Adaptive filtering method and system based on generalized maximum asymmetric correlation entropy criterion

Abstract

The invention discloses a self-adaptive filtering method and system based on a generalized maximum asymmetric correlation entropy criterion, and the method comprises the steps: 1, carrying out the modeling of an input time sequence signal based on an autoregression model, enabling the input signal to pass through a filter, obtaining an output signal, taking the output signal as a predicted value of the signal at the moment, and carrying out the calculation of the predicted value; obtaining a prediction error of the autoregression model according to the prediction value; 2, according to the prediction error of the autoregression model, constructing an objective function based on a generalized maximum asymmetric correlation entropy criterion, and calculating the loss corresponding to the prediction error; 3, according to the prediction error loss, a gradient descent method is adopted to obtain a filter parameter updating expression, and the filter parameters are updated in real time; 4, the robustness of the adaptive filter under the asymmetric and non-Gaussian noise is analyzed; and 5, carrying out steady-state performance analysis and verification on the adaptive filter. According to the method, the robustness, the performance and the adaptive capacity of the adaptive filter under the non-Gaussian noise can be effectively improved.

Description

technical field [0001] The invention belongs to the field of signal processing, and in particular relates to an adaptive filtering method and system based on the generalized maximum asymmetric correlation entropy criterion. Background technique [0002] Traditional adaptive filtering establishes a cost function based on the Minimum Mean Square Error (MMSE) criterion, which can give the optimal filtering solution when the system noise obeys a Gaussian distribution. However, when the system noise contains impulsive components, the performance of the MMSE criterion will be seriously degraded. In order to solve this problem and reduce the impact of non-Gaussian noise, inspired by information learning theory, the Maximum Correntropy Criterion (MCC) and its extensions have been widely studied and considered to be an effective method for dealing with non-Gaussian system noise . [0003] The above criteria are based on a symmetric Gaussian kernel, which is suitable for dealing wit...

AI Technical Summary

Problems solved by technology

However, in many fields of data analysis and signal processing, such as insurance analysis, financial analysis, image processing, etc., there are asymmetric signals or noise

In an asymmetric noise environment, the estimation error follows a skewed distribution, so the criterion based on a symmetric Gaussian kernel no longer applies

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