雷达学报Issue(1)：99-108,10.DOI:10.12000/JR15056
基于块稀疏贝叶斯学习的雷达目标压缩感知
Compressive Sensing for Radar Target Signal Recovery Based on Block Sparse Bayesian Learning
摘要
Abstract
Nowadays, high-speed sampling and transmission is a foremost challenge of radar system. In order to solve this problem, a compressive sensing approach is proposed for radar target signals in this study. Consider-ing the block sparse structure of signals, the proposed method uses a simple measurement matrix to sample the signals and employ a Block Sparse Bayesian Learning (BSBL) algorithm to recover the signals. The classical BSBL algorithm is applicable to real signal, while radar signals are complex. Therefore, a Complex Block Sparse Bayesian Learning (CBSBL) is extended for the radar target signal reconstruction. Since the existed radar signal compressive sensing models do not take block structures in consideration, the signal reconstruction of proposed approach is more accurate and robust, and the simple measurement matrix leads to an easy imple-mentation of hardware. The effectiveness of the proposed approach is demonstrated by numerical simulations.关键词
雷达信号处理/压缩感知雷达/块结构/压缩测量/稀疏重构Key words
Radar signal processing/Compressive Sensing (CS) radar/Block structure/Compressed measure-ment/Sparse reconstruction分类
信息技术与安全科学引用本文复制引用
钟金荣,贡坚..基于块稀疏贝叶斯学习的雷达目标压缩感知[J].雷达学报,2016,(1):99-108,10.基金项目
Foundation Item：The New Century Excellent Talents Supporting Plan of Ministry Education (No.NCET-11-0866) （No.NCET-11-0866）
