In response to the challenges of maintaining the configuration and navigation stability of low-cost unmanned aerial vehicle swarms under intermittent global navigation satellite system (GNSS) signal conditions, this study aims to introduce a fast network topology generation algorithm and a hybrid covariance filter.
First, using spatially stable structures and the principle of three-sphere intersection, connectivity between nodes is rapidly generated, ultimately forming a network topology with tetrahedrons as the basic unit. This ensures the stability of the configuration. Subsequently, a problem arises from the improper distribution of internal confidence within the system when some nodes are connected to GNSS, whereas others rely solely on ranging. In response, a hybrid covariance method with independent relative and absolute covariance matrices is proposed, which can improve the overall navigation precision of the swarm.
Simulation results show that the approach achieves rapid convergence of relative positioning errors to less than 0.5 m for internode distances over 100 m. When one, two and three anchor nodes are accessed, the positioning accuracy of the proposed method is improved by 31.59 %, 64.53 % and 64.48 %, respectively, compared with the existing methods.
The proposed method can stabilize configurations and improve overall positioning accuracy, providing support for addressing distributed navigation issues in intermittent GNSS signals.
