DOA Estimation Based on Logistic Function for CD Sources in Impulsive Noise

IF 1.1 4区工程技术 Q4 ENGINEERING, ELECTRICAL & ELECTRONIC IET Signal Processing Pub Date : 2024-07-05 DOI:10.1049/2024/7043115

Quan Tian, Ruiyan Cai, Yang Luo

引用次数: 0

Abstract

To improve direction of arrival (DOA) estimation for coherently distributed sources under impulsive noise environments, a logistic-based adaptive factor is proposed to suppress the impulsive noise contained in the output signals of the array. The properties of this adaptive factor are derived. Furthermore, this adaptive factor is applied to subspace methods, and a novel DOA estimation algorithm is proposed. This novel algorithm ensures the boundedness of the signal and the noise subspaces while improving the DOA estimation accuracy and robustness. The experimental results demonstrate that the proposed algorithm outperforms existing algorithms in terms of resolution probability and estimation accuracy under impulsive noise environments.

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基于 Logistic 函数的脉冲噪声中 CD 声源的 DOA 估算

为了改进脉冲噪声环境下相干分布源的到达方向（DOA）估计，提出了一种基于逻辑的自适应因子，以抑制阵列输出信号中包含的脉冲噪声。该自适应因子的特性已被推导出来。此外，还将该自适应因子应用于子空间方法，并提出了一种新型 DOA 估计算法。这种新型算法确保了信号和噪声子空间的有界性，同时提高了 DOA 估计精度和鲁棒性。实验结果表明，在脉冲噪声环境下，所提出的算法在分辨概率和估计精度方面优于现有算法。

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来源期刊

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

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