Open AccessA GM-CPHD Filtering Algorithm Assisted by Luminance Information
Author(s) -
Yue Li,
Quan Sun,
Dongya Wang,
Jian Huang,
Pengyuan Li,
Yupeng Wang,
Zhaodong Niu
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1971/1/012065
Subject(s) - clutter , luminance , tracking (education) , computer science , algorithm , gaussian , artificial intelligence , pattern recognition (psychology) , function (biology) , process (computing) , constant false alarm rate , computer vision , physics , pedagogy , radar , quantum mechanics , operating system , evolutionary biology , biology , psychology , telecommunications
Optical tracking of small and weak targets in space, has problems such as a large and unknown number of targets, dense clutter, close measurement of neighboring targets, and false detection and missed detection. In this paper, a Gaussian mixture cardinalized probability hypothesis density (GM-CPHD) filtering algorithm assisted by luminance information is proposed. By establishing the luminance likelihood function, the luminance information is introduced into the CPHD filtering process, and the Gaussian mixture implementation method is given. The simulation results indicate that the proposed algorithm can effectively adapt to the dense clutter environment, and compared with the traditional GM-CPHD filtering algorithm, its tracking accuracy for multiple targets is significantly improved.