Direction of arrival estimation in the presence of model errors by signal subspace matching

Wax, M.; Adler, Amir

doi:10.1016/j.sigpro.2020.107900

Cited by 11 publications

(1 citation statement)

References 35 publications

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“…After performing the weighted superposition of data received by each array element, the main lobe of the beam is aligned with the signal of interest (SOI), and a null is formed in the direction of the interference signal to suppress the interference, which effectively improves the system’s output performance. Because most methods based on this technology require prior information on the incoming direction of the desired signal, they have been frequently combined with the high-resolution direction of arrival (DOA) estimation algorithms [ 4 , 5 ] to improve an antenna array system’s interference suppression ability and output performance. A minimum variance distortionless response (MVDR) beamformer can maximize the output signal-to-interference-plus-noise ratio (SINR) under the ideal condition that the SOI steering vector (SV) and interference-plus-noise covariance matrix (INCM) are known [ 6 , 7 ].…”

Section: Introductionmentioning

confidence: 99%

Virtual covariance matrix reconstruction-based adaptive beamforming for small aperture array

Chang,

Zhang,

Yang

et al. 2023

PLoS ONE

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Recently, many robust adaptive beamforming (RAB) algorithms have been proposed to improve beamforming performance when model mismatches occur. For a uniform linear array, a larger aperture array can obtain higher array gain for beamforming when the inter-sensor spacing is fixed. However, only the small aperture array can be used in the equipment limited by platform installation space, significantly weakening beamforming output performance. This paper proposes two beamforming methods for improving beamforming output in small aperture sensor arrays. The first method employs an integration algorithm that combines angular sector and gradient vector search to reconstruct the interference covariance matrix (ICM). Then, the interference-plus-noise covariance matrix (INCM) is reconstructed combined with the estimated noise power. The INCM and ICM are used to optimize the desired signal steering vector (SV) by solving a quadratically constrained quadratic programming (QCQP) problem. The second method proposes a beamforming algorithm based on a virtual extended array to increase the degree of freedom of the beamformer. First, the virtual conjugated array element is designed based on the structural characteristics of a uniform linear array, and received data at the virtual array element are obtained using a linear prediction method. Then, the extended INCM is reconstructed, and the desired signal SV is optimized using an algorithm similar to the actual array. The simulation results demonstrate the effectiveness of the proposed methods under different conditions.

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