2010/08/25 by Ke Sun, Hao Zhang, Sun, Ke +7
Computer Science · Engineering · Mathematics · #Advanced SAR Imaging Techniques #Clutter #Computer science #Environmental science #FOS: Computer and information sciences #Geology #Information Theory (cs.IT) #Microwave Imaging and Scattering Analysis #Radar #Radar Systems and Signal Processing #Remote sensing #Telecommunications #cs.IT #math.IT
paper · pdf · doi:10.48550/arxiv.1008.4185
28 pages, 11 figures, Submitted to the IEEE Transactions on Aerospace and Electronic Systems in April,2010
arxiv created 2010/08/25 · openalex publication_date 2010/08/25 · arxiv updated 2010/08/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Space-time adaptive processing (STAP) is an effective tool for detecting a moving target in spaceborne or airborne radar systems. Statistical-based STAP methods generally need sufficient statistically independent and identically distributed (IID) training data to estimate the clutter characteristics. However, most actual clutter scenarios appear only locally stationary and lack sufficient IID training data. In this paper, by exploiting the intrinsic sparsity of the clutter distribution in the angle-Doppler domain, a new STAP algorithm called SR-STAP is proposed, which uses the technique of sparse recovery to estimate the clutter space-time spectrum. Joint sparse recovery with several training samples is also used to improve the estimation performance. Finally, an effective clutter covariance matrix (CCM) estimate and the corresponding STAP filter are designed based on the estimated clutter spectrum. Both the Mountaintop data and simulated experiments have illustrated the fast convergence rate of this approach. Moreover, SR-STAP is less dependent on prior knowledge, so it is more robust to the mismatch in the prior knowledge than knowledge-based STAP methods. Due to these advantages, SR-STAP has great potential for application in actual clutter scenarios.