Rao-Blackwellized Particle Filter using Noise Adaptive Kalman Filter for Fully Mixing State-Space Models
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A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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Date
2024
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Language
en
Pages
11
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IEEE Transactions on Aerospace and Electronic Systems, Volume 60, issue 5, pp. 6972-6982
Abstract
This article proposes a Rao-Blackwellized particle filter (RBPF) for fully mixing state-space models that replace the Kalman filter within the RBPF method with a noise-adaptive Kalman filter. This extension aims to deal with unknown time-varying measurement variances. Consequently, a variational Bayesian (VB) adaptive Kalman filter estimates the conditionally linear states and the measurement noise variances, whereas the nonlinear (or latent) states are handled by sequential Monte Carlo sampling. Thus, by modifying the underlying mathematical framework of RBPF, we construct the Monte Carlo variational Bayesian (MCVB) filter. A stopping criterion for VB approximations is proposed by employing Tikhonov regularization. In addition, an analysis of the numerical stability of the proposed filtering mechanism is presented. The performance of the MCVB filter is illustrated in simulations and mobile robot tracking experiments in the presence of measurement model uncertainties.Description
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Keywords
Adaptation models, Adaptive kalman filter, Bayes methods, Kalman filters, Monte Carlo methods, Noise, State-space methods, Vectors, monte carlo methods, particle filter, rao–blackwellization, tikhonov regularization, tracking, variational bayesian methods, Tikhonov regularization, Rao-Blackwellization, Adaptive Kalman filter, Monte Carlo (MC) methods, variational Bayesian (VB) methods
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Citation
Badar, T, Sarkka, S, Zhao, Z & Visala, A 2024, ' Rao-Blackwellized Particle Filter using Noise Adaptive Kalman Filter for Fully Mixing State-Space Models ', IEEE Transactions on Aerospace and Electronic Systems, vol. 60, no. 5, pp. 6972-6982 . https://doi.org/10.1109/TAES.2024.3409644