A new likelihood approach to autonomous multiple model estimation

Date

2020-04-01

Author

Söken, Halil Ersin

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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.

Subject Keywords

Instrumentation, Electrical and Electronic Engineering, Applied Mathematics, Computer Science Applications

URI

https://hdl.handle.net/11511/69729

Journal

ISA TRANSACTIONS

DOI

https://doi.org/10.1016/j.isatra.2019.09.005

Collections

Department of Aerospace Engineering, Article

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Citation Formats

H. E. Söken, “A new likelihood approach to autonomous multiple model estimation,” ISA TRANSACTIONS, pp. 50–58, 2020, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/69729.