基于子空间的阵元误差估计方法

摘要

The knowledge-aided secondary data selection method is a model-based algorithm,but its per-formance degrades significantly when there is a mismatch due to array errors between the assumed clutter model and the received data.To address this issue,an approach based on the subspace for array calibration is presen-ted.Firstly,the orthogonal complement subspace of clutter can be represented using the radar geometry param-eter.Then,a left singular vector that corresponds to the maximal singular value is constructed by decomposi-tion of the received data.Finally,using the orthogonality between them,the array error can be estimated.Sim-ulation results show that this method can estimate array errors accurately,and improve the robustness of the knowledge-aided secondary data selection method.%基于知识辅助的样本挑选算法是一种模型化的算法，当存在阵元误差时，假定的杂波模型与实际接收数据不匹配，算法性能严重下降。为了解决这个问题，提出一种基于子空间的阵元误差估计方法。该方法首先利用雷达构型参数计算杂波的正交补空间，再对接收数据分解得到最大奇异值对应的左奇异向量，最后利用两者的正交性来估计阵元误差。仿真实验表明，该方法可以准确地估计阵元误差，提高知识辅助样本挑选算法的稳健性。