Design method for convex combination self-adapting filter based on minimum error entropy

Abstract

The invention discloses a design method for a convex combination self-adapting filter based on minimum error entropy, and belongs to the field of treatment of signals. The design of the traditional self-adapting filter based on the minimum mean square error rule only can achieve good effect in the case that system noise is Gaussian, but is non-ideal in performance in the case of non-Gaussian noise. Although the self-adapting principle based on minimum error entropy can overcome the defect of non-ideal performance in the case of non-Gaussian noise, but still can not simultaneously achieve optimal effects on convergence rate and steady accuracy as facing a balancing problem therebetween. According to the design method for the convex combination self-adapting filter based on minimum error entropy, the two self-adapting filters based on minimum error entropy are combined, so that the designed convex combination self-adapting filter based on minimum error entropy still can implement abilities of rapid converging and tracking in the case of keeping good steady accuracy, and is more prone to popularize and use in practical application.

Description

Technical field [0001] The invention belongs to method research in the field of signal processing, and relates to a convex combination filter design based on a minimum error entropy algorithm criterion. Background technique [0002] In recent years, adaptive filtering has rapidly developed as an optimal filtering method. Adaptive filtering is a filtering method developed on the basis of linear filtering such as Wiener filtering and Kalman filtering. Because of its stronger adaptability and better filtering performance, it has been widely used in engineering practice, especially in information processing technology. [0003] The research object of adaptive filtering is a system or information process with uncertainty. The "uncertainty" here means that the mathematical model of the information processing process and its environment under study is not completely certain. It contains some unknown factors and random factors. [0004] Any actual information process has varying degrees ...

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Problems solved by technology

However, when the step size parameter is selected to ensure a higher convergence speed, a lower steady-state accuracy cannot be obtained; on the contrary, when a lower steady-state accuracy is guaranteed, a faster convergence speed cannot be achieved

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