摘要
由于其较低的成像成本和较强的鲁棒性,使得利用多发射机和多接收机对目标进行有效观测的分布式无源雷达成为雷达技术研究的热门领域。本文在分布式雷达稀疏成像模型基础上,提出一种分布式无源雷达成像接收机配置优化方法,以成像分辨率最高为优化目标函数,针对不同发射机布局采用遗传算法计算出最优接收机布局。同时针对正交匹配追踪(Orthogonal Matching Pursuit,OMP)算法在低信噪比下成像精度较低,信号估计不准确的情况,推导出用协方差稀疏表示接收信号,利用稀疏贝叶斯学习(Sparse Bayesian Learning,SBL)进行信号重构的成像算法,并通过仿真实验对成像性能的改善进行了验证。
Due to its low imaging cost and strong robustness, the distributed passive radar had become a popular research area. Based on the distributed radar sparse imaging model, this paper proposes a distributed passive radar imaging receiver configuration optimization method, with the highest imaging resolution as the optimization objective function, and the genetic algorithm to calculate the optimal receiver for different transmitter layouts. At the same time, the Orthogonal Matching Pursuit algorithm has low imaging accuracy and low SNR, and the signal estimation is inaccurate. It is derived that the received signal is sparsely represented by covariance, and Sparse Bayesian Learning is used. An imaging algorithm for signal reconstruction is performed, and the improvement of imaging performance is verified by simulation experiments.
作者
公富康
张顺生
GONG Fu-kang;ZHANG Shun-sheng(Research Institute of Electronic Science and Technology, University of ElectronicScience and Technology of China, Chengdu, Sichuan 611731, China)
出处
《信号处理》
CSCD
北大核心
2018年第11期1339-1344,共6页
Journal of Signal Processing
基金
国家自然科学基金(61671122)
装备预研重点实验室基金(614241302040217)
关键词
分布式无源雷达
布局优化
协方差稀疏表示
稀疏贝叶斯学习
distributed passive radar
station layout optimization
sparsely represented by covariance
sparse Bayesian learning
