Multiclass Generalized Labeled Multi-Bernoulli Filter With Kernel Density Estimation for Target Tracking in Unknown Backgrounds

In multitarget tracking (MTT), the prior information of background parameters, such as clutter intensity and detection probability, exerts a significant influence on the performance of the filter. Thus, when performing MTT in unknown backgrounds, the filter should possess the capability to estimate...

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Vydané v:	IEEE sensors journal Ročník 25; číslo 15; s. 29075 - 29090
Hlavní autori:	Wu, Qinchen, Sun, Jinping, Yang, Bin, Li, Juan, Wang, Yanping
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.08.2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Clutter Estimation Filtering algorithms Generalized labeled multi-Bernoulli (GLMB) filter Graphical models Information filters Kernel kernel density estimation (KDE) Multiple target tracking multitarget tracking (MTT) Noise measurement nonuniform clutter Parameters Radio frequency random finite sets (RFSs) robust filtering Target tracking Tracking unknown clutter rate unknown detection probability
ISSN:	1530-437X, 1558-1748
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