A Rao-Blackwellized Particle Filter With Variational Inference for State Estimation With Measurement Model Uncertainties
This paper develops a Rao-Blackwellized particle filter with variational inference for jointly estimating state and time-varying parameters in non-linear state-space models (SSM) with non-Gaussian measurement noise. Depending on the availability of the conjugate prior for the unknown parameters, the...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9042224/ |
| _version_ | 1818445539337830400 |
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| author | Cheng Cheng Jean-Yves Tourneret Xiaodong Lu |
| author_facet | Cheng Cheng Jean-Yves Tourneret Xiaodong Lu |
| author_sort | Cheng Cheng |
| collection | DOAJ |
| description | This paper develops a Rao-Blackwellized particle filter with variational inference for jointly estimating state and time-varying parameters in non-linear state-space models (SSM) with non-Gaussian measurement noise. Depending on the availability of the conjugate prior for the unknown parameters, the joint posterior distribution of the state and unknown parameters is approximated by using an auxiliary particle filter with a probabilistic changepoint model. The distribution of the SSM parameters conditionally on each particle is then updated by using variational Bayesian inference. Experiments are first conducted on a modified nonlinear benchmark model to compare the performance of the proposed approach with other state-of-the-art approaches. Finally, in the context of GNSS multipath mitigation, the proposed approach is evaluated based on data obtained from a measurement campaign conducted in a street urban canyon. |
| first_indexed | 2024-12-14T19:33:26Z |
| format | Article |
| id | doaj.art-f6598184ef13417a891fef5028fe5c52 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T19:33:26Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-f6598184ef13417a891fef5028fe5c522022-12-21T22:50:00ZengIEEEIEEE Access2169-35362020-01-018556655567510.1109/ACCESS.2020.29819489042224A Rao-Blackwellized Particle Filter With Variational Inference for State Estimation With Measurement Model UncertaintiesCheng Cheng0https://orcid.org/0000-0002-3726-657XJean-Yves Tourneret1Xiaodong Lu2School of Astronautics, Northwestern Polytechnical University, Xi’an, ChinaENSEEIHT-IRIT-T??SA, University of Toulouse, Toulouse, FranceSchool of Astronautics, Northwestern Polytechnical University, Xi’an, ChinaThis paper develops a Rao-Blackwellized particle filter with variational inference for jointly estimating state and time-varying parameters in non-linear state-space models (SSM) with non-Gaussian measurement noise. Depending on the availability of the conjugate prior for the unknown parameters, the joint posterior distribution of the state and unknown parameters is approximated by using an auxiliary particle filter with a probabilistic changepoint model. The distribution of the SSM parameters conditionally on each particle is then updated by using variational Bayesian inference. Experiments are first conducted on a modified nonlinear benchmark model to compare the performance of the proposed approach with other state-of-the-art approaches. Finally, in the context of GNSS multipath mitigation, the proposed approach is evaluated based on data obtained from a measurement campaign conducted in a street urban canyon.https://ieeexplore.ieee.org/document/9042224/Joint state and parameter estimationRao-blackwellized particle filterstate-space modelsvariational inference |
| spellingShingle | Cheng Cheng Jean-Yves Tourneret Xiaodong Lu A Rao-Blackwellized Particle Filter With Variational Inference for State Estimation With Measurement Model Uncertainties IEEE Access Joint state and parameter estimation Rao-blackwellized particle filter state-space models variational inference |
| title | A Rao-Blackwellized Particle Filter With Variational Inference for State Estimation With Measurement Model Uncertainties |
| title_full | A Rao-Blackwellized Particle Filter With Variational Inference for State Estimation With Measurement Model Uncertainties |
| title_fullStr | A Rao-Blackwellized Particle Filter With Variational Inference for State Estimation With Measurement Model Uncertainties |
| title_full_unstemmed | A Rao-Blackwellized Particle Filter With Variational Inference for State Estimation With Measurement Model Uncertainties |
| title_short | A Rao-Blackwellized Particle Filter With Variational Inference for State Estimation With Measurement Model Uncertainties |
| title_sort | rao blackwellized particle filter with variational inference for state estimation with measurement model uncertainties |
| topic | Joint state and parameter estimation Rao-blackwellized particle filter state-space models variational inference |
| url | https://ieeexplore.ieee.org/document/9042224/ |
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