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Sensors 2016, 16(7), 1103; doi:10.3390/s16071103

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

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1,2,3
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1,2,3,4,* , 3
and
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1
College of Computer Science and Technology, Jilin University, Changchun 130012, China
2
Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China
3
State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130012, China
4
Department of Computer Science and Engineering, Hanyang University, Ansan 426791, Korea
*
Author to whom correspondence should be addressed.
Academic Editors: Xue-Bo Jin, Feng-Bao Yang, Shuli Sun and Hong Wei
Received: 6 May 2016 / Revised: 6 July 2016 / Accepted: 11 July 2016 / Published: 16 July 2016
(This article belongs to the Special Issue Advances in Multi-Sensor Information Fusion: Theory and Applications)

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 ‘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. View Full-Text
Keywords: Joint Kalman Filter; innovation-based adaptive estimation; motion state estimation; data fusion Joint Kalman Filter; innovation-based adaptive estimation; motion state estimation; data fusion
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Gao, S.; Liu, Y.; Wang, J.; Deng, W.; Oh, H. The Joint Adaptive Kalman Filter (JAKF) for Vehicle Motion State Estimation. Sensors 2016, 16, 1103.

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