirp bandwidth (⁠ $B = S T_{c}$ ⁠) by:

d_{r e s} = \frac{c}{\begin{array}{c} 2 B \\ 2 \end{array}}

(3)

where c is the speed of light. For 4-GHz chirp bandwidth, the range resolution is 3.75 cm.

Velocity resolution (⁠ $v_{r e s}$ ⁠) refers to the minimum radial velocity difference between two objects that allows them to be distinguished as separate targets by the radar system. This parameter is crucial for accurate tracking of moving objects. To avoid ambiguity in velocity estimation, condition <2π must be satisfied. From (2), this implies that the maximum unambiguous velocity is $| Δ ϕ | < 2 π$ when there is a single moving target. When transmitting a frame of N chirps, with duration ${N T}_{c}$ ⁠, the velocity resolution is given by (Alabaster, 2012):

v_{r e s} = \frac{λ}{2 {N T}_{c}}

(4)

Angular resolution (⁠ $ϑ_{r e s}$ ⁠) defines the minimum angular separation between two objects that allows them to be distinguished as separate targets and is dependent on the number of receiving antennas (⁠ $N_{R X}$ ⁠). Assuming, for example, two receiving antennas and a planar-wavefront observation geometry, the differential distance ΔR from the target to the RX antennas is given by $ℓ \sin ϑ$ ⁠, where $ℓ$ is the spacing between the antennas and ϑ is the angle of arrival (AoA). The corresponding measured phase difference is $Δ φ = 2 π Δ R / λ = 2 π ℓ \sin ϑ / λ$ ⁠, from which the AoA is derived:

ϑ = \sin^{- 1} (\frac{λ Δ φ}{2 π ℓ})

(5)

The estimation accuracy of the AoA ϑ depends on ϑ and is better when ϑ ≪ 1, so that $\sin ϑ ≅ ϑ$ and Δφ depends linearly on ϑ. To enhance $ϑ_{r e s}$ and improve the accuracy of angle measurements, Multiple Input Multiple Output (MIMO) radars are employed. These systems require at least two transmitting antennas (⁠ $N_{T X} \geq 2$ ⁠). By leveraging this configuration, a virtual array with $N_{R X} x N_{T X}$ elements is synthesized, effectively increasing the number of independent channels and improving the spatial resolution of the radar system. The angular resolution (in degrees) achieved by this approach is given by:

ϑ_{r e s} = \frac{λ}{ℓ N_{R X} N_{T X} \cos ϑ} \times \frac{180}{π} (\deg)

(6)

The effective detection of far-off objects is constrained by the signal-to-noise ratio (SNR) and the IF bandwidth supported by the radar system. The maximum range (⁠ $d_{\max}$ ⁠) of the radar system is directly related to the IF bandwidth as follows:

d_{\max} = \frac{{I F}_{\max} c}{\begin{array}{c} 2 S \\ 34 \end{array}}

(7)

where $I F_{\max}$ is the maximum IF bandwidth supported.

Hardware description

The setup (Figures 2 and 3) is composed by the following components.

Figure 2

A block diagram showing a P C connected to an I W R 1642 BOOST Board and an A C D C Power Supply.

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The block diagram shows three rectangular elements connected by arrows on a plain background. On the left, a rectangular box contains an open laptop illustration, and centered text below it reads “P C”. On the right, a rectangular box contains a red electronic board illustration with visible pins, and text above it reads “I W R 1642 BOOST Board”. Below the board, a smaller rectangular box contains an illustration of a wall plug power adapter, and centered text below it reads “A C D C Power Supply”. A vertical arrow points upward from the “A C D C Power Supply” box to the bottom side of the “I W R 1642 BOOST Board” box. A horizontal arrow points to the right side of the “P C” box from the left side of the “I W R 1642 B O O S T Board” box. The boxes are outlined with thin borders.

Set-up architecture. Source: Created by authors

Figure 3

A laboratory photograph showing a laptop connected to an electronic board and test equipment on a workbench.

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The photograph shows a laboratory workbench setup with electronic equipment arranged horizontally. On the right, an open laptop computer is placed on the table, displaying a software interface with multiple text fields, buttons, and numeric entry boxes on the screen, with a keyboard and touchpad visible below. A cable is connected to the right side of the laptop and extends leftward toward external hardware. On the left side of the table, a rectangular electronic development board is mounted on a flat base, with multiple cables connected to it, including a cable running to the laptop and additional power and signal cables extending outward. Small illuminated indicators are visible on the board surface. Behind the board, a rack mounted electronic instrument is positioned, showing a front panel with a digital numeric display and control elements. Additional stacked equipment is visible further in the background on shelving. Several loose cables are coiled and laid across the table surface, connecting the laptop, the development board, and the test equipment. The components are arranged in a single workspace environment with all devices connected by visible wired links.

The system during the configuration process. Source: Created by authors

AC-DC power supply;
IWR1642-BOOST board and
PC with TI mmWave Studio software.

Data processing involves different levels, with each level managing a radar subsystem (RADARSS), as illustrated in Figure 4, where.

Figure 4

A flowchart shows processing stages from “Analog F E” and “Digital F E” down to “Scene Interpretation” and “Visualization”.

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In the center is a tall vertical flowchart composed of stacked rounded rectangular boxes connected by vertical segments and arrows. On the left side of the top section, left-aligned text reads, “Front End Radar Processing”. To the right of this text, a rounded rectangular text box reads, “Analog F E”. Directly below it, a rounded rectangular text box reads, “Digital F E”. A vertical connector leads downward from “Digital F E”. A horizontal dashed line spans across the diagram at this level. On the far left, aligned with this level, text reads, “A D C Samples”. Below, on the left side, left-aligned text reads, “Low Level Radar Processing”. Centered in this section, a rounded rectangular text box reads, “Range slash Doppler F F T”. A vertical connector leads downward to another rounded rectangular text box that reads, “Range slash Dopple slash Angle Processing”. On the right side of this middle section, vertically oriented text reads, “I V R 1642 E V M”. A horizontal dashed line spans across the diagram. On the far left, aligned with the next boundary, text reads, “Point Cloud”. Below, on the left side, left-aligned text reads, “High Level Radar Processing”. Centered beneath, a rounded rectangular text box reads, “Localization”. A horizontal dashed line spans across the diagram. On the far left, aligned with the next boundary, text reads, “Target List”. At the bottom section, on the left side, left-aligned text reads, “Matlab”. Centered are two rounded rectangular text boxes stacked vertically. The upper box reads, “Scene Interpretation”. A short vertical connector leads to the lower box, which reads, “Visualization”. On the right side of this bottom section, vertically oriented text reads, “H O S T P C”. A large downward-pointing arrow extends from below the “Visualization” box.

