NDLI: Sensor fusion for vehicle tracking based on the estimated probability

Content Provider	IET Digital Library
Author	Lee, Chang Joo Kim, Kyeong Eun Lim, Myo Taeg
Abstract	The track-to-track fusion (T2TF) algorithm is currently an attractive fusion methodology in the industry because the algorithm can reflect the reliability of sensor tracks. However, the T2TF algorithm cannot be applied when the probability information of the sensor is unknown. The aim of this study is to exploit the T2TF algorithm even in the absence of the probability information of the sensor. The covariance is estimated using the recursive equations of the Kalman filter. In addition, a novel track-association approach using the total similarity is developed to improve association performance. The total similarity complements the defects of the track disposition and the estimated track history. Finally, by fusing the associated tracks using the estimated covariance, the T2TF algorithm is successfully applied to sensors with an unknown covariance. The fusion results are then evaluated using the correct association rate and the optimal subpattern assignment metric. The simulation results obtained show the superiority of the proposed algorithm under three scenarios.
Starting Page	1386
Ending Page	1395
Page Count	10
ISSN	1751956X
Volume Number	12
e-ISSN	17519578
Issue Number	Issue 10, Dec (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-its/12/10
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-its.2018.5024
Journal	IET Intelligent Transport Systems
Publisher Date	2018-09-07
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Correct Association Rate Covariance Estimation Estimated Probability Estimated Track History Filtering Method in Signal Processing Kalman Filter Optimal Subpattern Assignment Metric Recursive Equation Recursive Estimation Road Vehicle Sensor Fusion Sensor Tracks Reliability Statistics T2TF Algorithm Target Tracking Total Similarity Track Disposition Track-association Approach Track-to-track Fusion Algorithm Traffic Engineering Computing Vehicle Tracking
Content Type	Text
Resource Type	Article
Subject	Law Transportation Environmental Science Mechanical Engineering

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2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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