线性电磁矢量阵列的空时极化平滑算法及相干源多参数估计

该文提出了一种基于线性电磁矢量阵列的空时极化平滑算法(STPSA),解决了相干信源的频率，2维波达方向和极化参数的联合估计问题。该算法通过对不同子阵和矢量传感器不同传感单元的测量数据及其相应延迟数据进行平滑，实现解相干预处理并抑制噪声干扰，然后利用传播算子方法得到相应的参数估计。与目前的算法相比，该文提出的算法能够同时实现相干信源的多个参数联合估计；无需通过奇异值或特征值分解提取信号/噪声子空间，也无须进行参数搜索，有较低的运算量；另外，算法能够通过增加相邻阵元的间隔来扩展阵列的有效孔径，改善估计性能，且无须进行参数去模糊处理。仿真结果验证了算法的有效性。

Spatial-temporal Polarization Smoothing for Joint Frequency,2-D DOA,and Polarization Estimation of Coherent Sources with Linear Electromagnetic Vector Sensors Array

This paper studies the issue of joint estimation of frequency, two-dimensional (2-D) Direction Of Arrival (DOA) and polarization of coherent sources using a uniform linear electromagnetic vector sensors array, and a Spatial-Temporal Polarization Smoothing Algorithm (STPSA) is proposed. The STPSA is used to decorrelate the source coherency and eliminate the effect of noise with the two time-delayed sets of data collected from different subarrays and different sensor components of vector sensors, and then the propagator method is applied to estimate the source’frequency, 2-D DOA and polarization. Comparing with the existing estimate methods, the proposed method requires neither the eigen-decomposition into signal/noise subspaces nor the iteratively searching, hence, has lower computational load. Furthermore, the proposed method can offer the improved estimation performance by increasing the intervector sensor spacing much farther apart than a half wavelength to extend aperture without any additional disambiguation processing. Simulation results show the effectiveness of the proposed method.