A new likelihood approach to autonomous multiple model estimation

Söken H. E. , Sakai S.

ISA TRANSACTIONS, vol.99, pp.50-58, 2020 (Journal Indexed in SCI) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 99
  • Publication Date: 2020
  • Doi Number: 10.1016/j.isatra.2019.09.005
  • Title of Journal : ISA TRANSACTIONS
  • Page Numbers: pp.50-58
  • Keywords: Hybrid systems, Parameter estimation, Multiple model estimation, Kalman filter, TARGET TRACKING, STATE, OPTIMIZATION, FILTER


This paper presents an autonomous multiple model (AMM) estimation algorithm for hybrid systems with sudden changes in their parameters. Estimates of Kalman filters (KFs) that are tuned and employed for different system modes are merged based on a newly defined likelihood function without any necessity for filter interaction. The proposed likelihood function is composed of two measures, the filter agility measure and the steady-state error measure. These measures are derived based on filter adaptation rules. The numerical results show that the proposed algorithm, so called Competing AMM (CAMM), guarantees both steady-state estimation accuracy and quick parameter tracking. (C) 2019 ISA. Published by Elsevier Ltd. All rights reserved.