Joint 2D-DOD and 2D-DOA Estimation for Coprime EMVS–MIMO Radar

Abstract

The issue of two-dimensional (2D) direction-of-departure and direction-of-arrival estimation for bistatic multiple-input multiple-output (MIMO) radar with a coprime electromagnetic vector sensor (EMVS) is addressed in this paper, and a tensor-based subspace algorithm is proposed. Firstly, the covariance measurement of the received data is arranged into a fourth-order tensor, which can maintain the multi-dimensional characteristic of the received data. Then, the higher-order singular value decomposition is followed to get an accurate signal subspace. By utilizing the uniformity of the subarrays in coprime EMVS-MIMO radar, the rotation invariant technique is adopted to achieve ambiguous elevation angle estimation. Thereafter, the unambiguous elevation angles are recovered by exploring the coprime characteristic of the subarrays. Finally, all azimuth angles are achieved by using the vector cross-product strategy. The tensor nature inherited from the array measurement is fully explored, and the coprime geometry enables EMVS-MIMO radar to achieve larger array aperture than the existing uniform linear configuration; thus, the proposed method offers better estimation performance than current state-of-the-art algorithms. Several computer simulations validate the effectiveness of the proposed algorithm.

Fast Target Localization Method for FMCW MIMO Radar via VDSR Neural Network

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“…When the array aperture is constant, the non-uniform array can effectively reduce the array element number and implementation costs, therefore, it is widely used. The coprime array can enlarge the array aperture [4][5]. In [4], a two-dimensional direction-of-departure and DOA estimation for bistatic multiple-input multiple-output radar was proposed by introducing an improved higher order singular value decomposition estimator and using a coprime electromagnetic vector sensor, and a tensor-based subspace algorithm is proposed.…”

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