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

• Published in: IET signal processing Vol. 11; no. 5; pp. 544 - 553
• Main Authors: Duan, Keqing, Wang, Zetao, Xie, Wenchong, Chen, Hui, Wang, Yongliang
• Format: Journal Article
• Language: English
• Published: The Institution of Engineering and Technology 01.07.2017
• Subjects: airborne radar; Bayes methods; Bayesian learning strategy; clutter suppression algorithm; computational complexity; learning (artificial intelligence); MMV; multiple measurement vectors; parameter‐dependent sparse recovery; radar clutter; radar computing; radar signal processing; Research Article; space‐time adaptive processing algorithm; sparse Bayesian learning; sparsity‐based STAP algorithm; clutter suppression algorithm; space-time adaptive processing algorithm; radar signal processing; Bayesian learning strategy; radar computing; radar clutter; MMV; parameter-dependent sparse recovery; multiple measurement vectors; Bayes methods; learning (artificial intelligence); sparsity-based STAP algorithm; airborne radar; sparse Bayesian learning; computational complexity
• ISSN: 1751-9675; 1751-9683; 1751-9683
• DOI: 10.1049/iet-spr.2016.0183

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.