张群1,2,
苏令华1,
梁佳1,
罗迎1
1.空军工程大学信息与导航学院 ??西安 ??710077
2.复旦大学波散射与遥感信息国家教育部重点实验室 ??上海 ??200433
基金项目:国家自然科学基金(61631019)，陕西省自然科学基础研究计划项目(2016JM4008, 2018JM6072)
详细信息
作者简介:李瑞：男，1992年生，博士生，研究方向为组网雷达成像、雷达关联成像
张群：男，1964年生，教授，博士生导师，研究方向为雷达成像、雷达目标识别、雷达对抗等
苏令华：男，1979年生，讲师，研究方向为信号处理
梁佳：男，1985年生，讲师，研究方向为信号处理
罗迎：男，1984年生，副教授，博士生导师，研究方向雷达信号处理、雷达成像与目标识别
通讯作者:李瑞 liruimissing@163.com
中图分类号:TN957.52计量
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被引次数:0
出版历程
收稿日期:2018-09-30
修回日期:2019-02-25
网络出版日期:2019-03-14
刊出日期:2019-12-01
Bistatic Radar Coincidence Imaging Based on Sparse Bayesian Learning
Rui LI1,,,Qun ZHANG1,2,
Linghua SU1,
Jia LIANG1,
Ying LUO1
1. Institute of Information and Navigation, Air Force Engineering University, Xi’an 710077, China
2. Key Laboratory of Wave Scattering and Remote Sensing Information, Fudan University, Shanghai 200433, China
Funds:The National Natural Science Foundation of China (61631019), The Natural Science Foundation Research Program of Shaanxi Province (2016JM4008, 2018JM6072)
摘要
摘要:双基雷达具有隐蔽性高、抗干扰性能强等优点，在现代电子战中发挥重要作用。基于雷达关联成像原理，该文研究运动目标双基雷达关联成像问题。首先，针对采用均匀线性阵列作为收发天线的双基雷达系统，在发射随机频率调制信号条件下，分析运动目标雷达回波信号特点，建立双基雷达关联成像参数化稀疏表征模型；其次，针对建立的参数化稀疏表征模型，提出一种基于稀疏贝叶斯学习的迭代关联成像算法。该算法在建立贝叶斯模型基础上，通过贝叶斯推理，得到稀疏重构信号，从而实现对运动目标成像和运动参数的精确估计。最后，通过仿真实验验证所提方法的有效性。
关键词:双基雷达/
雷达关联成像/
稀疏贝叶斯学习/
参数化稀疏表征
Abstract:Bistatic radar has the advantages of high concealment and strong anti-interference performance, and plays an important role in modern electronic warfare. Based on the principle of radar coincidence imaging, the problem of bistatic radar coincidence imaging of moving targets is studied. Firstly, based on the bistatic radar system that uses uniform linear array as the transmitting and receiving antenna, the characteristics of the moving target radar echo signal are analyzed under the condition of transmitting random frequency modulation signal, and a bistatic radar coincidence imaging parametric sparse representation model is established. Secondly, an iterative coincidence imaging algorithm based on sparse Bayesian learning is proposed for the parametric sparse representation model established. Based on the Bayesian model, the sparse reconstructed signal is obtained by Bayesian inference, so that the moving target imaging and accurate estimation of motion parameters can be achieved. Finally, the effectiveness of the proposed method is verified by simulation experiments.
Key words:Bistatic radar/
Radar coincidence imaging/
Sparse Bayesian learning/
Parametric sparse representation
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