Data processing phases. Source: Created by authors

Front-end processing. This stage entails the configuration of the sensor by the Demo Visualizer and the radar interface, interacting with the external environment through analog and digital front-end components.
Low-level processing. Here, the samples received from the ADC are processed by the digital signal processor (DSP). Range and Doppler FFT are performed.
High-level processing. Leveraging the microcontroller (MCU) ARM RF4, a cloud point is generated, representing the spatial coordinates of the targets. These coordinates are derived from the information obtained in the low-level processing stage and enable localization. Additionally, the velocity information for each target is obtained.
Post-processing. The processed data are further analyzed and displayed in the MATLAB^® environment.

Sensor: radar-on-chip IWR 1642

The TI IWR1642 device (Figure 5) is an integrated single-chip mm-wave sensor based on FMCW radar technology, operating in the 76- to 81-GHz band with up to 4 GHz continuous chirp (Texas Instruments datasheet, 2024).

Figure 5

Two electronic components showing a chip package top with “Texas Instruments” text and a bottom view with solder balls.

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The photograph shows two square electronic components placed side by side on a plain background. On the left, a flat square chip package is shown with a smooth surface, and white printed text reads “Texas Instruments”, accompanied by the Texas Instruments logo positioned to the left of the text. The edges of the chip are straight and uniform, forming a thin square profile. On the right, a similar square chip package is shown from the opposite side, revealing a dense grid of evenly spaced spherical solder balls arranged across the entire surface. The solder balls are uniform in size and aligned in rows and columns, covering the square area except for narrow margins near the edges. The two components are aligned horizontally, with the top surface view on the left and the ball grid array underside view on the right.

Radar-on-chip IWR 1642 – Texas instruments. Source: https://www.ti.com/product/IWR1642

IWR1642 includes a monolithic implementation of an MIMO (2TX, 4RX) system and integrates a DSP subsystem (C674x) for radar signal processing. The device includes an ARM R4F-based processor subsystem, which is responsible for front-end configuration, control and calibration. Simple programming model changes can enable a wide variety of sensor implementations, with the possibility of dynamic reconfiguration for implementing a multimode sensor. Table 1 shows the performance values of IWR1642.

Table 1

IWR1642 performance

Performance	Value
Maximum distance(m)	84.5
Range resolution (m)	0.0367
Maximum unambiguous velocity (km/h)	29.3
Velocity resolution (km/h)	0.46

Evaluation board: IWR1642-BOOST

The TI evaluation board IWR1642-BOOST (Figure 6) is aimed to be used for the IWR1642 sensor performance assessment, enabling direct connectivity to the MCU LaunchPad™ Development Kit. The BoosterPack™ allows developing software for on-chip C67x DSP core and low-power ARM® R4F controllers, including onboard emulation for programming and debugging, as well as onboard buttons and light-emitting diodes for quick integration of a simple user interface. This kit is supported by mmWave tools and software, including mmWave Studio and the mmWave software development kit (MMWAVE-SDK) (Texas Instruments datasheet, 2024). The main features are.

Figure 6

A red printed circuit board with “Texas Instruments” branding and mounted electronic components.

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The photograph shows a rectangular red printed circuit board oriented diagonally on a light background. The board has rounded corners, and four circular mounting holes are located near each corner. At the center of the board, white printed text reads “Texas Instruments”, with the Texas Instruments logo positioned above the text. Multiple surface mounted electronic components are distributed across the board, including integrated circuits, resistors, capacitors, and connectors. Near the upper left area, a long black board to board connector is mounted horizontally. Near the upper right area, a large black square integrated circuit is positioned adjacent to a light gray rectangular patterned region printed on the board. On the lower left area, a green two pin terminal block is mounted vertically. Near the lower center edge, a black barrel power jack protrudes outward from the board. Along the lower edge, a small metallic connector is visible. On the right side of the board, a white rectangular label with a Q R code and alphanumeric text “5 6 5 9 7 0 0 0 7 7” is present, and another white label reads “I W R 1642 BOOST”. Fine white silkscreen markings, reference designators, and circuit traces are visible across the entire surface of the board.

Texas instruments evaluation board IWR1642-BOOST. Source: https://www.ti.com/tool/IWR1642BOOST?keyMatch=&tisearch=search-everything&usecase=hardware

Onboard antenna to enable field testing;
XDS110-based JTAG with serial port interface for flash programming;
Universal Asynchronous Receiver-Transmitter (UART)-to-Universal Serial Bus (USB) interface for control, configuration and data visualization;
TI LaunchPad interface to seamlessly connect to TI MCUs and
Controller area network (CAN) connector, which enables direct interface to target units.

The signal processing chain, visualized in Figure 7, consists of.

Figure 7

A flowchart of m m Wave radar processing blocks from “m m Wave Front End” to “Send out detected object data to U A R T port”.

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On the far left, a rounded rectangular text box reads, “m m Wave Front End”. Below this box, smaller text reads, “B S S Firmware”. A right-pointing arrow from the “m m Wave Front End” box leads to a larger rectangular container. Inside the large rectangular container, arranged horizontally from left to right, are six rounded rectangular boxes connected by right-pointing arrows. Centered beneath this container is the text, “D S S”. The first rounded rectangular box inside the container reads, “Radar Data Capture”. A right-pointing arrow leads from this box to the second rounded rectangular box, which reads, “first dimension F F T”. A right-pointing arrow leads from the “first dimension F F T” box to the third rounded rectangular box, which reads, “second dimension F F T”. A right-pointing arrow leads from the “second dimension F F T” box to the fourth rounded rectangular box, which reads, “Object Detection (C F A R)”. A right-pointing arrow leads from the “Object Detection (C F A R)”, box to the fifth rounded rectangular box, which reads, “Direction of Arrival Estimation”. On the right side of the diagram, a right-pointing arrow leads from the large rectangular container to a rounded rectangular text box that reads, “Send out detected object data to U A R T port”. Below this rightmost box, smaller text reads, “M S S”.

Signal processing chain. Source:https://www.ti.com/tool/IWR1642BOOST?keyMatch=&tisearch=search-everything&usecase=hardware

Radio frequency (RF)/Analog Subsystem (BSS): This subsystem incorporates all RF and analog components of the radar system. It includes transmitting antennas with corresponding power amplifiers (PA), a synthesizer, receiving antennas with low-noise amplifiers (LNA), mixers and ADC. The Baseband Signal Subsystem (BSS) interfaces the radar with the external environment, handling the transmission and reception of signals, as well as initial signal processing.
Digital Signal Subsystem (DSS): The DSS executes algorithms to estimate the range and velocity of detected targets. This is achieved through the application of range FFT and Doppler FFT processing, followed by an angle-FFT to determine the AoA of the signals.
Master Subsystem (MSS): The MSS oversees the operation of the radar, incorporating a Cortex-R4F microprocessor and various peripherals, such as UART interfaces. These peripherals facilitate the transmission of processed data to an external personal computer for visualization.
Digital interface and built-in self-test (BIST): Positioned between the BSS and the DSS and/or MSS subsystems, this block serves as the bridge connecting the analog and/or RF components with the digital signal processing sections. It ensures seamless communication between the signal transmission and/or reception components and the data processing and display functionalities.

