A Multi-Angle Observation and Imaging Method for UAV Swarm SAR Based on Consensus Constraints

Unmanned Aerial Vehicle (UAV) swarms are characterized by flexible maneuverability and diverse configurations. By distributing UAV swarm Synthetic Aperture Radar (SAR) systems in different directions around the target scene, it is possible to simultaneously observe and acquire target information from multiple angles. Compared to multi-static SAR and circular SAR, each UAV-borne SAR system only collects partial aperture data. By leveraging the coordination among the UAV swarms, a large aperture can be synthesized in a short period of time. However, a critical issue that needs to be addressed is how to integrate short-aperture data, leverage target characteristics to mitigate the impact of sidelobes and noise, reduce the effects of layover and shadowing in SAR images, and achieve high-resolution SAR imaging. This paper proposes a distributed optimization method based on consensus constraints. First, the Minimization of the Concave Penalty (MCP) regularization term and the relative total variation (RTV) regularization term are used to introduce sparsity and structural priors, respectively, to achieve high-resolution imaging of single-platform SAR. Then, on this basis, consensus constraints are imposed on the local images formed by each UAV-borne SAR to preserve the spatial invariance characteristics of the target scene. Finally, under a distributed optimization framework, multi-angle SAR imaging results are optimized from multiple local images. Simulation and actual data processing results have verified the effectiveness and superiority of the proposed method, especially when the number of measurement angles is limited.