Open AccessSparsity‐based STAP algorithm with multiple measurement vectors via sparse Bayesian learning strategy for airborne radar
Author(s) -
Duan Keqing,
Wang Zetao,
Xie Wenchong,
Chen Hui,
Wang Yongliang
Publication year - 2017
Publication title -
iet signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.384
H-Index - 42
ISSN - 1751-9683
DOI - 10.1049/iet-spr.2016.0183
Subject(s) - algorithm , clutter , space time adaptive processing , computer science , radar , convergence (economics) , hyperparameter , bayesian probability , artificial intelligence , continuous wave radar , radar imaging , economics , economic growth , telecommunications
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.