Maximum correntropy criterion variational Bayesian adaptive Kalman filter based on strong tracking with unknown noise covariances |
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| Institution: | 1. College of Marine Electrical Engineering, Dalian Maritime University, Dalian 116026, Liaoning, China;2. Key Laboratory of Technology and System for Intelligent Ships of Liaoning Province, Dalian 116026, Liaoning, China;1. Department of Automation, Shanghai Jiao Tong University, Shanghai, China;2. Department of Information Engineering, University of Florence, Florence, Italy;3. Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, China;1. College of Information Science and Technology, Donghua University, Shanghai 201620, China;2. Department of Electronic Engineering, Jiangsu University, Zhenjiang 212013, China;3. College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China;4. Department of Electrical Engineering, City University of Hong Kong, Hong Kong, China |
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| Abstract: | The performance of the current state estimation will degrade in the existence of slow-varying noise statistics. To solve the aforementioned issues, an improved strong tracking maximum correntropy criterion variational-Bayesian adaptive Kalman filter is presented in this paper. First of all, the inverse-Wishart distribution, as the conjugate-prior, is adopted to model the unknown and time-varying measurement and process noise covariances, then the noise covariances and system state are estimated via the variational Bayesian method. Secondly, the multiple fading-factors are obtained and evaluated to modify the prediction error covariance matrix to address the problems associated with inaccurate error estimation. Finally, the maximum correntropy criterion is employed to correct the filtering gain, which improves the filtering performance of the proposed algorithm. Simulation results show that the proposed filter exhibits better accuracy and convergence performance compared to other existing algorithms. |
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