@inproceedings{89ad73c54eb04fea968ad99c43c533f4,
title = "Anti-bias Target Association Method Based on Laplacian Spectrum and Kuhn-Munkres Algorithm",
abstract = "In the problem of collaborative situational awareness, the single sensor inevitably has random errors, systematic errors, false alarms, and missed alarms. To improve the accuracy of target association in the above cases, this paper proposes an anti-bias target association method based on the Laplacian spectrum and Kuhn-Munkres (KM) algorithm. The main contributions of the paper include addressing the coupling problem between system error calibration and target association through two stages, focusing on the structural characteristics of the target formation in the first stage and the absolute position of the target itself in the second stage. The algorithm takes into account both factors and achieves accurate association under complex errors. The test results demonstrate that the proposed method effectively improves the accuracy of anti-bias target association.",
keywords = "Collaborative Situational Awareness, Kuhn-Munkres Algorithm, Laplacian Spectrum, Target Association",
author = "Xiaomeng Bai and Cheng Xu and Wenxing Fu and Xujun Guan and Hang Zhang and Wenxing Fu",
note = "Publisher Copyright: {\textcopyright} Beijing HIWING Scientific and Technological Information Institute 2024.; 3rd International Conference on Autonomous Unmanned Systems, ICAUS 2023 ; Conference date: 09-09-2023 Through 11-09-2023",
year = "2024",
doi = "10.1007/978-981-97-1091-1_16",
language = "英语",
isbn = "9789819710904",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "169--181",
editor = "Yi Qu and Mancang Gu and Yifeng Niu and Wenxing Fu",
booktitle = "Proceedings of 3rd 2023 International Conference on Autonomous Unmanned Systems (3rd ICAUS 2023) - Volume IV",
}