Methodologies: radar tests characterization

In order to study and evaluate the operational characteristics of the sensor used in the present work, the tests performed, the methodologies applied, and the results obtained will be illustrated in the following paragraphs.

Radar characterization and results

The data sessions were conducted in an outdoor scenario, utilizing a moving car as a target. This experimental setup was designed to simplify the initial testing phase, enabling a high frequency of trials with minimal resource expenditure. The choice of a vehicle as the target was strategic, as it provided a substantial radar cross-section (RCS), typically around 10 m² for a standard car (Kamel et al., 2017). This larger RCS significantly improved the probability of detection, facilitating the evaluation of system performance under realistic conditions. All sessions were held in the CIRA (Italian Aerospace Research Centre) facility, making use of outdoor spaces free from nearby metallic objects. The tests were classified into two types.

Along-track: The radar was positioned in front of the car, which moved towards and away from the sensor in a radial direction (Figure 8(a));
Cross-track: The car moved perpendicular to the radar's position (Figure 8(b)).

Figure 8

Two photographs showing a sensor-mounted device on a rural road and in a grassy field with a car in the background”.

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Two photographs are arranged side by side and labeled “(a)” and “(b)”. On the left, photograph “(a)” shows a straight paved rural road extending toward the horizon with grassy fields on both sides and a clear sky above”. In the foreground, the rear view of a sensor mount is visible, with handlebars and a mounted rectangular electronic setup consisting of circuit boards, wiring, and a small platform fixed above the front wheel”. Farther along the road, a dark-colored car is centered in the lane and facing away from the camera”. On the right, photograph “(b)” shows a grassy open field under a clear sky”. In the foreground, the rear view of the same sensor mount is visible with the same mounted electronic setup on the handlebars, including rectangular boards and exposed wires”. In the midground, a dark-colored car is parked or moving laterally across the field area near a dirt or grass path”. Low vegetation covers the ground, and distant landforms are faintly visible near the horizon”.

Outdoor data sessions. (a) Along track and (b) cross-track tests

In both cases, a Geographical Positioning System (GPS) sensor was employed as a reference measurement onboard to ascertain the velocity and position of the car during the tests, facilitating a comparison with radar data.

To conduct these tests, four different chirp configurations were evaluated. Table 2 provides details on the maximum range and detectable velocities, as well as range and velocity resolutions and the number of antennas used for transmission and reception. Table 2 lists the chirp parameters corresponding to the four used configurations.

Table 2

Chirp configurations used during the tests

Chirp Configuration	Max range [m]	Range Resolution [m]	Max Velocity [m/s]	Velocity Resolution [m/s]	TX Antennas	RX antennas
Conf. 1	50	0.244	8.4	1.05	2	4
Conf. 2	50	0.326	10.43	0.66	2	4
Conf. 3	50	0.206	7.54	0.95	2	4
Conf. 4	50	0.186	6.98	0.88	2	4
Conf. 1	50	0.244	8.4	1.05	2	4

ChirpConfiguration	Max range [m]	RangeResolution [m]	MaxVelocity [m/s]	VelocityResolution [m/s]	TXAntennas	RX antennas
Conf. 1	50	0.244	8.4	1.05	2	4
Conf. 2	50	0.326	10.43	0.66	2	4
Conf. 3	50	0.206	7.54	0.95	2	4
Conf. 4	50	0.186	6.98	0.88	2	4
Conf. 1	50	0.244	8.4	1.05	2	4

The frame duration was set at 100 milliseconds, ensuring that sampling was equal to the maximum allowed sampling frequency of 6.250 Mksps (see Table 3).

Table 3

Chirp configuration parameters

Parameters	Conf. 1	Conf. 2	Conf. 3	Conf. 4
Idle time (μs)	8	8	8	8
ADC start time (μs)	7	7	7	7
Ramp end time (μs)	48.98	38.67	56.54	61.76
Slope (MHz/μs)	15	15	15	15
ADC band (MHz)	614.4	388.8	729.6	806.4
ADC samples number	256	192	304	336
Sampling frequency (ksps)	6,250	6,250	6,250	6,250
Single chirp duration (μs)	40.96	25.92	48.64	53.76

Parameters	Conf. 1	Conf. 2	Conf. 3	Conf. 4
Idle time (μs)	8	8	8	8
ADC start time (μs)	7	7	7	7
Ramp end time (μs)	48.98	38.67	56.54	61.76
Slope (MHz/μs)	15	15	15	15
ADC band (MHz)	614.4	388.8	729.6	806.4
ADC samples number	256	192	304	336
Sampling frequency (ksps)	6,250	6,250	6,250	6,250
Single chirp duration (μs)	40.96	25.92	48.64	53.76

Experimental along-track tests

These tests aim to evaluate radar precision, velocity and range estimation by using a moving object along the radial direction toward and away from the sensor. The radar measures the radial component of the target velocity by exploiting a Doppler shift. Table 4 shows the tests performed and the corresponding chirp configurations used. Each test had a duration of about 30 seconds.

Table 4

Along track tests performed and corresponding chirp used

Test	Chirp Configuration	Target motion
1	Conf. 1	Target moving away
2	Conf. 2	Target moving away
3	Conf. 2	Target approaching
4	Conf. 3	Target moving away
5	Conf. 3	Target approaching
6	Conf. 4	Target moving away
7	Conf. 4	Target approaching

Test	Chirp Configuration	Target motion
1	Conf. 1	Target moving away
2	Conf. 2	Target moving away
3	Conf. 2	Target approaching
4	Conf. 3	Target moving away
5	Conf. 3	Target approaching
6	Conf. 4	Target moving away
7	Conf. 4	Target approaching

Due to the ground proximity, multiple tracks are observed corresponding to stationary objects and unwanted reflections from the ground, which were classified as clutter and removed. Figures 9 and 10 show the collected echoes and scatter plots (i.e. the position of the echoes in a XY-plane with origin at the antenna position) tests 1 and 7, respectively.

Figure 9

Two graphs with a range versus time line graph on the left and a y versus x scatter plot on the right.

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Two graphs show (a) on the left and (b) on the right. On the left, the vertical axis, labeled “Range in meters”, ranges from 0 to 40, in increments of 5 meters. The horizontal axis, labeled “time in seconds”, ranges from 0 to 8, in increments of 1 second. The plotted line begins at (0, 10), decreases to a low region near (0.451, 4.968), then rises continuously and ends at (7.721, 37.451). On the right, the vertical axis, labeled “y in meters”, ranges from 0 to 50, in increments of 5 meters. The horizontal axis, labeled “x in meters”, ranges from negative 25 to 25, in increments of 5 meters. A fan of dashed radial lines originates from the bottom center at coordinates (0, 0) and radiates upward to the boundary with regular intervals. The plot contains several scatter markers forming a narrow vertical cluster around x approximately near 0, with most markers lying between approximately 10 meters and 40 meters vertically. Note: All numerical data values are approximated.

