Underdetermined 2D-DOD and 2D-DOA Estimation for Bistatic Coprime EMVS-MIMO Radar: From the Difference Coarray Perspective

TL;DR

This paper introduces a tensor-based method for underdetermined 2D direction-of-departure and direction-of-arrival estimation in bistatic coprime EMVS-MIMO radar, enhancing accuracy and resolving paired parameters.

Contribution

It develops a novel tensor model and processing framework leveraging difference coarrays and tensor decomposition for improved 2D parameter estimation.

Findings

Superior estimation accuracy demonstrated in simulations.

Effective handling of underdetermined 2D-DOD and 2D-DOA estimation.

Enhanced parameter pairing through tensor-based approach.

Abstract

In this paper, the underdetermined 2D-DOD and 2D-DOA estimation for bistatic coprime EMVS-MIMO radar is considered. Firstly, a 5-D tensor model was constructed by using the multi-dimensional space-time characteristics of the received data. Then, an 8-D tensor has been obtained by using the auto-correlation calculation. To obtain the difference coarrays of transmit and receive EMVS, the de-coupling process between the spatial response of EMVS and the steering vector is inevitable. Thus, a new 6-D tensor can be constructed via the tensor permutation and the generalized tensorization of the canonical polyadic decomposition. {According} to the theory of the Tensor-Matrix Product operation, the duplicated elements in the difference coarrays can be removed by the utilization of two designed selection matrices. Due to the centrosymmetric geometry of the difference coarrays, two DFT beamspace matrices were subsequently designed to convert the complex steering matrices into the real-valued ones, whose advantage is to improve the estimation accuracy of the 2D-DODs and 2D-DOAs. Afterwards, a third-order tensor with the third-way fixed at 36 was constructed and the Parallel Factor algorithm was deployed, which can yield the closed-form automatically paired 2D-DOD and 2D-DOA estimation. The simulation results show that the proposed algorithm can exhibit superior estimation performance for the underdetermined 2D-DOD and 2D-DOA estimation.