A Novel Clutter Suppression Algorithm for Airborne Radar via Subspace Projection

Strong ground clutter submerges the moving targets and hinders the radar image analysis. An effective technique called space-time adaptive processing (STAP) is used to suppress clutter in airborne radar systems but has the disadvantage of large training sample requirements. Sparse recovery STAP (SR-STAP) has the benefit of improving the algorithm performance when the training sample size is small. However, the computational burden and storage space restrict the practical application of SR-STAP. To address this issue, a fast subspace-based SR-STAP algorithm is proposed in this article. Using the Bayesian framework, the proposed algorithm selects the most matched atoms to span a clutter subspace by minimizing the likelihood function. In comparison with the existing SR-STAP algorithm, the proposed algorithm avoids the time-consuming sparse reconstruction process. Moreover, the estimated subspace can be applied to gain-phase error calibration. The proposed algorithm is experimentally demonstrated to have superior performances in terms of clutter suppression, target detection, and computational complexity.