Coarray Tensor Train Decomposition for Bistatic MIMO Radar With Uniform Planar Array

doi:10.1109/TAP.2025.3567446

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Coarray Tensor Train Decomposition for Bistatic MIMO Radar With Uniform Planar Array

2025-08-01

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DOI：10.1109/TAP.2025.3567446

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Abstract

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Recently, tensor network decomposition has attracted increasing attention due to its high efficacy in modeling multidimensional correlations in high-order tensors. Considering this advantage, this study employs tensor train decomposition (TTD) to achieve joint 2-D direction-of-departure (2D-DOD) and 2-D direction-of-arrival (2D-DOA) estimation in bistatic multiple-input multiple-output (MIMO) radar systems with uniform planar array (UPA) configurations. First, a 5-D tensor is defined to incorporate the 2-D spatial information from transmit UPA (TUPA) and receive UPA (RUPA), which is then combined with temporal information. Next, a low-rank 4-D tensor is generated by performing an autocorrelation operation along the temporal dimension of the 5-D tensor, forming a structure that encapsulates the difference coarrays of the TUPA and RUPA. Then, a TTD framework is designed to decompose the 4-D 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.

Keywords:

2-D direction-of-departure (2D-DOD) and 2-D direction-of-arrival (2D-DOA) estimation

bistatic uniform planar array (UPA) multiple-input multiple-output (MIMO) radar

high-order tensor

tensor train decomposition (TTD)

Vandermonde matrix