A Two-Level Weighted Low-Complexity Adaptive Beamforming Method

Aiming at the problem of high computational complexity of adaptive beamforming techniques in array radar systems, a two-level weighted low-complexity adaptive beamforming method is proposed in this letter. First, the uniform linear array is divided into subarrays and each subarray has the same elements. The desired signal received by each array element in the subarray is then superimposed by compensating for the delay using a first level weighting. Finally, the interference signals and noise are suppressed using a second level weighting to obtain the ideal output signal to interference plus noise ratio. Simulation results verify the effectiveness and reliability of the proposed method.

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Publication Details

Journal
IEEE Signal Processing Letters
Published
2025-01-01
DOI
https://doi.org/10.1109/lsp.2025.3551957
Citations
1
Primary Topic
Advanced Adaptive Filtering Techniques
Type
article
Field-Weighted Citation Impact
1.33

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article

A Two-Level Weighted Low-Complexity Adaptive Beamforming Method

Bin Ba, Yang Du, Weijia Cui, Long Zhang et al.
1 citations
IEEE Signal Processing Letters
Advanced Adaptive Filtering Techniques
1.33
article

A Two-Level Weighted Low-Complexity Adaptive Beamforming Method

Bin Ba, Yang Du, Weijia Cui, Long Zhang, Yangyang Chen
article en
1 citations

Abstract

Aiming at the problem of high computational complexity of adaptive beamforming techniques in array radar systems, a two-level weighted low-complexity adaptive beamforming method is proposed in this letter. First, the uniform linear array is divided into subarrays and each subarray has the same elements. The desired signal received by each array element in the subarray is then superimposed by compensating for the delay using a first level weighting. Finally, the interference signals and noise are suppressed using a second level weighting to obtain the ideal output signal to interference plus noise ratio. Simulation results verify the effectiveness and reliability of the proposed method.

IEEE Signal Processing LettersVol. 32
PLA Information Engineering University (CN)
National Natural Science Foundation of China
Affordable and clean energy
Openalex Percentile: Top 26%
Advanced Adaptive Filtering Techniques
1.33
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