The Joint Adaptive Kalman Filter (JAKF) for Vehicle Motion State Estimation

Title
The Joint Adaptive Kalman Filter (JAKF) for Vehicle Motion State Estimation
Author
오희국
Keywords
Joint Kalman Filter; innovation-based adaptive estimation; motion state estimation; data fusion; CAMERA CALIBRATION; TRACKING
Issue Date
2016-07
Publisher
MDPI AG
Citation
SENSORS, v. 16, No. 7, Article no. 1103
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.
URI
http://www.mdpi.com/1424-8220/16/7/1103/htmhttp://hdl.handle.net/20.500.11754/65825
ISSN
1424-8220
DOI
10.3390/s16071103
Appears in Collections:
COLLEGE OF COMPUTING[E] > COMPUTER SCIENCE(소프트웨어학부) > Articles
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