Two-Dimensional DOA Estimation of MIMO Radar Coherent Source Based on Toeplitz Matrix Set Reconstruction

Abstract

In order to realize the high-precision direction of arrival (DOA) estimation of the coherent source of two-dimensional multiple-input and multiple-output (MIMO) radar, a solution is given by combining Toeplitz matrix set reconstruction. MIMO radar obtains a larger aperture with fewer arrays. Traditional two-dimensional reconstruction Toeplitz-like algorithms use part of the information in the construction of two correlation matrices or covariance matrices to construct the Toeplitz matrix when performing two-dimensional coherent source DOA estimation, which makes the information utilization incomplete and requires additional denoising processing. To solve the above problems, this paper proposes an improved Toeplitz matrix set reconstruction algorithm based on the two-dimensional reconstruction Toeplitz class algorithm. The complete array element receiving signal vector is used to construct two Toeplitz matrix sets containing complete information, and then their conjugate transposes. Multiply and sum to correct the matrix to obtain a full-rank matrix, so as to achieve the purpose of decoherence and combine the traditional ESPRIT algorithm to perform two one-dimensional reconstruction processing through rotation invariance and then perform angle matching to achieve two-dimensional coherent signal angle estimation, while avoiding additional denoising processing. Finally, the simulation results of the cross array and the L-shaped array verify the effectiveness of the algorithm in this paper and further extend it to the two-dimensional MIMO radar array model and compare it with the traditional ESPRIT-like algorithm and the REC-FBSS-ESPRIT algorithm. In comparison, the algorithm in this paper has better performance under the conditions of successful resolution, DOA estimation accuracy, and low signal-to-noise ratio.

Efficient Angle Estimation for MIMO Systems via Redundancy Reduction Representation

“…can be regarded as a cross-correlation matrix collected from a virtual cross array with A t (φ), A * r (θ) and R s being the two manifold matrices and signal correlation matrix. By now, we transform the original problem of DOD and DOA estimation from the covariance matrix R y for MIMO systems to a smaller-size problem of 2D DOA estimation from the core matrix Z also known as a cross-correlation matrix for an equivalent cross array [13], [14]. While the literature about angle estimation algorithms for cross arrays is limited, in this letter, we adopt the cross-correlation based algorithms for an L-shaped array [15]- [21] for 2D angle estimation since their cross-correlation matrices have similar structures.…”

A Nyström-Based Low-Complexity Algorithm with Improved Effective Array Aperture for Coherent DOA Estimation in Monostatic MIMO Radar

“…In this section, decorrelation processing is performed. Instead of the conventional SSP technique [27,28] and its variants [32][33][34][35][36][37][38], or matrix reconstruction [29][30][31] on the covariance matrix R for decorrelation processing, the approximate signal subspace U can be used to directly reconstruct the matrix for decorrelation, which makes the algorithm simpler. There is no need for covariance of the received signals and any other relevant calculation, such as the spatial smoothing technique or the methods based on Toeplitz matrix reconstruction.…”

“…To solve this problem, decorrelation processing is needed. The usual methods are spatial smoothing preprocessing (SSP) [27,28], Toeplitz-based methods [29,30], and Hankel matrix reconstruc-tion [31]. In traditional array signal processing, the basic idea of SSP is to partition the total array into several groups containing overlapping subarrays and take the average of the subarray covariance matrices with restored full rank for decorrelation.…”