Information Perception Adaptive Filtering Algorithm Sensitive to Signal Statistics: Theory and Design

Shiwei Yun, Sihai Guan (), Yong Zhao, Qiang Xiang, Chuanwu Zhang and Bharat Biswal
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Shiwei Yun: College of Electronic and Information, Southwest Minzu University, Chengdu 610041, China
Sihai Guan: College of Electronic and Information, Southwest Minzu University, Chengdu 610041, China
Yong Zhao: School of Mechanical and Power Engineering, Henan Polytechnic University, Jiaozuo 454003, China
Qiang Xiang: College of Electronic and Information, Southwest Minzu University, Chengdu 610041, China
Chuanwu Zhang: College of Electronic and Information, Southwest Minzu University, Chengdu 610041, China
Bharat Biswal: Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ 07102, USA

Mathematics, 2025, vol. 13, issue 20, 1-14

Abstract: To address the challenges of information perception, this paper proposes a novel adaptive filtering algorithm. The algorithm is built upon an asymmetric cost function. It incorporates an information perception strategy, referred to as the information perception adaptive filtering (IPAF) algorithm, in which the parameters of the cost function are directly linked to statistical characteristics. The key advantage of this algorithm is that its parameters can be adaptively adjusted in real-time according to higher-order statistical properties in different environments, thereby overcoming the limitations of traditional fixed-parameter algorithms. A comprehensive performance analysis of the IPAF algorithm is presented, including convergence analysis, mean square deviation analysis, and computational complexity analysis. Extensive simulation experiments and evaluations of real datasets demonstrate that the IPAF algorithm achieves reliable information perception with excellent robustness.

Keywords: adaptive estimation; information perception; input and interference; performance analysis (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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