Coarray Tensor Train Decomposition for Bistatic MIMO Radar With Uniform Planar Array
Qianpeng Xie, Zhanling Wang, Fangqing Wen, Jin He, Trieu‐Kien Truong
Abstract
Recently, tensor network decomposition has attracted increasing attention due to its high efficacy in modeling multi-dimensional correlations in high-order tensors. Considering this advantage, this study employs tensor train decomposition (TTD) to achieve joint two-dimensional direction-of-departure (2D-DOD) and direction-of-arrival (2D-DOA) estimation in bistatic multiple-input multiple-output (MIMO) radar systems with uniform planar array (UPA) configurations. First, a five-dimensional (5D) tensor is defined to incorporate the two-dimensional (2D) spatial information from transmit and receive UPAs, which is then combined with temporal information. Next, a low-rank four-dimensional (4D) tensor is generated by performing an autocorrelation operation along the temporal dimension of the 5D tensor, forming a structure that encapsulates the difference coarrays of the transmit and receive UPAs. Then, a TTD framework is designed to decompose the 4D tensor into interconnected lower-order TT-cores. Furthermore, a precise bidirectional mapping is performed between the TT-cores and the original Vandermonde factor matrices. Finally, leveraging distinct combinations of TT-cores, two innovative methods are developed to recover the Vandermonde factor matrices. The proposed methods conduct automatic pairing of 2D-DOD and obtain 2D-DOA estimates by enforcing consistent permutation orders across all factor matrices. The proposed methods are verified by simulation experiments. The results confirm that the proposed methods can surpass the existing tensor-based methods in terms of estimation accuracy.