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Adaptive Maximum Correntropy Gaussian Filter Based on Variational Bayes.

Guoqing Wang1, Zhongxing Gao2, Yonggang Zhang3

  • 1College of Automation, Harbin Engineering University, Harbin 150001, China. wangguoqing2014@hrbeu.edu.cn.

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|June 20, 2018
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Summary
This summary is machine-generated.

This study introduces an improved Gaussian filter (GF) using maximum correntropy (MCC) and variational Bayes (VB) for robust state estimation. The novel approach enhances accuracy in systems with non-Gaussian noise, outperforming existing methods.

Keywords:
Gaussian filterKalman filtermaximum correntropy criterionvariational Bayes

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Area of Science:

  • Control Systems Engineering
  • Signal Processing
  • Statistical Inference

Background:

  • State estimation is crucial for dynamic systems but challenged by non-Gaussian measurement noise.
  • Accurate estimation of noise covariance is vital for filter performance.
  • Existing methods struggle with unknown covariance non-Gaussian noise.

Purpose of the Study:

  • To develop a robust state estimation algorithm for systems with unknown covariance non-Gaussian measurement noise.
  • To improve the accuracy and reliability of state estimation in challenging noise environments.
  • To provide a general framework applicable to both linear and nonlinear systems.

Main Methods:

  • Proposed an improved Gaussian filter (GF) incorporating the maximum correntropy criterion (MCC).
  • Utilized variational Bayes (VB) approximation for online estimation of measurement noise covariance.
  • Integrated MCC and VB via fixed-point iteration to refine noise covariance estimation.

Main Results:

  • The proposed algorithm demonstrated superior estimation accuracy compared to existing robust and adaptive filters.
  • Validated through a target tracking simulation and a real-world INS/DVL integrated navigation system.
  • The method effectively suppresses pollution from non-Gaussian measurement noise.

Conclusions:

  • The novel improved Gaussian filter offers enhanced state estimation performance under non-Gaussian noise conditions.
  • The integration of MCC and VB provides an effective approach for online noise covariance estimation.
  • The algorithm's general applicability and demonstrated effectiveness in practical scenarios highlight its significance.