一种基于平行因子分解的电磁矢量传感器MIMO雷达测向算法

A Direction-Finding Algorithm for Electromagnetic Vector Sensor MIMO Radar Based on Parallel Factor Decomposition

现有的电磁矢量传感器?多输入多输出(Electromagnetic vector sensor multiple?input multiple?output, EMVS?MIMO)雷达大多限制收发阵元以半波长约束分布,极大限制了雷达测向的分辨率。针对此问题,本文提出了一种基于平行因子(Parallel factor, PARAFAC)分解方法对目标进行二维角度估计的算法,该算法适用于所有任意发射阵列几何以及任意稀疏的接收阵列几何。首先,对接收阵列的匹配滤波后的信号构造三阶平行因子张量模型。然后,利用PARAFAC分解,得到发射方向、接收方向以及复合因子矩阵的估计值。最后,联合旋转不变法、矢量叉积法和最小二乘法,得到目标的高分辨率、无模糊特性二维角度估计的闭式解。本文所提出的算法具有精度高、计算复杂度低的特点,计算机仿真验证了基于张量分解的算法在任意双基地EMVS?MIMO雷达模型上的应用,该算法能够准确地估计出多个目标的二维角度参数,证明了所提算法的有效性和优越性。

Most existing electromagnetic vector sensor multiple-input multiple-output(EMVS-MIMO)radars restrict the distribution of transceiver array elements.The resolution of radar direction measurement is limited due to half-wavelength constraints.To address this limitation,this paper proposes an algorithm based on the parallel factor(PARAFAC)decomposition for two dimension(2D)angle estimation of the target.The algorithm is applicable to arbitrary transmitter array geometries and sparse receiver array geometries.First,a third-order PARAFAC tensor model is constructed for the matched-filtered signal of the receiving array.Second,the PARAFAC decomposition is utilized to estimate the transmit direction,receive direction,and composite factor matrix.Finally,a closed-form solution for high-resolution,ambiguity-free 2D angle estimation of the target is obtained by combining the rotationally invariant method,the vector outer product method and the least squares method.The proposed algorithm is characterized by high accuracy and low computational complexity.Computer simulations verify the tensor decomposition-based algorithm can be applied to an arbitrary dual-base EMVS-MIMO radar model,and can accurately estimate the 2D angular parameters of multiple targets.This validation demonstrates the effectiveness and superiority of the proposed algorithm.