A weighted likelihood criteria for learning importance densities in particle filtering
Selecting an optimal importance density and ensuring optimal particle weights are central challenges in particle-based filtering. In this paper, we provide a two-step procedure to learn importance densities for particle-based filtering. The first stage importance density is constructed based on ense...
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Springer International Publishing
2018
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my.utp.eprints.207402019-02-26T02:22:43Z A weighted likelihood criteria for learning importance densities in particle filtering Javvad ur Rehman, M. Dass, S.C. Asirvadam, V.S. Selecting an optimal importance density and ensuring optimal particle weights are central challenges in particle-based filtering. In this paper, we provide a two-step procedure to learn importance densities for particle-based filtering. The first stage importance density is constructed based on ensemble Kalman filter kernels. This is followed by learning a second stage importance density via weighted likelihood criteria. The importance density is learned by fitting Gaussian mixture models to a set of particles and weights. The weighted likelihood learning criteria ensure that the second stage importance density is closer to the true filtered density, thereby improving the particle filtering procedure. Particle weights recalculated based on the latter density are shown to mitigate particle weight degeneracy as the filtering procedure propagates in time. We illustrate the proposed methodology on 2D and 3D nonlinear dynamical systems. © 2018, The Author(s). Springer International Publishing 2018 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85048610027&doi=10.1186%2fs13634-018-0557-5&partnerID=40&md5=dc6f02a8759dd89aeac0e498b0e7fcf3 Javvad ur Rehman, M. and Dass, S.C. and Asirvadam, V.S. (2018) A weighted likelihood criteria for learning importance densities in particle filtering. Eurasip Journal on Advances in Signal Processing, 2018 (1). http://eprints.utp.edu.my/20740/ |
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Selecting an optimal importance density and ensuring optimal particle weights are central challenges in particle-based filtering. In this paper, we provide a two-step procedure to learn importance densities for particle-based filtering. The first stage importance density is constructed based on ensemble Kalman filter kernels. This is followed by learning a second stage importance density via weighted likelihood criteria. The importance density is learned by fitting Gaussian mixture models to a set of particles and weights. The weighted likelihood learning criteria ensure that the second stage importance density is closer to the true filtered density, thereby improving the particle filtering procedure. Particle weights recalculated based on the latter density are shown to mitigate particle weight degeneracy as the filtering procedure propagates in time. We illustrate the proposed methodology on 2D and 3D nonlinear dynamical systems. © 2018, The Author(s). |
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Article |
author |
Javvad ur Rehman, M. Dass, S.C. Asirvadam, V.S. |
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Javvad ur Rehman, M. Dass, S.C. Asirvadam, V.S. A weighted likelihood criteria for learning importance densities in particle filtering |
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Javvad ur Rehman, M. Dass, S.C. Asirvadam, V.S. |
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Javvad ur Rehman, M. |
title |
A weighted likelihood criteria for learning importance densities in particle filtering |
title_short |
A weighted likelihood criteria for learning importance densities in particle filtering |
title_full |
A weighted likelihood criteria for learning importance densities in particle filtering |
title_fullStr |
A weighted likelihood criteria for learning importance densities in particle filtering |
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A weighted likelihood criteria for learning importance densities in particle filtering |
title_sort |
weighted likelihood criteria for learning importance densities in particle filtering |
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Springer International Publishing |
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2018 |
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https://www.scopus.com/inward/record.uri?eid=2-s2.0-85048610027&doi=10.1186%2fs13634-018-0557-5&partnerID=40&md5=dc6f02a8759dd89aeac0e498b0e7fcf3 http://eprints.utp.edu.my/20740/ |
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