模式耦合稀疏贝叶斯MIMO雷达成像

通过压缩感知稀疏恢复理论可利用少量MIMO雷达收发阵元实现对目标的高分辨成像。利用MIMO雷达目标图像的块稀疏特性,将模式耦合稀疏贝叶斯学习算法应用于MIMO雷达成像,首先建立MIMO面阵回波信号模型,引入模式耦合稀疏贝叶斯分层模型,将相邻系数通过共用超参数的方法耦合起来。通过贝叶斯推理得到雷达信号的估计式,再通过EM算法实现对超参数的迭代估计,进而实现对雷达信号的估计,直到信号满足误差允许范围,最后重构信号实现MIMO阵列高分辨成像。仿真实验表明,该方法的成像效果在图像的聚焦性能上优于传统的傅里叶、稀疏贝叶斯算法,在散射点重构上优于OMP算法。

MIMO Radar Imaging Based on Pattern Coupled Sparse Bayesian Learning

MIMO radar imaging relies on data acquired by a large number of transceivers, and a small number of MIMO radar can be used to realize high resolution imaging of the target by means of compressed sensing sparse recovery theory. This paper utilizes the Pattern coupled sparse bayesian learning algorithm applied to MIMO radar imaging. For this reason, the paper proposes a MIMO radar imaging method based on PCSBL. The method utilizes the corresponding characteristics at the target adjacent scattering points described by a pattern coupled hierarchical Gaussian prior and the Expectation Maximization algorithm for realizing the iterative estimation of the hype parameter, and for further reconstructing the radar target block region accurately. The simulation results show that this method is better than the traditional Fourier and Sparse bayesian algorithm, and is better than the OMP algorithm in scattering point reconstruction.