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The Joint Adaptive Kalman Filter (JAKF) for Vehicle Motion State Estimation
This paper proposes a multi-sensory Joint Adaptive Kalman Filter (JAKF) through extending innovation-based adaptive estimation (IAE) to estimate the motion state of the moving vehicles ahead. JAKF views Lidar and Radar data as the source of the local filters, which aims to adaptively adjust the meas...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4970148/ https://www.ncbi.nlm.nih.gov/pubmed/27438835 http://dx.doi.org/10.3390/s16071103 |
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author | Gao, Siwei Liu, Yanheng Wang, Jian Deng, Weiwen Oh, Heekuck |
author_facet | Gao, Siwei Liu, Yanheng Wang, Jian Deng, Weiwen Oh, Heekuck |
author_sort | Gao, Siwei |
collection | PubMed |
description | This paper proposes a multi-sensory Joint Adaptive Kalman Filter (JAKF) through extending innovation-based adaptive estimation (IAE) to estimate the motion state of the moving vehicles ahead. JAKF views Lidar and Radar data as the source of the local filters, which aims to adaptively adjust the measurement noise variance-covariance (V-C) matrix ‘R’ and the system noise V-C matrix ‘Q’. Then, the global filter uses R to calculate the information allocation factor ‘β’ for data fusion. Finally, the global filter completes optimal data fusion and feeds back to the local filters to improve the measurement accuracy of the local filters. Extensive simulation and experimental results show that the JAKF has better adaptive ability and fault tolerance. JAKF enables one to bridge the gap of the accuracy difference of various sensors to improve the integral filtering effectivity. If any sensor breaks down, the filtered results of JAKF still can maintain a stable convergence rate. Moreover, the JAKF outperforms the conventional Kalman filter (CKF) and the innovation-based adaptive Kalman filter (IAKF) with respect to the accuracy of displacement, velocity, and acceleration, respectively. |
format | Online Article Text |
id | pubmed-4970148 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-49701482016-08-04 The Joint Adaptive Kalman Filter (JAKF) for Vehicle Motion State Estimation Gao, Siwei Liu, Yanheng Wang, Jian Deng, Weiwen Oh, Heekuck Sensors (Basel) Article This paper proposes a multi-sensory Joint Adaptive Kalman Filter (JAKF) through extending innovation-based adaptive estimation (IAE) to estimate the motion state of the moving vehicles ahead. JAKF views Lidar and Radar data as the source of the local filters, which aims to adaptively adjust the measurement noise variance-covariance (V-C) matrix ‘R’ and the system noise V-C matrix ‘Q’. Then, the global filter uses R to calculate the information allocation factor ‘β’ for data fusion. Finally, the global filter completes optimal data fusion and feeds back to the local filters to improve the measurement accuracy of the local filters. Extensive simulation and experimental results show that the JAKF has better adaptive ability and fault tolerance. JAKF enables one to bridge the gap of the accuracy difference of various sensors to improve the integral filtering effectivity. If any sensor breaks down, the filtered results of JAKF still can maintain a stable convergence rate. Moreover, the JAKF outperforms the conventional Kalman filter (CKF) and the innovation-based adaptive Kalman filter (IAKF) with respect to the accuracy of displacement, velocity, and acceleration, respectively. MDPI 2016-07-16 /pmc/articles/PMC4970148/ /pubmed/27438835 http://dx.doi.org/10.3390/s16071103 Text en © 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Gao, Siwei Liu, Yanheng Wang, Jian Deng, Weiwen Oh, Heekuck The Joint Adaptive Kalman Filter (JAKF) for Vehicle Motion State Estimation |
title | The Joint Adaptive Kalman Filter (JAKF) for Vehicle Motion State Estimation |
title_full | The Joint Adaptive Kalman Filter (JAKF) for Vehicle Motion State Estimation |
title_fullStr | The Joint Adaptive Kalman Filter (JAKF) for Vehicle Motion State Estimation |
title_full_unstemmed | The Joint Adaptive Kalman Filter (JAKF) for Vehicle Motion State Estimation |
title_short | The Joint Adaptive Kalman Filter (JAKF) for Vehicle Motion State Estimation |
title_sort | joint adaptive kalman filter (jakf) for vehicle motion state estimation |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4970148/ https://www.ncbi.nlm.nih.gov/pubmed/27438835 http://dx.doi.org/10.3390/s16071103 |
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