基于稀疏贝叶斯学习的双基雷达关联成像

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。

Bistatic Radar Coincidence Imaging Based on Sparse Bayesian Learning

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.