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Proceedings Paper

An independent sequential maximum likelihood approach to simultaneous track-to-track association and bias removal
Author(s): Qiong Song; Yuehuan Wang; Xiaoyun Yan; Dang Liu
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Paper Abstract

In this paper we propose an independent sequential maximum likelihood approach to address the joint track-to-track association and bias removal in multi-sensor information fusion systems. First, we enumerate all kinds of association situation following by estimating a bias for each association. Then we calculate the likelihood of each association after bias compensated. Finally we choose the maximum likelihood of all association situations as the association result and the corresponding bias estimation is the registration result. Considering the high false alarm and interference, we adopt the independent sequential association to calculate the likelihood. Simulation results show that our proposed method can give out the right association results and it can estimate the bias precisely simultaneously for small number of targets in multi-sensor fusion system.

Paper Details

Date Published: 14 December 2015
PDF: 5 pages
Proc. SPIE 9813, MIPPR 2015: Pattern Recognition and Computer Vision, 981308 (14 December 2015); doi: 10.1117/12.2205507
Show Author Affiliations
Qiong Song, Huazhong Univ. of Science and Technology (China)
Yuehuan Wang, Huazhong Univ. of Science and Technology (China)
National Key Lab. of Science and Technology on Multi-spectral Information Processing (China)
Xiaoyun Yan, Huazhong Univ. of Science and Technology (China)
Dang Liu, Huazhong Univ. of Science and Technology (China)


Published in SPIE Proceedings Vol. 9813:
MIPPR 2015: Pattern Recognition and Computer Vision
Tianxu Zhang; Jianguo Liu, Editor(s)

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