The variable structure multiple model GM-PHD filter based on likely-model set algorithm

The multiple model (MM) version of Gaussian mixture probability hypothesis density (GM-PHD) filter is an effective method for multiple maneuvering target tracking. However, the model set used in the MM version of GM-PHD (MM-GM-PHD) filter is the same for each target at each time step. In this paper,...

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Bibliographic Details
Published in2016 19th International Conference on Information Fusion (FUSION) pp. 2289 - 2295
Main Authors Peng Dong, Zhongliang Jing, Minzhe Li, Han Pan
Format Conference Proceeding
LanguageEnglish
Published ISIF 01.07.2016
Subjects
Adaptation models
Estimation
Extraterrestrial measurements
Measurement uncertainty
Radio frequency
Target tracking
Uncertainty
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Summary:The multiple model (MM) version of Gaussian mixture probability hypothesis density (GM-PHD) filter is an effective method for multiple maneuvering target tracking. However, the model set used in the MM version of GM-PHD (MM-GM-PHD) filter is the same for each target at each time step. In this paper, we present a variable structure MM-GM-PHD (VSMM-GM-PHD) filter. Different model sets at different time are used for each target, and the GM-PHD filter for variable structure MM (VSMM) is also developed. Then the likely-model set (LMS) algorithm is employed to determine the model sets used for the different targets at different time steps. In this paper, the VSMM-GM-PHD filter based on LMS is proposed. The simulation results show that the proposed algorithm can work more efficiently with better accuracy compared with the effective MM-GM-PHD filter.