An Adaptive Kalman Filter With Inaccurate Noise Covariances in the Presence of Outliers

• 成果类型: Article
• 署名作者: Zhu, Hao; Zhang, Guorui; Li, Yongfu; Leung, Henry
• 署名单位: Chongqing University of Posts & Telecommunications; University of Calgary
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2021.3056343
• 发表日期: 2022
• 页码: 374-381
• 关键词: Kalman filters; Noise measurement; computational modeling; Bayes methods; Adaptation models; Probability density function; gamma distribution; Adaptive Kalman filter (KF); Gaussian-Gamma mixture (GGM) distribution; outliers; variational Bayesian (VB)

摘要

In this article, a novel variational Bayesian (VB) adaptive Kalman filter with inaccurate nominal process and measurement noise covariances (PMNC) in the presence of outliers is proposed. The probability density functions of state transition and measurement likelihood are modeled as Gaussian-Gamma mixture distributions. The VB inference is used to perform the state and PMNC simultaneously. Simulations show that the effectiveness of the proposed method with inaccurate noise covariances in the presence of outliers environments.