未预期模型误差影响下秩减估计空域谱的统计特性及分辨概率

Statistical Characteristics and Resolution Probability of Rank Reduction Spatial Spectrum in the Presence of Unexpectant Model Errors

秩减估计器能在未知预期的阵列误差的条件下估计信源方位,并可避免迭代运算和局部收敛,但该类方法对阵列误差模型有一定要求,实际中未预期的模型误差会影响其角度分辨性能.为此,该文分析了未预期模型误差影响下秩减估计器的角度分辨性能,给出了秩减估计空域谱关于未预期模型误差的二阶表示及其统计特性,并推导了三类角度分辨概率的计算公式.针对用于均匀阵列互耦自校正、依赖方位的幅相误差自校正这两种秩减估计器进行数值实验,验证了理论推导的正确性.

Rank reduction estimator （RARE） can provide direction-of-arrival （DOA） estimation without knowing the （expectant） array model errors, and avoid iterative computation and local convergence. However, RARE requires the array errors model, and its angle resolution performance is affected by unexpectant model errors in practice. We analyze the angle resolution performance of RARE. The second-order error representation and statistical properties of the perturbed RARE spatial spectrum are obtained based on the derivation of three types of angle resolution probability. The theoretical analysis is validated in the numerical experiment aimed at RARE for mutual coupling self-calibration of uniform array, and direction-dependent amplitude-phase errors self-calibration.