Clutter Suppression via Joint Sparse Recovery for Airborne Passive Radar

Clutter suppression is a critical challenge in airborne passive radar systems, where platform motion induces significant Doppler spreading that renders conventional suppression algorithms ineffective. To address this limitation, this paper proposes a clutter suppression algorithm based on joint sparse recovery. Similar to the CLEAN iterative procedure, the proposed algorithm utilizes the least-squares method to estimate the clutter component in each iteration. During each iteration, it leverages both the joint sparse characteristics of multi-channel clutter and the cluster structure within channels to select multiple clutter components. This design enables efficient joint clutter estimation across multiple channels. Furthermore, the proposed algorithm utilizes the estimated direct-path signal to derive correction factors, ensuring stable performance even in the presence of channel amplitude and phase errors. Finally, a series of simulation experiments are conducted to evaluate the performance of the proposed algorithm.