Space-time adaptive processing in bistatic passive radar exploiting group sparsity

In this paper, we propose a novel method to estimate the clutter covariance matrix (CCM) and perform space-time adaptive processing (STAP) for effective clutter suppression based on a small number of secondary data samples. By exploiting the group sparsity of the angle-Doppler domain clutter profile shared by nearby range cells in a bistatic passive radar platform, we first apply the complex multi-task Bayesian compressive sensing (CMT-BCS) algorithm to reconstruct the sparse clutter profile based on the secondary data samples. The clutter profile in the range cell under test is then obtained within the common clutter support over all secondary data samples to ensure the exclusion of target signals in the estimated CCM. Compared to the conventional STAP method, the number of required secondary samples is significantly reduced due to the group sparsity of the clutter profile. The effectiveness of the proposed algorithm is verified using simulation results.