@inproceedings{c7b1b4a0004a4ac6aff80f8b5a47dc11,
title = "Fuzzy JPDAF approach for vehicle tracking in road situation",
abstract = "Data association was an important content in Multi-target tracking. Typical algorithms to deal with such problems are the joint probabilities data association filter (JPDAF) proposed by Bar-Shalom and his team. The basis of JPDAF is the calculus of the joint probabilities between the measurements and the tracks. The algorithm assigns weights for reasonable measurements and uses a weighted centroid of those measurements to update the track. In this paper, a new weight assignment method based on fuzzy c-means methodology was proposed, and the general methodology of JPDAF remains unchanged. This leads to a fruitful combination between fuzzy and probability approaches. It is proved that the method is simple and fast by simulation, and suits for automotive radar multi-target tracking.",
keywords = "Data association, Fuzzy clustering, Multi-target tracking",
author = "Hongshe Dang and Chongzhao Han and Zhansheng Duan",
year = "2003",
doi = "10.1109/ICIF.2003.177400",
language = "英语",
isbn = "0972184449",
series = "Proceedings of the 6th International Conference on Information Fusion, FUSION 2003",
publisher = "IEEE Computer Society",
pages = "1389--1393",
booktitle = "Proceedings of the 6th International Conference on Information Fusion, FUSION 2003",
note = "6th International Conference on Information Fusion, FUSION 2003 ; Conference date: 08-07-2003 Through 11-07-2003",
}