大连理工大学电子信息与电气工程学部 大连 116024
基金项目:国家自然科学基金(61671105, 61172108, 61139001, 81241059)
详细信息
作者简介:邱天爽：男，1954年生，教授，博士生导师，主要研究方向为非高斯、非平稳统计信号处理
通讯作者:邱天爽 qiutsh@dlut.edu.cn
中图分类号:TN911.7计量
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被引次数:0
出版历程
收稿日期:2019-08-28
修回日期:2019-11-05
网络出版日期:2019-11-12
刊出日期:2020-01-21
Development in Signal Processing Based on Correntropy and Cyclic Correntropy
Tianshuang QIU,Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024, China
Funds:The National Natural Science Foundation of China (61671105, 61172108, 61139001, 81241059)
摘要
摘要:在无线电监测和目标定位等应用中，接收信号经常会受到脉冲噪声和同频带干扰等复杂电磁环境的影响，传统的基于2阶统计量的信号处理方法往往不能正常工作，基于分数低阶统计量的信号处理方法也由于对信号噪声统计先验知识的依赖性而遇到困难。近年来提出并受到信号处理领域普遍关注的相关熵和循环相关熵信号处理理论与方法，是解决复杂电磁环境下信号分析处理、参数估计、目标定位和其他应用问题的有效技术手段，有力促进了非高斯、非平稳信号处理理论方法和应用的发展。该文系统性地综述了相关熵和循环相关熵信号处理的基本理论和基本方法，包括相关熵与循环相关熵的起源背景、定义概念、性质特点，以及所包含的数学物理意义。该文还介绍了相关熵与循环相关熵信号处理在多个领域的应用问题，希望对非高斯、非平稳统计信号处理的研究和应用有所裨益。
关键词:信号处理/
相关熵/
循环相关熵/
非高斯/
非平稳
Abstract:In radio monitoring and target location applications, the received signals are often affected by complex electromagnetic environment, such as impulsive noise and cochannel interference. Traditional signal processing methods based on second-order statistics often fail to work properly. The signal processing methods based on fractional lower order statistics also encounter difficulties due to their dependence on prior knowledge of signals and noises. In recent years, the theory and method of correntropy and cyclic correntropy signal processing, which are widely concerned in the field of signal processing, are put forward. They are effective technical means to solve the problems of signal analysis and processing, parameter estimation, target location and other applications to complex electromagnetic environment. They promote greatly the development of the theory and application of non-Gaussian and non-stationary signal processing. This paper reviews systematically the basic theory and methods of correntropy and cyclic correntropy signal processing, including the background, definition, properties and characteristics of correntropy and cyclic correntropy, as well as their mathematical and physical meanings. This paper introduces also the applications of correntropy and cyclic correntropy signal processing to many fields, hoping to benefit the research and application of non-Gaussian and non-stationary statistical signal processing.
Key words:Signal processing/
Correntropy/
Cyclic correntropy/
Non-Gaussian/
Non-stationary
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