This paper presents a method to improve inverse problem resolution. This method focuses on the measurement set and particularly on sensor position. Based on experiment, it aims at finding sensor position criteria to insure the least bad inverse problem solving.
The studied device is a magnetized steel sheet measured by four sensors. Three optimization techniques are compared: condition number, solid angle and signature optimization.
An efficient criterion to compare the inverse problem resolution quality is presented. The comparison of optimization techniques shows that only signature optimization gives accurate results.
A relative simple case is studied in this paper: only four sensors are used to measure a steel sheet. Moreover magnetostatic low‐field case is supposed. Nevertheless techniques presented could be applied to more complex studies. Condition number and solid angle optimizations techniques should be tested with more sensors to confirm or infirm their inefficiency.
This paper presents the first step of a larger study concerning ships for naval application. The aim is to predict magnetic anomaly created by ship to compensate it. This anomaly could be computed through the resolution of an inverse problem based on internal measurements. The signature optimization technique could be used to find the optimal sensor location onboard.
Traditional regularization techniques are focusing on adding mathematical or physical information to the system in order to improve it. This paper provides another approach to improve inverse problem resolution through measurement set. It shows that sensor position optimization should be efficient.
