Sparsity-based STAP algorithm with multiple measurement vectors via sparse Bayesian learning strategy for airborne radar

作者：Duan, Keqing; Wang, Zetao; Xie, Wenchong; Chen, Hui; Wang, Yongliang^*

来源：IET Signal Processing, 2017, 11(5): 544-553.

DOI：10.1049/iet-spr.2016.0183

• airborne radar
• radar signal processing
• learning (artificial intelligence)
• radar computing
• Bayes methods
• radar clutter
• computational complexity
• computational complexity
• sparse Bayesian learning
• MMV
• clutter suppression algorithm
• space-time adaptive processing algorithm
• parameter-dependent sparse recovery
• airborne radar
• Bayesian learning strategy
• multiple measurement vectors
• sparsity-based STAP algorithm

To improve the performance of the recently developed parameter-dependent sparse recovery (SR) space-time adaptive processing (STAP) algorithms in real-world applications, the authors propose a novel clutter suppression algorithm with multiple measurement vectors (MMVs) using sparse Bayesian learning (SBL) strategy. First, the necessary and sufficient condition for uniqueness of sparse solutions to the SR STAP with MMV is derived. Then the SBL STAP algorithm in MMV case is introduced, and the process for hyperparameters estimation via expectation maximisation is given. Finally, a computational complexity comparison with the existing algorithms and an analysis of the proposed algorithm are conducted. Results with both simulated and the Mountain-Top data demonstrate the fast convergence and good performance of the proposed algorithm.

• 出版日期2017-7
• 单位中国人民解放军国防科学技术大学