Test 1: (a) Radar echoes of the detected object and (b) scatter plot

Figure 10

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Two graphs show (a) on the left and (b) on the right. On the left, the vertical axis, labeled “Range in meters”, ranges from 10 to 50, in increments of 5 meters. The horizontal axis, labeled “time in seconds”, ranges from 0 to 9, in increments of 1 second. The plotted line begins at approximately (0, 50), decreases steadily throughout the duration, and ends near approximately (8.721, 12.554). On the right, the vertical axis, labeled “y in meters”, ranges from 0 to 50, in increments of 5 meters. The horizontal axis, labeled “x in meters”, ranges from negative 25 to 25, in increments of 5 meters. A fan of dashed radial lines originates from the bottom center at coordinates (0, 0) and radiates upward to the boundary with regular intervals. The plot contains several scatter markers forming a narrow vertical cluster around x approximately near 0, with most markers lying between approximately 0 meters and 50 meters vertically. Note: All numerical data values are approximated.

Test 7: (a) Radar echoes and (b) scatter plot

Figure 11 presents the velocities and distances of the target during test 1, obtained from the radar. Furthermore, Figure 12 depicts a comparison of velocities and distances derived from the GPS sensor for Test 7.

Figure 11

Two line graphs show speed versus time rising, then sharply dropping, and distance versus time steadily increasing.

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Two line graphs are presented vertically. The horizontal axis, labeled “time in seconds”, ranges from 0 to 10 in increments of 1 second. At the top, the graph is titled “Speed in kilometers per hour, Test 1 dot dat dot c s v”. The vertical axis, labeled “Speed in kilometers per hour”, ranges from negative 40 to 40 in increments of 20 units. The line starts at (0, 10), peaks at (6.373, 27), falls at (7.684, negative 30), and ends at (9.161, negative 30). At the bottom, the graph is titled “Distance in meters Test 1 dot dat dot c s v”. The vertical axis, labeled “Distance in meters”, ranges from 0 to 60 in increments of 20 meters. The line starts at (0, 10.125), falls at (0.471, 4.5), rises at (4.959, 19.501), and ends at (9.139, 49.501). Note: All numerical data values are approximated.

Velocities and distances of the target obtained from the radar data, Test 1

Figure 12

Two line graphs compare Radar and G P S data, showing velocity over time and distance over time.

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Two graphs are presented side by side. The horizontal axis is labeled “Time in seconds”. The legend in the top-right lists “Radar“ and “G P S”. On the left, the graph is labeled “(a)”. The vertical axis, labeled “Velocity in kilometers per hour”, ranges from 0 to 25 in increments of 5. The horizontal axis ranges from 0 to 16 in increments of 2. The Radar curve starts at (0, 0), rises and peaks at (3.592, 20.243), remains nearly constant for a short duration, then decreases and ends at (9.796, 13.827). The G P S curve starts at (0.196, 0), rises and peaks at (4.441, 20.907), remains almost flat briefly, then drops at (11, 0), and ends at (12.147, 2.765). On the right, the graph is labeled “(b)”. The vertical axis, labeled “Distance in meters”, ranges from 0 to 50 in increments of 5. The horizontal axis ranges from 0 to 30 in increments of 5. The Radar curve starts at (0.214, 47.955), falls at (9.429, 22.955), and ends at (14.893, 3.636). The G P S curve starts at (0.214, 47.955), falls at (9.429, 22.955), and ends at (29.379, 4.545). Note: All numerical data values are approximated.

Radar vs GPS comparison, during Test 7

During Test 1, illustrated in Figure 11, when the target exceeded the maximum detectable velocity (8.4 m/s or 30.2 km/h, see Table 2), an instantaneous negative velocity value was observed, while the distance curve gradually increases. This is due to the target speed becoming greater than the maximum unambiguous speed, causing aliasing in the Doppler FFT and giving a folding of the measured velocity into a negative value.

Experimental cross-track tests

Figure 13 shows the experimental setup. Two distinct configurations were selected based on the maximum and minimum values of the maximum radial velocity observed among the previous tests. This selection allows assessing the radar's capability to detect objects moving at various maximum velocities. Table 5 presents the conducted tests along with the corresponding chirp configurations utilized. Each chirp configuration underwent two tests: in Tests 1 and 3, the car moved from the left to the right side relative to the radar position, whereas in Tests 2 and 4, the movement was in the opposite direction.

Figure 13

A satellite photograph showing a rural road segment with a marked red path and a labeled “Radar” location.

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The satellite photograph shows an overhead view of a rural landscape with grassy fields and intersecting paved roads. A narrow paved road runs diagonally from the lower left toward the upper center of the frame. A solid red line is drawn along this road, following its length and curvature. To the left of the road, a blue map pin icon appears with adjacent text reading “Radar”. The surrounding area consists of green and brown grass fields with visible texture variations. At the top of the frame, a wider paved road intersects the narrower road, forming a curved junction.

Radar position and car path during the cross-track tests

Table 5

Cross-track tests and corresponding chirp configurations

Test	Chirp Configuration	Target motion
1	Conf. 2	Left to Right
2	Conf. 2	Right to Left
3	Conf. 4	Left to Right
4	Conf. 4	Right to Left

Figure 14(a) shows the radar-target distance variation during the car motion, whereas Figure 14(b) depicts the scatter plots for Test 1, during experimental tests in cross-track modality. Figure 15 indicates radar-target distances during the Test 1 performed with chirp in Configuration 2.

Figure 14

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Two graphs show (a) on the left and (b) on the right. On the left, the vertical axis, labeled “Range in meters”, ranges from 20.5 to 25.5, in increments of 0.5 meters. The horizontal axis, labeled “time in seconds”, ranges from 6 to 10, in increments of 0.5 seconds. The plotted line begins at (6.213, 22.299), decreases to (7.511, 20.561), then rises upward and ends at (9.603, 25.104). On the right, the vertical axis, labeled “y in meters”, ranges from 0 to 30, in increments of 5 meters. The horizontal axis, labeled “x in meters”, ranges from negative 20 to 20, in increments of 5 meters. A fan of dashed radial lines originates from the bottom center at coordinates (0, 0) and radiates upward to the boundary with regular intervals. The plot contains several cross markers, mostly grouped where x ranges approximately from negative 10 to 10 meters, and D ranges from 20 to 25 meters. Note: All numerical data values are approximated.

