Abstract: Robust PCA has found important applications in many areas, such as video surveillance, face recognition, latent semantic indexing and so on. In this paper, we study its application in ground moving target indication (GMTI) in wide-area surveillance radar system. MTI is the key task in wide-area surveillance radar system. Due to its great importance in future reconnaissance systems, it attracts great interest from scientists. In (Yan et al. in IEEE Geosci. Remote Sens. Lett., 10:617–621, 2013), the authors first introduced robust PCA to model the GMTI problem, and demonstrate promising simulation results to verify the advantages over other models. However, the robust PCA model can not fully describe the problem. As pointed out in (Yan et al. in IEEE Geosci. Remote Sens. Lett., 10:617–621, 2013), due to the special structure of the sparse matrix (which includes the moving target information), there will be difficulties for the exact extraction of moving targets. This motivates our work in this paper where we will detail the GMTI problem, explore the mathematical properties and discuss how to set up better models to solve the problem. We propose two models, the structured RPCA model and the row-modulus RPCA model, both of which will better fit the problem and take more use of the special structure of the sparse matrix. Simulation results confirm the improvement of the proposed models over the one in (Yan et al. in IEEE Geosci. Remote Sens. Lett., 10:617–621, 2013).

Key words: Ground moving target indication , Alternating direction method , Wide-area surveillance radar system , Joint sparsity , Matrix recovery