Variable structure multiple model fixed-interval smoothing

• Published in: Chinese journal of aeronautics Vol. 36; no. 2; pp. 139 - 148
• Main Authors: ZHANG, Bolun, GAO, Yongxin, DUAN, Zhansheng
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.02.2023; Faculty of Electronic and Information Engineering,School of Automation Science and Engineering,Xi'an Jiaotong University,Xi'an 710049,China
• Subjects: Fixed-interval smoothing; Model-set adaptation; Multiple model estimation; Smoothing algorithm; Variable structure; Model-set adaptation; Variable structure; Multiple model estimation; Fixed-interval smoothing; Smoothing algorithm
• ISSN: 1000-9361; 2588-9230
• DOI: 10.1016/j.cja.2022.04.006

This paper focuses on fixed-interval smoothing for stochastic hybrid systems. When the truth-mode mismatch is encountered, existing smoothing methods based on fixed structure of model-set have significant performance degradation and are inapplicable. We develop a fixed-interval smoothing method based on forward- and backward-filtering in the Variable Structure Multiple Model (VSMM) framework in this paper. We propose to use the Simplified Equivalent model Interacting Multiple Model (SEIMM) in the forward and the backward filters to handle the difficulty of different mode-sets used in both filters, and design a re-filtering procedure in the model-switching stage to enhance the estimation performance. To improve the computational efficiency, we make the basic model-set adaptive by the Likely-Model Set (LMS) algorithm. It turns out that the smoothing performance is further improved by the LMS due to less competition among models. Simulation results are provided to demonstrate the better performance and the computational efficiency of our proposed smoothing algorithms.