(a) Curve of the detected “objects” and (b) scatter plot, during Test 1 in cross-track scenario

Figure 15

A scatter plot for D versus x, with cross markers mostly grouped around D 20 to 25 meters and dashed radial guide lines.

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The vertical axis is labeled “D in meters”, ranging from 0 to 30 in increments of 5 meters. The horizontal axis is labeled “x in meters”, ranging from negative 20 to 20 in increments of 5 meters. A fan of dashed radial lines originates from the bottom center at coordinates (0, 0) and radiates upward to the boundary with regular intervals. The plot contains several cross markers mostly grouped where x ranges approximately from negative 10 to 10 meters, and D ranges from 20 to 25 meters. Markers scatter at (negative 9.041, 22.275), (negative 7.441, 21.825), (negative 3.361, 21.225), (2, 20.711), (5.521, 21.525), (8.643, 22.804), and (12.562, 24.975). A few lower markers scatter at (negative 0.243, 2.025) and (2.643, 7.654). Note: All numerical data values are approximated.

Radar-target distance during test 1

Figure 16 shows the velocities and distances of the target, calculated from the radar, whereas Figure 17 shows the comparison of the distances between the radar and the on-board GPS sensor, during Test 1 and Test 2, respectively.

Figure 16

Two line graphs show speed versus time rising and distance versus time steadily increasing.

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Two line graphs are presented vertically. At the top, the graph is titled “Speed in kilometers per hour, Test 1”. The vertical axis, labeled “Speed in kilometers per hour”, ranges from negative 10 to 15 in increments of 5 units. The horizontal axis, labeled “time in seconds”, ranges from 6.3 to 9.5 in increments of 0.5 seconds. The line starts at (6.213, negative 10), rises at (7.5, 0), and ends at (9.184, 13.784). At the bottom, the graph is titled “Distance in meters, Test 1”. The vertical axis, labeled “Distance in meters”, ranges from 20 to 30 in increments of 2 meters. The horizontal axis, labeled “time in seconds”, ranges from 6.3 to 9.5 in increments of 0.5 seconds. The line starts at (6.194, 22.396), falls at (7.5, 20.599), rises at (9, 22.719), and ends at (9.582, 25). Note: All numerical data values are approximated.

Velocities and distances of the target obtained from the radar data, Test 1 in cross-track

Figure 17

Two line graphs compare Radar and G P S data, showing range over time.

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Two graphs are presented side by side. The vertical axis is labeled “Range in meters”. The horizontal axis is labeled “time in seconds”. The legend at the top lists “Radar” and “G P S”. On the left, the graph is labeled “(a)”. The vertical axis ranges from 20 to 29 in increments of 1. The horizontal axis ranges from 7 to 13 in increments of 1. The Radar curve starts at (8.292, 10.111), falls at (10.197, 2.294), then rises, and ends at (11.271, 6.917). The G P S curve starts at (7, 28.225), falls at (9.956, 3.725), then rises sharply, and ends at (12.015, 21.284). On the right, the graph is labeled “(b)”. The vertical axis ranges from 23 to 28 in increments of 1. The horizontal axis ranges from 19 to 23.5 seconds in increments of 0.5. The Radar curve starts at (19.596, 23.991), falls at (20.869, 22.281), then rises, and ends at (23.005, 26.822). The G P S curve starts at (19.017, 26.654), falls at (21.004, 22.364), then rises sharply, and ends at (22.987, 27.495). Note: All numerical data values are approximated.

Distance comparison between radar and GPS, for (a) Test 1 and (b) Test 2

In post-processing, it was observed that the moving target was detected in all tests within an angular sector of 60-degree width (±30°), rather than the entire field of view of the sensor, which is 120° (±60°), due to the specular reflection of the metallic surface of the car, which, when viewed from an offset angle greater than 30°, backscattered extremely low energy toward the sensor. Moreover, within the time frame when the target was inside the radar's field of view, the distance values were coherent with the radial velocity values. Specifically, when the object was in the approach phase, the velocity values were negative (as described in the previous section). When the target was directly in front of the radar, the detected radial velocity was zero (Doppler shift equal to 0).

Error analysis

The data analysis and error evaluation were conducted in the MATLAB^® environment by comparing the tracking curves obtained from radar and GPS. Error analysis was specifically focused on along-track tests, as these provided a larger dataset for comparison between radar and GPS sensors. To compare the range curves, a fitting process was implemented. Specifically, two interpolating polynomial curves of appropriate degree were defined to provide a better approximation of the distance measurements obtained from the radar. To achieve a perfect match between radar and GPS data, it was necessary to synchronize the measurements before the interpolation process. After the synchronization phase, two polynomial curves were generated: the first curve was derived from radar measurements, and the second curve was obtained from GPS data, both at the same acquisition times. Figure 18, Figure 19 and Figure 20 depict the comparison between the polynomial curves for radar and GPS data for Test 1, Test 4 and Test 7, respectively, using a second-degree polynomial for Tests 1 and 4 and a fourth-degree polynomial for Test 7.

Figure 18

A line graph of distance versus time comparing radar and G P S original data and polynomial fitted lines.

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The vertical axis, labeled “Distance in meters”, ranges from 0 to 160 in increments of 20 meters. The horizontal axis, labeled “Time in seconds”, ranges from 15 to 21 in increments of 1 second. The legend in the top-right lists “Radar Polynomial”, “G P S Polynomial”, “Radar Original”, and “G P S Original”. The line labeled “Radar Polynomial”, begins at (15, 8.473), rises to (18, 25.418), and ends at (21, 50.183). The “Radar Original”, data markers closely follow the same increasing pattern as the “Radar Polynomial” line. The line labeled “G P S Polynomial”, begins at (15, 6.517), rises to (19, 33.238), and ends at (21, 48.228). The “G P S Original”, data markers closely follow the same increasing pattern as the “G P S Polynomial” line. Note: All numerical data values are approximated.

Comparison between radar-GPS polynomial curves, for Test 1

Figure 19

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The vertical axis, labeled “Distance in meters”, ranges from 0 to 100 in increments of 10 meters. The horizontal axis, labeled “Time in seconds”, ranges from 5 to 14 in increments of 1 second. The legend in the top-right lists “Radar Polynomial”, “G P S Polynomial”, “Radar Original”, and “G P S Original”. The line labeled “Radar Polynomial”, begins at (5, 12.768), rises to (9, 30.534), and ends at (14, 82.697). The “Radar Original”, data markers closely follow the same increasing pattern as the “Radar Polynomial” line, with some data markers deviating slightly. The line labeled “G P S Polynomial”, begins at (5, 13.232), rises to (9, 31.043), and ends at (14, 82.000). The “G P S Original”, data markers closely follow the same increasing pattern as the “G P S Polynomial” line, with some data markers deviating slightly. Note: All numerical data values are approximated.

