Robust Sparse Bayesian Two-dimensional DOA Estimation with Gain-phase Errors

doi:10.12382/bgxb.2023.0778

Abstract

Abstract:

To reduce the influence of gain-phase errors and improve the performance of direction-of-arrival (DOA) estimation, a robust sparse Bayesian two-dimensional DOA estimation method with gain-phase errors is proposed for the L-shaped sensor array. In the proposed method, an auxiliary angle is introduced to transform a 2D DOA estimation problem into two 1D angle estimation problems. A sparse representation model with gain-phase errors is constructed by using the diagonal element vector of the cross-covariance matrix of two submatrices of L-shaped sensor array. The expectation maximization algorithm is used to derive the unknown parameter expression,which is used to perform the iterative operations for obtaining the off-grid and the precision of signal. A new spatial spectral function is constructed by using the off-grid and the precision of signal. The auxiliary angle can be estimated by searching the new spatial spectra peak. The estimated auxiliary angle is introduced into the sparse representation model of the received data with gain-phase errors, and then the sparse Bayesian learning method is used to estimate the elevation angle of incident signal. According to the relationship among three angles, the azimuth angle can be estimated. The results show that this method realizes the automatic matching of azimuth angle and elevation angle, and improves the accuracy of DOA estimation and angle resolution. Simulated results verify the effectiveness of the proposed method.

Key words: direction-of-arrival estimation, gain-phase error, sparse signal reconstruction, sparse Bayesian learning, L-shaped sensor array

CLC Number:

TN911.7

WANG Xuhu, JIN Xu, HOU Yujun, ZHANG Qunfei, XU Zhenhua, WANG Xinjie, CHEN Jianjun. Robust Sparse Bayesian Two-dimensional DOA Estimation with Gain-phase Errors[J]. Acta Armamentarii, 2024, 45(10): 3608-3618.

Figures/Tables 9

Fig.1 Uniform L-shaped sensor array

Fig.2 Flow chart of the proposed method

Fig.3 DOA estimated results

Fig.4 DOA estimated results of different methods

Fig.5 RMSEs of azimuth angle of DOA estimation under different SNRs

Fig.6 RMSEs of azimuth angle of DOA estimation versus snapshots

Fig.7 RMSEs of azimuth angle of DOA estimation versus standard deviations of gain-phase errors

Fig.8 Resolution success probability of DOA estimation under different SNRs

Fig.9 Resolution success probability of DOA estimation versus standard deviations of gain-phase errors

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