Variational Adaptive Kalman Filter With Gaussian-Inverse-Wishart Mixture Distribution

• 成果类型: Article
• 署名作者: Huang, Yulong; Zhang, Yonggang; Shi, Peng; Chambers, Jonathon
• 署名单位: Harbin Engineering University; University of Adelaide; University of Leicester
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2020.2995674
• 发表日期: 2021
• 页码: 1786-1793
• 关键词: Covariance matrices; Gaussian distribution; Probability density function; Kalman filters; Noise measurement; estimation; Bayes methods; Adaptive filter; Gaussian-inverse-Wishart mixture (GIWM) distribution; Kalman filter (KF); variational Bayesian (VB)

摘要

In this article, a new variational adaptive Kalman filter with Gaussian-inverse-Wishart mixture distribution is proposed for a class of linear systems with both partially unknown state and measurement noise covariance matrices. The state transition and measurement likelihood probability density functions are described by a Gaussian-inverse-Wishart mixture distribution and a Gaussian-inverse-Wishart distribution, respectively. The system state vector together with the state noise covariance matrix and the measurement noise covariance matrix are jointly estimated based on the derived hierarchical Gaussian model. Examples are provided to demonstrate the effectiveness and potential of the developed new filtering design techniques.