Comparison between radar-GPS polynomial curves, for Test 4

Figure 20

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The vertical axis, labeled “Distance in meters”, ranges from 0 to 45 in increments of 5 meters. The horizontal axis, labeled “Time in seconds”, ranges from 5 to 11 in increments of 1 second. The legend in the top-right lists “Radar Polynomial”, “G P S Polynomial”, “Radar Original”, and “G P S Original”. The line labeled “Radar Polynomial”, begins at (5, 45), decreases to (8, 30.659), and ends at (11, 13.475). The “Radar Original”, data markers closely follow the same decreasing pattern as the “Radar Polynomial” line. The line labeled “G P S Polynomial”, begins at (5, 44.753), decreases to (8, 30.536), and ends at (11, 13.723). The “G P S Original”, data markers closely follow the same decreasing pattern as the “G P S Polynomial” line. Note: All numerical data values are approximated.

Comparison between radar-GPS polynomial curves for test 7

From the difference between the radar and GPS fitting curves, the absolute error and percentage relative error were calculated. The obtained results indicated that the radar can detect a target along a radial direction, with accuracy increasing as the distance between the radar and the target increases. These findings are consistent with the results obtained under static conditions (Menichino et al., 2023a, b). During the entire data campaign, the percentage relative error was generally less than 1%, with a maximum value of 3.7% observed in only one test (Test 1). The maximum absolute error estimated was 2 meters, while the mean absolute error was approximately 20 cm. The minimum absolute error recorded was 7 cm, which was estimated during test 3. The mean values of percentage error (Ε) and absolute error (|Ε|) for each test performed are shown in Figure 21 (see Figure 22 and Table 6).

Figure 21

Two line graphs show percentage error and absolute error versus distance for radar G P S comparison.

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On the left, the line graph is labeled “(a)”. The vertical axis, labeled “Error percentage”, ranges from 0 to 30 percent in increments of 5 percent. The horizontal axis, labeled “Distance in meters”, ranges from 5 to 50 meters in increments of 5 meters. A single solid line begins at (8, 29.497), decreases steeply to (18, 1.812), rises gently to (25, 3.624), decreases again to (40, 2.012), and ends at (49, 3.636). On the right, the line graph is labeled “(b)”. The vertical axis, labeled “Error in meters”, ranges from 0 to 2.5 meters in increments of 0.5 meters. The horizontal axis, labeled “Distance in meters”, ranges from 5 to 50 meters in increments of 5 meters. A single solid line begins at (9, 2.037), decreases sharply to (19, 0.149), increases to (30, 0.708), decreases again to (40, 0.149), and ends at (49, 1.163). Note: All numerical data values are approximated.

The trend of (a) percentage error and (b) absolute error, calculated from the difference between radar and GPS sensors during Test 1 in the along-track modality

Figure 22

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On the left, the line graph is labeled “(a)”. The vertical axis, labeled “Error percentage”, ranges from 0 to 3.5 percent in increments of 0.5 percent. The horizontal axis, labeled “Distance in meters”, ranges from 10 to 90 meters in increments of 10 meters. A single thin solid line forms a smooth curve. The line begins at (12, 2.823), rises slightly and peaks at (15, 3.112), then decreases steadily to (60, 012). After reaching the minimum, the line rises gently and ends at (85, 0.553). On the right, the line graph is labeled “(b)”. The vertical axis, labeled “Error in meters”, ranges from 0 to 0.6 meters in increments of 0.1 meters. The horizontal axis, labeled “Distance in meters”, ranges from 10 to 90 meters in increments of 10 meters. A single thin solid line forms a smooth curve. The line begins at (12, 0.381), rises to a peak at (22, 0.561), then decreases steadily to (60, 023). After the minimum, the line rises again and ends at (85, 0.451). Note: All numerical data values are approximated.

The trend of (a) percentage error and (b) absolute error, calculated from the difference between radar and GPS sensors during Test 4 in the along-track modality

Table 6

Error analysis for along-track tests

Test	E [%]	\|Ε \| [m]
1	3.7	1.15
2	0.5	0.17
3	0.33	0.07
4	0.82	0.32
5	0.57	0.18
6	0.97	0.27
7	0.66	0.19

In the comparison among velocities, a quantization error is present due to the difference between the analog sinusoidal signal and the quantized digital value, which is typical of the A/D conversion process. For this reason, the velocity curves show a “step” pattern when compared to the “smooth” curves from the GPS data. This discrepancy results in errors that display a “saw-tooth” trend. For representative purposes, Figure 23 shows the comparison between radar and GPS velocities during Test 5 in the along-track modality, highlighting the sampled radar velocity values.

Figure 23

A line graph compares radar and G P S velocity versus time during test 5.

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The vertical axis is labeled “meters per second” and ranges from 0 to 8 meters per second in increments of 1 meter per second. The horizontal axis is labeled “Time in seconds” and ranges from 0 to 35 seconds in increments of 5 seconds. A legend appears in the upper right with two entries labeled “Radar” and “G P S”. A solid line with circular markers labeled “Radar” begins at (4, 0.408), rises steadily through (6, 5.711), and reaches a peak at (8, 6.527). The line remains nearly flat between (8, 6.554) and (10, 6.612), then decreases sharply through (11, 5.656), (12, 3.823), and (13, 0.453). From (13, 0.432) to (32, 0.381), the line remains flat at zero. A solid line with plus-shaped markers labeled “G P S” begins at (1, 0.499), remains at zero until (4, 0.476), then rises steeply through (6, 4.612) and (8, 6.222), reaching a peak at (9, 6.902). The line then decreases through (10, 6.467), (11, 4.798), and (12, 2.825), reaching (13,0.472). A small isolated rise appears at (14, 1.065), followed by a return to (15, 0.453). Note: All numerical data values are approximated.

Comparison between radar-GPS velocities, during Test 5

Flight tests

Finally, after the sensor's characterization, to simulate autonomous operations in a UAM scenario, the radar was mounted onboard the UAV for flight tests. Figure 24 illustrates the structure designed and developed for this purpose, with the radar sensor in place. The flight tests were made possible through collaboration with ISARail S.P.A, a certified drone operator responsible for conducting the flight operations. The selected site for these tests is shown in Figure 25. All necessary procedural steps for the flight operations, including obtaining clearance for operations within the Grazzanise CTR (Southern Italy), were coordinated by the airfield manager. ISARail contributed to the process by conducting a detailed risk assessment and implementing mitigation measures to ensure the safety and effectiveness of the tests.

Figure 24

Two photographs of cylindrical modules with a schematic and a physical radar module on a tripod stand.

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On the left, labeled “a”, a cylindrical schematic has a rectangular section labeled “Radar”. The cylinder has multiple circular holes arranged in rows along its body. The cylinder stands on three short legs. The schematic is gray with shading showing curvature. On the right, labeled “b”, a physical cylindrical radar module stands on three extended legs forming a tripod. The cylinder has multiple circular holes along the body. A rectangular sensor board is mounted on the front with wires connected to it and to the lower part of the cylinder. The top of the cylinder is flat and circular. The legs extend outward and have small circular feet. The module is placed on a tiled floor in front of closed cabinet doors.

