@inproceedings{4057750f3918418da839b6c713075173,
title = "Track association based on the empirical mode decomposition in passive localization",
abstract = "In distributed passive localization and tracking system, the track observed by the subsystem seems like Brownian motion track, because the tracked target is non-cooperative target and its maneuver is often complex, and the localization accuracy is poor. These track characteristics will seriously disturb track association between different subsystems. In order to solve this problem, the track to track association algorithm based on empirical mode decomposition (EMD) is proposed in this article. To lessen the impact of target placement and maneuvering mistakes, components that do not follow the track trend are removed from each dimension of the track recorded by each sub-system. The track motion trend vector is formed using the remaining low-frequency components as track characteristics, and the relevant correlation criteria are created. The track association between sub-systems is ultimately finished since the correlation threshold is self-adaptive and does not require the creation of a motion model.",
keywords = "Track association, empirical mode decomposition, hausdorff distance, movement trend",
author = "Kai Lu and Chundong Qi",
note = "Publisher Copyright: {\textcopyright} COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.; 2023 5th International Conference on Information Science, Electrical, and Automation Engineering, ISEAE 2023 ; Conference date: 24-03-2023 Through 26-03-2023",
year = "2023",
doi = "10.1117/12.2689576",
language = "English",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Tao Lei",
booktitle = "5th International Conference on Information Science, Electrical, and Automation Engineering, ISEAE 2023",
address = "United States",
}