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Variational Bayesian-Based Improved Maximum Mixture Correntropy Kalman Filter for Non-Gaussian Noise.

Xuyou Li1, Yanda Guo1, Qingwen Meng1

  • 1Department of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China.

Entropy (Basel, Switzerland)
|January 21, 2022
PubMed
Summary

The improved maximum mixture correntropy Kalman filter (IMMCKF) enhances robustness in non-Gaussian filtering. This new method effectively handles non-stationary noise, improving accuracy over existing maximum correntropy Kalman filters.

Keywords:
Kalman filtermaximum correntropy criterionmixture correntropyvariational Bayesian inference

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Area of Science:

  • Signal Processing
  • State Estimation
  • Robust Filtering

Background:

  • The maximum correntropy Kalman filter (MCKF) offers robustness against non-Gaussian noise in linear systems.
  • Constant kernel bandwidth in MCKF can degrade performance with non-stationary noises.

Purpose of the Study:

  • To address the limitations of MCKF in non-stationary noise environments.
  • To propose an improved maximum mixture correntropy Kalman filter (IMMCKF) for enhanced applicability.

Main Methods:

  • Developed a new hierarchical Gaussian state-space model using Beta-Bernoulli distributed intermediate parameters.
  • Employed a variational Bayesian approach for inferring unknown mixing probabilities and state estimation vectors.

Main Results:

  • The proposed IMMCKF demonstrates significant performance improvements in non-stationary noise scenarios.
  • The variational Bayesian inference effectively estimates parameters for improved filtering.

Conclusions:

  • The IMMCKF provides a robust solution for filtering problems with non-stationary noises.
  • This approach enhances the practical applicability of correntropy-based Kalman filtering techniques.