(a) CAD project of the structure and (b) printed structure with the sensor mounted

Figure 25

A satellite photograph showing a grassy airfield area with buildings, a runway strip, and labeled coordinates.

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The satellite photograph shows an overhead view of a grassy airfield landscape with roads, buildings, and open land. Near the upper center, a long rectangular structure with a red and white checkered pattern is positioned horizontally. To the left of this structure, additional rectangular buildings with light colored roofs are visible, connected by paved and unpaved paths. A narrow road runs horizontally across the upper portion of the scene, curving slightly as it passes the buildings. In the central area, a wide grassy strip extends diagonally across the frame, resembling a runway or open landing area. Near the center of the image, a blue map marker icon appears with adjacent text reading “Campo Volo Rains Club Caiazzo”. On the right side of the image, white text reads “E 14 degrees 20 minutes 19.68 seconds”. The surrounding areas consist of green and brown grassy fields with visible texture variations, tree clusters along the right edge.

Flight site

Figure 26 shows the system mounted onboard the UAV during the (a) calibration phase and during the (b) subsequent flight test. The calibration phase was crucial for ensuring that the radar system was properly aligned and configured to collect accurate data, while the flight test demonstrated the system's real-time performance in a dynamic environment. A single-board computer (SBC) Raspberry Pi 4 was used to receive data from the sensor to send them to the ground control station (GCS).

Figure 26

Two photographs show a multi rotor drone in an open field with distant hills in the background.

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The drone has a central circular body with four curved arms extending outward, each ending in a short cylindrical motor housing with a horizontal rotor blade on top. A cylindrical payload unit is mounted vertically above the center of the drone on a slim support. Several thin rods and cables connect the central body to the upper cylindrical unit. In the left photograph labeled “(a)”, the drone rests on low landing supports on short grass, viewed from a slightly elevated front angle, with the horizon line and tree line in the distance. In the right photograph labeled “(b)”, the same drone is captured in mid air above the grassy field, viewed from the side against a backdrop of sloping hills and open sky, with the rotor blades appearing blurred in motion.

(a) The whole system before the simulation and (b) drone during take-off phase

This dual-phase approach ensured the sensor's readiness for reliable obstacle detection and navigation during autonomous UAV operations.

During the flight tests, telemetry data were received in real-time to monitor and verify the functionality of the system. The data link used to receive telemetry from the onboard obstacle detection system consisted of a pair of transmitter and receiver units (Radiomodem SFMN) operating in the very high frequency/ultra high frequency band at 433 MHz. The transmitter, installed on the drone, was connected to a Raspberry Pi 4, while the receiver was linked to a laptop functioning as the GCS. This setup allowed for real-time data visualization and analysis during flight operations.

Based on the distance definitions reported in Figure 27, Figure 28 shows the points detected by the radar during the flight.

Figure 27

A rectangular hangar, showing distances d subscript H 1, d subscript H 2, d subscript minimum, and points H 1, H 2.

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The schematic diagram shows a rectangular block labeled with uppercase text “HANGAR” positioned horizontally. Two points are marked on the bottom corners of the hangar. The left corner is labeled “H 1” and the right corner is labeled “H 2”. A drone is depicted below the hangar. Three distance indicators are shown as straight lines connecting the drone to the hangar. A diagonal line from the drone to point “H 1” is labeled “d subscript H 1”. A diagonal line from the drone to point “H 2” is labeled “d subscript H 2”. A vertical line from the drone to the bottom edge of the hangar is labeled “d subscript minimum”.

Drone distance from hangar definitions

Figure 28

A line and scatter graph of distance versus trajectory point with radar detections and three distance curves.

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The line and scatter graph is titled “Radar versus Hangar Distance”. The vertical axis is labeled “Distance in meters” and ranges from 0 to 70, in increments of 10 meters. The horizontal axis is labeled “Trajectory point” and ranges from 100 to 400, in increments of 50. The legend in the upper right lists “Radar Detections”, “d subscript H 1”, “d subscript H 2”, and “d subscript minimum”. The line, labeled “d subscript H 1”, begins at (78.947, 65.711), falls at (383.553, 25.049), and ends at (435.526, 32.225). The line, labeled “d subscript H 2”, begins at (78.947, 46.324), falls at (355.263, 14.412), and ends at (435.526, 24.191). The line, labeled “d subscript minimum”, begins at (78.947, 44.951), falls at (381.579, 8.407), and ends at (435.526, 20.761). The circular markers labeled “Radar Detections” appear scattered across the plot, forming a dense horizontal band at a distance of about 5 meters. A broader scatter is aligned around the “d subscript H 1” and “d subscript minimum” curves across the trajectory points, and some markers are scattered between the “d subscript H 1” and “d subscript minimum” curves across the trajectory points. Note: All numerical data values are approximated.

Distance from hangar detected from radar vs real distance

Figure 29 shows details of the minimum distance detected from the radar versus the distance of the hangar calculated from the GPS position of the trajectory.

Figure 29

A scatter and line graph of distance versus trajectory point showing radar detection points and a d subscript minimum curve.

View large Download slide

The scatter and line graph is titled “Radar versus Hangar Distance”. The vertical axis is labeled “Distance in meters” and ranges from 5 to 50, in increments of 5 meters. The horizontal axis is labeled “Trajectory point” and ranges from 50 to 450, in increments of 50. The legend in the upper right lists “Radar Detection” and “d subscript minimum”. The line, labeled “d subscript minimum”, begins at (77.505, 45), falls at (181.238, 14.998), rises slightly at (235.462, 19.589), falls again at (284.185, 12.071), rises at (332.122, 18.354), dips at (383.202, 7.581), then rises again and ends at (436.641, 20.935). The scatter labeled “Radar Detection” closely follows the line labeled “d subscript minimum” across the trajectory points, with small deviations around the line. Note: All numerical data values are approximated.

Minimum distance from hangar detected from radar vs real distance

It is possible to observe that the distances measured by the radar follow well the minimum (⁠ $d_{\min}$ ⁠) and maximum (⁠ $d_{H 1}$ ⁠) distances from the hangar. There is a background noise with detection at about 5 meters due to the reflection of the ground and dependent on the drone's low altitude and flight pitch.

The result is that the radar can detect the hangar's wall at a distance starting from about 45m. In particular, the radar can correctly detect the minimum distance from the hangar's wall.

The following figures show the error between the distance measurement from the hangar detected by the radar and that calculated from the GPS coordinates of the trajectory.

