@inproceedings{ea5e8f3bd914493c860140364624262d,
title = "Joint probabilistic data association filter with unknown detection probability and clutter rate",
abstract = "This paper proposes a novel joint probabilistic data association (JPDA) filter for joint target tracking and track maintenance in the presence of unknown detection probability and clutter rate. The proposed algorithm consists of two main parts: (1) the standard JPDA filter with a Poisson point process birth model for multi-object state estimation; and (2) a multi-Bernoulli filter for detection probability and clutter rate estimation. Performance evaluation shows that the proposed JPDA filter can rapidly recover the performance of the ideal JPDA filter with perfect knowledge of detection probability and clutter rate. Therefore, the suggested algorithm is more suitable for real applications in a complex environment for multi-target tracking.",
author = "Shaoming He and Shin, {Hyo Sang} and Antonios Tsourdos",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 13th IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI 2017 ; Conference date: 16-11-2017 Through 18-11-2017",
year = "2017",
month = dec,
day = "7",
doi = "10.1109/MFI.2017.8170380",
language = "English",
series = "IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "559--564",
booktitle = "MFI 2017 - 2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems",
address = "United States",
}