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.
作者简介: Zhong Jinrong was born in Guangxi,China in 1985. He received the BS andMS degrees from National Universityof Defense Technology in 2008, and2010, respectively. He is a doctoralcandidate in information and communicationengineering at the Key Laboratory of ATR, NationalUniversity of Defense Technology. His main interestsare automatic target recognition, radar target characteristics,and radar signal processing.E-mail: zhong_nudt@163.comWen Gongjian was born in Changsha,China in 1972. He received the BS,MS, and Ph.D. degrees from NationalUniversityof Defense Technologyin1994, 1997, and 2000, respectively.Then he went through two years ofpostdoctoral work at Wuhan University. He is currently aProfessor with National University of Defense Technologyand the head of the fourth department of the key laboratoryof ATR. He is mainly interested in remote sensing,photogrammetry, and image understanding.
引用本文:
钟金荣 文贡坚. 基于块稀疏贝叶斯学习的雷达目标压缩感知(英文)[J]. 雷达学报, 2016, 5(1): 99-108.
Zhong Jinrong, Wen Gongjian. Compressive Sensing for Radar Target Signal Recovery Based on Block Sparse Bayesian Learning(in English). JOURNAL OF RADARS, 2016, 5(1): 99-108.
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