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...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2016-07-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1424-8220/16/7/1103 |
| _version_ | 1811302455652122624 |
|---|---|
| author | Siwei Gao Yanheng Liu Jian Wang Weiwen Deng Heekuck Oh |
| author_facet | Siwei Gao Yanheng Liu Jian Wang Weiwen Deng Heekuck Oh |
| author_sort | Siwei Gao |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-04-13T07:28:16Z |
| format | Article |
| id | doaj.art-b4d8aacb33e74d7382bc282505affe2e |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-13T07:28:16Z |
| publishDate | 2016-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-b4d8aacb33e74d7382bc282505affe2e2022-12-22T02:56:25ZengMDPI AGSensors1424-82202016-07-01167110310.3390/s16071103s16071103The Joint Adaptive Kalman Filter (JAKF) for Vehicle Motion State EstimationSiwei Gao0Yanheng Liu1Jian Wang2Weiwen Deng3Heekuck Oh4College of Computer Science and Technology, Jilin University, Changchun 130012, ChinaCollege of Computer Science and Technology, Jilin University, Changchun 130012, ChinaCollege of Computer Science and Technology, Jilin University, Changchun 130012, ChinaState Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130012, ChinaDepartment of Computer Science and Engineering, Hanyang University, Ansan 426791, KoreaThis 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.http://www.mdpi.com/1424-8220/16/7/1103Joint Kalman Filterinnovation-based adaptive estimationmotion state estimationdata fusion |
| spellingShingle | Siwei Gao Yanheng Liu Jian Wang Weiwen Deng Heekuck Oh The Joint Adaptive Kalman Filter (JAKF) for Vehicle Motion State Estimation Sensors Joint Kalman Filter innovation-based adaptive estimation motion state estimation data fusion |
| 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 | Joint Kalman Filter innovation-based adaptive estimation motion state estimation data fusion |
| url | http://www.mdpi.com/1424-8220/16/7/1103 |
| work_keys_str_mv | AT siweigao thejointadaptivekalmanfilterjakfforvehiclemotionstateestimation AT yanhengliu thejointadaptivekalmanfilterjakfforvehiclemotionstateestimation AT jianwang thejointadaptivekalmanfilterjakfforvehiclemotionstateestimation AT weiwendeng thejointadaptivekalmanfilterjakfforvehiclemotionstateestimation AT heekuckoh thejointadaptivekalmanfilterjakfforvehiclemotionstateestimation AT siweigao jointadaptivekalmanfilterjakfforvehiclemotionstateestimation AT yanhengliu jointadaptivekalmanfilterjakfforvehiclemotionstateestimation AT jianwang jointadaptivekalmanfilterjakfforvehiclemotionstateestimation AT weiwendeng jointadaptivekalmanfilterjakfforvehiclemotionstateestimation AT heekuckoh jointadaptivekalmanfilterjakfforvehiclemotionstateestimation |