Wang Junxiang, Wang Xianpeng, Xu Dingjie, Bi Guoan
College of Automation, Harbin Engineering University, Harbin 150001, China.
State Key Laboratory of Marine Resource Utilization in South China Sea, Hainan University, Haikou 570228, China.
Sensors (Basel). 2018 Mar 9;18(3):832. doi: 10.3390/s18030832.
This paper deals with joint estimation of direction-of-departure (DOD) and direction-of- arrival (DOA) in bistatic multiple-input multiple-output (MIMO) radar with the coexistence of unknown mutual coupling and spatial colored noise by developing a novel robust covariance tensor-based angle estimation method. In the proposed method, a third-order tensor is firstly formulated for capturing the multidimensional nature of the received data. Then taking advantage of the temporal uncorrelated characteristic of colored noise and the banded complex symmetric Toeplitz structure of the mutual coupling matrices, a novel fourth-order covariance tensor is constructed for eliminating the influence of both spatial colored noise and mutual coupling. After a robust signal subspace estimation is obtained by using the higher-order singular value decomposition (HOSVD) technique, the rotational invariance technique is applied to achieve the DODs and DOAs. Compared with the existing HOSVD-based subspace methods, the proposed method can provide superior angle estimation performance and automatically jointly perform the DODs and DOAs. Results from numerical experiments are presented to verify the effectiveness of the proposed method.
本文通过开发一种基于协方差张量的新型鲁棒角度估计方法，研究了在存在未知互耦和空间色噪声的双基地多输入多输出（MIMO）雷达中联合估计出发方向（DOD）和到达方向（DOA）的问题。在所提出的方法中，首先构造一个三阶张量来捕捉接收数据的多维特性。然后，利用色噪声的时间不相关特性和互耦矩阵的带状复对称托普利兹结构，构造一个新颖的四阶协方差张量，以消除空间色噪声和互耦的影响。通过使用高阶奇异值分解（HOSVD）技术获得鲁棒的信号子空间估计后，应用旋转不变技术来实现DOD和DOA。与现有的基于HOSVD的子空间方法相比，该方法能够提供更优的角度估计性能，并且能够自动联合估计DOD和DOA。给出了数值实验结果以验证所提方法的有效性。
