基于贝叶斯压缩感知的FD-MIMO雷达Off-Grid目标稀疏成像

传统压缩感知(CS,Compressive Sensing)成像方法一般假定目标精确位于事先划定的成像网格上,实际中由于散射点空间位置是连续分布的,因此偏离网格(Off-grid)问题必然存在.这会引起真实回波测量值与默认系统观测矩阵之间失配,导致传统CS成像方法性能恶化.本文基于频率分集多输入多输出(FD-MIMO,Frequency Diverse Multiple-Input Multiple-Output)雷达,针对Off-grid目标提出了一种基于贝叶斯压缩感知的稀疏自聚焦(SAF-BCS,Sparse Autofocus Imaging Method Based on Bayesian Compressive Sensing)成像算法.该算法依据最大后验(MAP,Maximum A Posteriori)准则,利用变分贝叶斯学习技术求解含有Off-grid目标的稀疏像.与传统稀疏重构方法相比,所提方法充分利用了目标先验信息,可自适应调整参数,能够更好地反演稀疏目标,同时具有校正Off-grid目标的网格位置偏差以及估计噪声功率等优势.仿真结果表明SAF-BCS算法对网格划分不敏感,具有稳健的成像性能.

Bayesian Co mpressive Sensing-Based Sparse I maging for Off-Grid Target in Frequency Diverse MIMO Radar

Conventional compressive sensing (CS)imaging methods rely on the assumption that all scatterers in the ima-ging scene are located exactly on the pre-defined grids.However,since the scatterers are distributed in a continuous scene,the off-grid problem inevitably exists,which makes basis mismatch between echo measurement and the assumed sensing matrix,and leads to considerable performance degradation by CS-based methods.Therefore,this paper investigates the sparse imaging for off-grid target in frequency diverse multiple-input multiple-output (FD-MIMO)radar.A sparse autofocus imaging method based on Bayesian compressive sensing (SAF-BCS)is proposed.It employs the technique of variational Bayesian inference to achieve the imaging of off-grid scatterres in light of the criterion of maximum a posteriori (MAP).Compared with the conventional sparse re-covery algorithms,the proposed method adequately utilizing the prior information of the target,is able to automatically tune pa-rameters,and thus can provide a better capability to correct the off-grid errors,and to estimate the noise power,etc.Simulation re-sults confirm that SAF-BCS is not sensitive to grid discretization,and has a robust imaging performance.