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

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...

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Veröffentlicht in:	IEEE transactions on instrumentation and measurement 2021, Vol.70, p.1-11
Hauptverfasser:	Youn, Wonkeun, Ko, Nak Yong, Gadsden, Stephen Andrew, Myung, Hyun
Format:	Artikel
Sprache:	eng
Schlagworte:	Adaptive filters Algorithms Global Positioning System Inference algorithms Interacting multiple model Kalman filter (KF) Kalman filters localization Loss measurement measurement loss Measurement uncertainty Noise measurement Random variables Time measurement variational Bayesian (VB) inference
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description	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.
doi_str_mv	10.1109/TIM.2020.3023213
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subjects	Adaptive filters Algorithms Global Positioning System Inference algorithms Interacting multiple model Kalman filter (KF) Kalman filters localization Loss measurement measurement loss Measurement uncertainty Noise measurement Random variables Time measurement variational Bayesian (VB) inference
title	A Novel Multiple-Model Adaptive Kalman Filter for an Unknown Measurement Loss Probability
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