Abstract
In this paper, a new robust adaptive beamforming method is proposed in order to improve the robustness against steering vector (SV) mismatches that arise from multiple types of array errors. First, the sub-array technique is applied in order to obtain the decoupled sample covariance matrix (DSCM), in which the auxiliary sensors are selected to decouple the array. The decoupled interference-plus-noise covariance matrix (DINCM) is reconstructed with the estimated interference SV and maximum eigenvalue of the DSCM. Furthermore, the desired signal SV is estimated as the corresponding eigenvector determined by the correlation coefficients of the assumed SV and eigenvectors. Finally, the optimal weighting vector is obtained by combining the reconstructed DINCM and the estimated desired signal SV. Our simulation results show significant signal-to-interference-plus-noise ratio (SINR) enhancement of the proposed method over existing methods under multiple types of array errors.
Highlights
Adaptive beamforming has gained attention as an effective technique in array signal processing, due to its good target detection performance [1,2]
Two interferences were assumed from DOAs of −50◦ and 32◦ with INR 20 dB, while the desired signal was pointed with a DOA of 0◦
This paper introduced a new robust adaptive beamforming method, which is robust to the sensor position, gain-phase, and mutual coupling errors
Summary
Adaptive beamforming has gained attention as an effective technique in array signal processing, due to its good target detection performance [1,2]. Interference-plus-noise covariance matrix (INCM) reconstruction-based algorithms have been shown to obtain excellent beamforming performance when the array manifold is accurately known [25,26,27], but they are not suitable for situations where an array of manifold mismatches exist [28]. Ye et al proposed a method where the mutual coupling effect could be mitigated by selecting middle array elements [9], but the presence of desired signal degrades its performance at high SNRs. Recently, the researchers combined the middle subarray technique and covariance matrix reconstruction technique in order to obtain the interfernce-noise covariance matrix in [29]. The middle array interference-plus-noise covariance matrix (INCM) is accurately reconstructed with estimated interference SV and power, which handles the problem of multiple types of array errors, and mitigates the effect of the desired signal in the sample snapshots. The notation E [·] denotes the expectation operator and I stands for the unit matrix. is the Hadamard product. [·]−1 represents the matrix inversion operator
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