基于块稀疏贝叶斯学习的雷达目标压缩感知(英文)

钟金荣文贡坚

doi:10.12000/JR15056

基于块稀疏贝叶斯学习的雷达目标压缩感知(英文)

DOI: 10.12000/JR15056

钟金荣文贡坚,

(国防科技大学自动目标识别重点实验室长沙 410073)

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- 被引次数: 0
出版历程
- 收稿日期: 2015-05-11
- 修回日期: 2016-02-01
- 网络出版日期: 2016-02-28

Compressive Sensing for Radar Target Signal Recovery Based on Block Sparse Bayesian Learning(in English)

(Science and Technology on Automatic Target Recognition Laboratory, National University of Defense Technology, Changsha 410073, China)

Funds:

The New Century Excellent Talents Supporting Plan of Ministry Education (No.NCET-11-0866)

摘要

摘要: 高速采样和传输是目前雷达系统面临的一个重要挑战。针对这一问题,该文提出一种利用信号块结构特性的雷达目标压缩感知方法。该方法采用一个简单的测量矩阵对信号进行采样,然后运用块稀疏贝叶斯学习算法恢复信号。经典的块稀疏贝叶斯学习算法适用于实信号,该文将其扩为可直接处理雷达信号的复数域稀疏贝叶斯算法。相对于现有压缩感知方法,该方法不仅具有更好的信号重构精度和鲁棒性,更重要的是其压缩测量矩阵形式简单、易于硬件实现。数值仿真实验结果验证了该方法的有效性。
- 雷达信号处理 /
- 压缩感知雷达 /
- 块结构 /
- 压缩测量 /
- 稀疏重构
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. Considering 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 implementation of hardware. The effectiveness of the proposed approach is demonstrated by numerical simulations.
- Radar signal processing /
- Compressive Sensing (CS) radar /
- Block structure /
- Compressed measurement /
- Sparse reconstruction

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