基于稀疏表示的双基地MIMO雷达多目标定位及幅相误差估计

 基于稀疏表示理论,提出一种新的双基地多输入多输出(MIMO)雷达收发角度及幅相误差估计算法。利用接收数据,分别构造发射和接收协方差矩阵,并以列向量化后的发射和接收协方差矩阵为量测信号建立2个一维稀疏线性模型,构造模型求解的 L₂-L₁ 混合范数优化目标函数,通过交替迭代寻优获得目标角度估计和幅相误差估计,最后给出了本文算法的收敛性分析。与现有算法相比,该算法充分利用了目标发射和接收空域的稀疏特性,且能够通过对噪声功率的预估计来抑制噪声。仿真结果表明:在低信噪比(SNR)条件下,本文算法仍能够得到较好的估计精度,且对幅相误差具有一定的稳健性。

Localization and Estimation of Gain-phase Error for Bistatic MIMO Radar Based on Sparse Representation

A new algorithm is presented for the joint estimation of angle and gain-phase error of a bistatic multiple-input multiple-output (MIMO) radar based on sparse representation. The transmitting and receiving covariance matrices are constructed by using the received data. Two one-dimensional sparse linear models are obtained by performing the vectorization operation on the transmitting and receiving covariance matrices. Then the mixed L₂-L₁ norm cost functions are constructed, in which the solution is derived by utilizing the alternating minimization technique. Furthermore, the coverage analysis of the iterative algorithm is provided. Compared with the existing algorithms, the proposed method fully utilizes the sparse characteristic of the spatial field of a target, and the noise can be suppressed by pre-estimating the noise power. The simulation results show that the proposed method can achieve good estimation performance even under low signal to noise ratio (SNR) and is robust against the variation of gain-phase errors.