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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。
Based on the theory of sparse representation, a novel algorithm for estimating the transmission and reception angle and amplitude and phase error of bistatic MIMO (Multiple Input Multiple Output) MIMO radar is proposed. Based on the received data, the covariance matrixes of transmission and reception are constructed respectively, and two one-dimensional sparse linear models are constructed by using the vector of column vectors and the received covariance matrix as measurement signals. The L2-L1 mixed norm optimization The objective function is obtained through alternating iterative optimization to obtain the target angle estimation and the amplitude and phase error estimation. Finally, the convergence analysis of the proposed algorithm is given. Compared with the existing algorithms, this algorithm makes full use of the sparse characteristics of the target transmitting and receiving airspace, and can suppress the noise by estimating the noise power. The simulation results show that the algorithm can still get better estimation accuracy under low signal-to-noise ratio (SNR), and has certain robustness to amplitude and phase error.