In these figures, it is evident that the error is of the order of GPS measurement error. Therefore, the error is mainly attributable to the GPS measurement of the drone's position rather than to the radar, which in previous tests has shown to have accuracy in the order of cm (see Figure 30 and 31).

Figure 30

A scatter graph of distance error versus trajectory point showing radar error values distributed above and below zero.

View large Download slide

The scatter graph is titled “Radar versus Hangar Distance Error”. The vertical axis is labeled “Distance Error in meters” and ranges from negative 2 to 2, in increments of 0.5 meters. The horizontal axis is labeled “Trajectory point” and ranges from 100 to 400, in increments of 50. The legend in the upper right lists “Radar Error”. Circular markers labeled “Radar Error” are distributed above and below zero across the plot. Clusters appear at (129.395, negative 1.125), (190.778, negative 0.806), (319.597, negative 0.806), and (349.856, negative 1.311). Most points lie between negative 0.5 and 0.5, with some points reaching about 1 meter, across the full trajectory point range. Note: All numerical data values are approximated.

Detected distance error

Figure 31

A histogram titled “Radar Detection Error” showing frequency versus trajectory point values.

View large Download slide

The vertical axis is labeled “Frequency”, ranging from 0 to 45 in increments of 5. The horizontal axis is labeled “Trajectory point”, ranging from negative 2 to 1 in increments of 0.5. The bars start at low frequency, rise to a peak in the negative trajectory point range, then decrease, and rise again slightly before tapering off toward the end. The bars begin at (negative 1.48, 10.532), peak at (negative 1.134, 42.447), fall at (negative 0.527, 11.451), rise again and show a peak at (negative 0.267, 22.661), fall to (0.653, 0.319), then rise again at (0.751, 3.511). The histogram follows an overall right-skewed distribution with multiple peaks and troughs. Note: All numerical data values are approximated.

Detected distance error distribution

Figure 32 shows the detection of the radar at a point of the trajectory where the ground reflection is missing.

Figure 32

A line graph shows flight trajectory and radar detections near a hangar wall with field of view and heading.

View large Download slide

The line graph titled “Detection versus Trajectory”. The vertical axis, labeled “y North in meters”, ranges from negative 10 to 100 meters in increments of 10 meters. The horizontal axis, labeled “x East in meters”, ranges from negative 70 to 10 meters in decrements of 10 meters. A legend in the upper right lists “Trajectory”, “Hangar Up”, “Hangar Front”, “Radar R equals negative 27.5”, “Radar”, “Heading”, and “Radar F o V”. In the center and lower right, a continuous thin line labeled “Trajectory” begins at (negative 38, 35.543), curves upward and leftward through (negative 26, 37.941) and (negative 27, 58.452), loops around the central region at (negative 25, 51.523), and continues with small oscillations before ending at (0, 5.456). The curve is smooth and continuous with multiple bends. At the top center, two thin straight line segments labeled “Hangar Up” and “Hangar Front” form a rectangular outline. The upper horizontal segment spans from (negative 50, 80.1) to (negative 15, 85.082). A lower horizontal segment spans from (negative 50, 67.5) to (negative 15, 67.243). Vertical connecting segments extend from (negative 50, 67.244) to (negative 50, 80.1) and from (negative 15, 67.221) to (negative 15, 85.052). Near the upper middle area, two star-shaped markers labeled “Radar R equals negative 27.5” are located at (negative 45, 66.174) and (negative 24, 65.104). Two circular markers labeled “Radar” are positioned close to these star markers at similar coordinates. From the central region at (negative 25, 57.112), a straight line labeled “Heading” extends upward and leftward to (negative 36, 80.081). From the same central point, two straight lines labeled “Radar F o V” extend outward, one to (negative 66, 58.18) and the other to (negative 10, 77.045), forming a wide angular shape. Note: All numerical data values are approximated.

Flight result – radar detection near hangar's wall

Findings

The tests were conducted using four different chirp configurations, with an onboard GPS sensor used as the reference measurement. The performed tests provided good results in terms of precision and accuracy of the sensor during dynamic motion, based on the four different chirp configurations used, set up by the mmWave Demo Visualizer software and exploiting the algorithms already implemented on the radar sensor. In the post-processing phase, the radar measurements were compared to GPS data in terms of position and velocity. The comparison of the tracks demonstrated a good match between the two sensors and confirmed their coherence with the target's trajectories. The mean absolute error evaluated was approximately 20 cm, and the percentage relative error was generally less than 1% on average throughout the entire data campaign.

After evaluating the sensor's behavior across different chirp configurations in several ground-based tests, flight tests were conducted to simulate operations within an UAM scenario. A dedicated structure was designed to house the radar, which was mounted on board the UAV. The flight tests were carried out at an airfield, using the hangar on-site as a simulated obstacle along the UAV's trajectory. The results demonstrated that the radar was able to detect the hangar's wall from a distance of approximately 45 meters. Notably, the radar consistently and accurately detected the minimum distance between the UAV and the hangar's wall, validating its performance in obstacle detection.

Conclusions

This paper evaluated the accuracy and precision of the TIIWR1642 radar sensor for UAM applications. The primary objective was to characterize the behavior of the sensor under dynamic conditions by performing two test campaigns: along-track and cross-track tests, relative to a fixed radar position. A moving car was chosen as the target due to its geometric shape and composition.

The application of the considered sensor appears to be suitable for obstacle detection systems in UAM operations.

Future work will include additional tests to improve radar performance using different chirp configurations. The primary objectives will be.

To develop configurations that increase the maximum detectable distance in both static and dynamic conditions;
To achieve maximum radial velocities on the order of 90 km/h by using the radar in Single-Input Multiple-Output mode with a single transmitting antenna and
To evaluate the detection of multiple targets (both fixed and mobile), with a focus on range and velocity resolution.

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Performance evaluation of a radar-on-chip system for obstacle detection strategies for urban air mobility (UAM) applications

Acronyms

Introduction

Chirp parameters, range and velocity measurements

Hardware description

Sensor: radar-on-chip IWR 1642

Evaluation board: IWR1642-BOOST

Methodologies: radar tests characterization

Radar characterization and results

Experimental along-track tests

Experimental cross-track tests

Error analysis

Flight tests

Findings

Conclusions

References

Further reading

Email Alerts

Citing articles via

Performance evaluation of a radar-on-chip system for obstacle detection strategies for urban air mobility (UAM) applications Open Access

Acronyms

Introduction

Chirp parameters, range and velocity measurements

Hardware description

Sensor: radar-on-chip IWR 1642

Evaluation board: IWR1642-BOOST

Methodologies: radar tests characterization

Radar characterization and results

Experimental along-track tests

Experimental cross-track tests

Error analysis

Flight tests

Findings

Conclusions

References

Further reading

Email Alerts

Suggested Reading

Related Chapters

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Performance evaluation of a radar-on-chip system for obstacle detection strategies for urban air mobility (UAM) applications