Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 오희국 | - |
dc.date.accessioned | 2018-04-13T04:25:52Z | - |
dc.date.available | 2018-04-13T04:25:52Z | - |
dc.date.issued | 2016-07 | - |
dc.identifier.citation | SENSORS, v. 16, No. 7, Article no. 1103 | en_US |
dc.identifier.issn | 1424-8220 | - |
dc.identifier.uri | http://www.mdpi.com/1424-8220/16/7/1103/htm | - |
dc.identifier.uri | http://hdl.handle.net/20.500.11754/65825 | - |
dc.description.abstract | 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 'beta' 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. | en_US |
dc.description.sponsorship | This work was supported by National Nature Science Foundation (61202472, 61373123, 61572229, U1564211); Scientific Research Foundation for Returned Scholars; International Scholar Exchange Fellowship (ISEF) program of Korea Foundation for Advanced Studies (KFAS); Jilin University Young Teacher and Student Cross Discipline Foundation (JCKY-QKJC09); and Jilin Provincial International Cooperation Foundation (20140414008GH, 20150414004GH). | en_US |
dc.language.iso | en_US | en_US |
dc.publisher | MDPI AG | en_US |
dc.subject | Joint Kalman Filter | en_US |
dc.subject | innovation-based adaptive estimation | en_US |
dc.subject | motion state estimation | en_US |
dc.subject | data fusion | en_US |
dc.subject | CAMERA CALIBRATION | en_US |
dc.subject | TRACKING | en_US |
dc.title | The Joint Adaptive Kalman Filter (JAKF) for Vehicle Motion State Estimation | en_US |
dc.type | Article | en_US |
dc.relation.no | 7 | - |
dc.relation.volume | 16 | - |
dc.identifier.doi | 10.3390/s16071103 | - |
dc.relation.page | 1103-1131 | - |
dc.relation.journal | SENSORS | - |
dc.contributor.googleauthor | Gao, SW | - |
dc.contributor.googleauthor | Liu, YH | - |
dc.contributor.googleauthor | Wang, J | - |
dc.contributor.googleauthor | Deng, WW | - |
dc.contributor.googleauthor | Oh, HK | - |
dc.relation.code | 2016007221 | - |
dc.sector.campus | E | - |
dc.sector.daehak | COLLEGE OF COMPUTING[E] | - |
dc.sector.department | DIVISION OF COMPUTER SCIENCE | - |
dc.identifier.pid | hkoh | - |
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