A Novel Outlier-Robust Kalman Filtering Framework Based on Statistical Similarity Measure

• 成果类型: Article
• 署名作者: Huang, Yulong; Zhang, Yonggang; Zhao, Yuxin; Shi, Peng; Chambers, Jonathon A.
• 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.2020.3011443
• 发表日期: 2021
• 页码: 2677-2692
• 关键词: Kalman filters; Robustness; Noise measurement; Covariance matrices; estimation; Pollution measurement; iterative methods; Heavy-tailed noise; Kalman filter; outliers; statistical similarity measure; separate iterative algorithm

摘要

In this article, a statistical similarity measure is introduced to quantify the similarity between two random vectors. The measure is, then, employed to develop a novel outlier-robust Kalman filtering framework. The approximation errors and the stability of the proposed filter are analyzed and discussed. To implement the filter, a fixed-point iterative algorithm and a separate iterative algorithm are given, and their local convergent conditions are also provided, and their comparisons have been made. In addition, selection of the similarity function is considered, and four exemplary similarity functions are established, from which the relations between our new method and existing outlier-robust Kalman filters are revealed. Simulation examples are used to illustrate the effectiveness and potential of the new filtering scheme.