Robust Adaptive Beamforming via Simplified Interference Power Estimation

In this paper, we propose an adaptive beamforming algorithm for large uniform linear arrays (ULAs), where only a nested subarray is utilized to calculate the beamforming coefficients for the original ULA. In this algorithm, the steering vectors and powers of the signal-of-interest (SOI) and interferences are firstly estimated using the Capon spatial spectrum and known array structure, and the interferenceplus-noise covariance matrix (INCM) is then constructed. Subsequently, an augmented INCM is formed via vectorization and spatial smoothing operations. Finally, the beamformer weight vector is determined by the augmented INCM and the estimated SOI steering vector. Our proposed algorithm exploits the enhanced degrees of freedom of the nested array, and thus can be applied to a large ULA to reduce the implementation complexity. Moreover, it fundamentally eliminates the SOI component. Numerical results demonstrate that the proposed algorithm performs better than the existing approaches. INDEX TERMS Adaptive beamforming, nested subarray, spatially smoothed matrix (SSM), augmented INCM (AINCM). ZHI ZHENG (Member, IEEE) received the M.S. degree in electronic engineering and the Ph.D. degree in information and communication engineering from the

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“…Comparing (34) and (35) results in (33). Consequently, the following equation holds: (16) to result in the proposed beamformer (38).…”

Section: Proposed Algorithmmentioning

confidence: 96%

Robust and Efficient Adaptive Beamforming Using Nested Subarray Principles

et al. 2020

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“…Adaptive beamforming aims at extracting desired signal (DS) while suppressing interferences and noise and is a fundamental technique in array signal processing [1][2][3][4][5]. Traditional adaptive beamformers mostly focus on second-order circular signals with stationary observations, such as minimum variance distortionless response (MVDR) beamformer and linearly constrained minimum variance (LCMV) beamformer [6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Robust widely linear beamforming using estimation of extended covariance matrix and steering vector

Meng

Zhou

Gazor

2020

J Wireless Com Network

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The distribution of the received signals in many array processing applications is noncircular. Although optimal widely linear beamformer (WLB) can provide the best performance for noncircular received signals, its performance degrades severely under model mismatches in practical applications. As a remedy, we propose a robust WLB by using precise reconstruction of extended interference-plus-noise covariance matrix (EINCM) and low-complexity estimation of extended desired signal steering vector (EDSSV). We propose to first determine the steering vectors, powers, and noncircularity coefficients of all signals and the noise power. In contrast to the previous reconstruction methods using the integration over a wide angular sector, we reconstruct the interference-plus-noise covariance matrix (INCM) and the pseudo INCM accurately according to their definitions. By using INCM and pseudo INCM, we can precisely reconstruct the EINCM. We propose to estimate the EDSSV by intersecting two extended subspaces, which are respectively formed by eigendecomposing the extended sample covariance matrix and the extended desired signal covariance matrix. Unlike the convex optimization methods, the proposed EDSSV estimation does not require any optimization programming and yields a solution with closed expression in low computational complexity. Simulation results show that the proposed robust WLB provides near optimal performance under several model mismatch cases.

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“…The use of uncertainty set (US) algorithm is limited, as the size of the uncertain set is hard to determine and the desired signal is still involved in the SCM [15][16][17][18][19][20][21][22][23][24]. 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].…”

Section: Introductionmentioning

confidence: 99%

An Improved Sub-Array Adaptive Beamforming Technique Based on Multiple Sources of Errors

Xie

Zhu

Fan

et al. 2020

JMSE

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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.

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Robust Adaptive Beamforming via Simplified Interference Power Estimation

Cited by 71 publications

References 54 publications

Robust and Efficient Adaptive Beamforming Using Nested Subarray Principles

Robust and Efficient Adaptive Beamforming Using Nested Subarray Principles

Robust widely linear beamforming using estimation of extended covariance matrix and steering vector

An Improved Sub-Array Adaptive Beamforming Technique Based on Multiple Sources of Errors

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