基于波束成形矢量规范约束的稳健侧叶消除算法

IF 1.1 4区 工程技术 Q4 ENGINEERING, ELECTRICAL & ELECTRONIC
Qing Wang, Huanding Qin, Kai Yang, Hao Wu, Fangmin He, Jin Meng
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引用次数: 0

摘要

侧叶消除(SLC)是一种成熟的波束成形技术,用于减轻干扰,特别是在卫星通信(SATCOM)领域。然而,传统的 SLC 存在在高信噪比(SNR)条件下部分抵消所需信号的问题,这主要是由于无约束波束成形处理造成的。为解决这一问题,人们进行了大量研究;然而,现有算法存在局限性,如依赖于对信号阵列矢量或干扰者数量的了解,且计算复杂度高。在本文中,我们提出了一种基于波束成形矢量规范约束的鲁棒 SLC 算法。我们的建议提供了一种实用的解决方案,只需知道地面站天线增益和所需信号的最大辅助阵列增益,而这两者都是完全已知的。此外,与传统的 SLC 相比,我们提出的方法引入了额外的计算复杂度,而计算复杂度仅与辅助阵列的大小成线性关系。仿真结果表明,我们提出的方法与对角加载和基于空间自由度控制算法等现有技术的性能相当。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

A Robust Sidelobe Cancellation Algorithm Based on Beamforming Vector Norm Constraint

A Robust Sidelobe Cancellation Algorithm Based on Beamforming Vector Norm Constraint

Sidelobe cancellation (SLC) is a well-established beamforming technique for mitigating interference, particularly in the context of satellite communication (SATCOM). However, traditional SLC suffers from the issue of partially canceling the desired signal at high signal-to-noise ratio (SNR), primarily due to unconstrained beamforming processing. Extensive research has been conducted to address this problem; however, existing algorithms have limitations such as dependence on knowledge of signal array vectors or number of interferers and involve high computational complexity. In this paper, we propose a robust SLC algorithm based on beamforming vector norm constraint. Our proposal offers a practical solution by only requiring knowledge of the earth station antenna gain and maximum auxiliary array gain to the desired signal, both of which are fully known. Furthermore, compared to traditional SLC, our proposed method introduces additional computational complexity that only scales linearly with the size of the auxiliary array. Simulation results demonstrate comparable performance between our proposed method and existing techniques such as diagonal loading and spatial degrees-of-freedom control-based algorithms.

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来源期刊
IET Signal Processing
IET Signal Processing 工程技术-工程:电子与电气
CiteScore
3.80
自引率
5.90%
发文量
83
审稿时长
9.5 months
期刊介绍: IET Signal Processing publishes research on a diverse range of signal processing and machine learning topics, covering a variety of applications, disciplines, modalities, and techniques in detection, estimation, inference, and classification problems. The research published includes advances in algorithm design for the analysis of single and high-multi-dimensional data, sparsity, linear and non-linear systems, recursive and non-recursive digital filters and multi-rate filter banks, as well a range of topics that span from sensor array processing, deep convolutional neural network based approaches to the application of chaos theory, and far more. Topics covered by scope include, but are not limited to: advances in single and multi-dimensional filter design and implementation linear and nonlinear, fixed and adaptive digital filters and multirate filter banks statistical signal processing techniques and analysis classical, parametric and higher order spectral analysis signal transformation and compression techniques, including time-frequency analysis system modelling and adaptive identification techniques machine learning based approaches to signal processing Bayesian methods for signal processing, including Monte-Carlo Markov-chain and particle filtering techniques theory and application of blind and semi-blind signal separation techniques signal processing techniques for analysis, enhancement, coding, synthesis and recognition of speech signals direction-finding and beamforming techniques for audio and electromagnetic signals analysis techniques for biomedical signals baseband signal processing techniques for transmission and reception of communication signals signal processing techniques for data hiding and audio watermarking sparse signal processing and compressive sensing Special Issue Call for Papers: Intelligent Deep Fuzzy Model for Signal Processing - https://digital-library.theiet.org/files/IET_SPR_CFP_IDFMSP.pdf
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