非高斯噪声下稀疏安德鲁正弦范数提升自适应算法

2022 IEEE 5th International Conference on Electronic Information and Communication Technology (ICEICT) Pub Date : 2022-08-21 DOI:10.1109/ICEICT55736.2022.9908826

Abdul Hadi, Xinqi Huang, Burhan Ali, Yingsong Li

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引用次数: 2

摘要

为了提高识别稀疏系统的性能，提出了两种基于Andrew正弦估计(ASE)的稀疏自适应滤波(AF)算法，其中ASE是在最小二乘框架内导出的。在此基础上，采用零吸引(ZA)方案构建ZA-ASE及其重加权形式(RZA-ASE)来对抗非高斯噪声，并利用系统的稀疏特性。通过仿真研究了该算法的性能，并与最小均方算法(LMS)和最大熵准则算法(MCC)进行了比较，证明了其优越的性能。

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Sparse Andrew's Sine Norm Promoting Adaptive Algorithm under Non-Gaussian Noises

Two sparse adaptive filtering (AF) algorithms based on Andrew's sine estimator (ASE) are presented to achieve improved performance for identifying sparse systems, where the ASE is derived within the least-square framework. Furthermore, zero-attracting (ZA) scheme is used in ASE to construct ZA-ASE and its re-weighting form (RZA-ASE) to combat non-Gaussian noises and use the sparse characteristics of the system. Their performance is investigated via simulations and compared with the least-mean square (LMS) and the maximum correntropy criterion (MCC) algorithms to show their superior performance.

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2022 IEEE 5th International Conference on Electronic Information and Communication Technology (ICEICT)

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