A Novel Multiple-Model Adaptive Kalman Filter for an Unknown Measurement Loss Probability
- 10 September 2020
- journal article
- research article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Instrumentation and Measurement
- Vol. 70 (00189456), 1-11
- https://doi.org/10.1109/tim.2020.3023213
Abstract
This paper 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 IMMAKF 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.Keywords
Funding Information
- Unmanned Aircraft System (UAS) Traffic Management System Design and Implementation in Low Altitude through Ministry of Land, Infrastructure and Transport (MOLIT) of the Korean Government (20USTR-B127901-04)
- project Development of A.I. Based Recognition, Judgement and Control Solution for Autonomous Vehicle Corresponding to Atypical Driving Environment (2019-0-00399)
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