A Novel Multiple-Model Adaptive Kalman Filter for an Unknown Measurement Loss Probability

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This article proposes a novel adaptive Kalman filter (AKF) to estimate the unknown probability of measurement loss using the interacting multiple-model (IMM) filtering framework, yielding the IMM-AKF algorithm. In the proposed IMM-AKF algorithm, the state, Bernoulli random variable, and measurement loss probability are jointly inferred based on the variational Bayesian (VB) approach. In particular, a new likelihood definition is derived for the mode probability update process of the IMM-AKF algorithm. Experiments demonstrate the superiority of the proposed IMM-AKF algorithm over existing filtering algorithms by adaptively estimating the unknown time-varying measurement loss probability.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2021-01
Language
English
Article Type
Article
Citation

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, v.70

ISSN
0018-9456
DOI
10.1109/TIM.2020.3023213
URI
http://hdl.handle.net/10203/279912
Appears in Collection
EE-Journal Papers(저널논문)
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