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Optical Scatter Microscopy Based on Two-Dimensional Gabor Filters
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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
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.
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.
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