基于三维压缩感知的MIMO雷达角度估计算法
Angle Estimation Algorithm for MIMO Radar Using Three-Way Compressive Sensing

基于张量模型的参数估计是雷达信号处理的一个发展趋势，然而现有张量算法法在估计精度和计算复杂度方面达到良好的折衷。为解决上述问题，提出一种三维压缩感知（Three-way compressive sensing，TWCS）的多输入多输出雷达角度估计算法。利用匹配滤波后的信号内部隐含的多维结构，将接收数据堆叠成一个三阶张量模型。为降低高维张量在存储和计算方面的复杂性，利用高阶奇异值分解对高维张量数据进行压缩。其次将压缩后的张量与三线性模型相联系，获取压缩的方向矩阵。利用目标角度在所处背景的稀疏性，设计两个过完备字典，采用优化的方法获取目标角度。由于利用了接收数据的多维结构，TWCS中参数估计的精度要优于传统的子空间算法。此外所提TWCS算法不需要额外配对计算，且能进一步获取目标的多普勒信息。最后，利用仿真实验验证TWCS算法的估计效果。
Tensor model-based parameters estimation is a trend for radar signal processing. However, the existing tensor-model based algorithms cannot achieve a good compromise between estimation accuracy and computational complexity. A three-way compressive sensing (TWCS) based algorithm is developed for angle estimation in multiple-input multiple-output radar. Exploiting the multidimensional structure inherent in the matched filtered data, a third-order tensor signal model is formulated. To lower the storage and computing complexity, the high-order singular value decomposition method is used to compressive the tensor data. The kernel tensor is linked to the trilinear model thus the compressed direction matrixes are obtained. Thereafter, the sparsity of the targets in the background is utilized and two overcomplete dictionaries are constructed for angle estimation with optimization methods. Taking advantage of the inherent multidimensional structure of the received data, the TWCS algorithm achieves better estimation accuracy than traditional subspace-based algorithms. In addition, the TWCS algorithm does not require further pairing of the estimated angles. Furthermore, it could achieve the doppler frequencies of the targets. Simulation results verify the effectiveness of the TWCS algorithm.