Fast communication: Gaussian mixture PHD filter for jump Markov models based on best-fitting Gaussian approximation

Authors:
Wenling Li;Yingmin Jia
Affiliations:
The Seventh Research Division and the Department of Systems and Control, Beihang University (BUAA), Beijing 100191, China;The Seventh Research Division and the Department of Systems and Control, Beihang University (BUAA), Beijing 100191, China and Key Laboratory of Mathematics, Informatics and Behavioral Semantics (L ...
Venue:
Signal Processing
Year:
2011

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Abstract

A new Gaussian mixture probability hypothesis density (PHD) filter is developed for tracking multiple maneuvering targets that follow jump Markov models. This approach is based on the best-fitting Gaussian approximation which has been shown to be an accurate predictor of the interacting multiple model (IMM) performance. Compared with the existing Gaussian mixture multiple model PHD filter without interacting, simulations show that the proposed filter achieves better results with much less computational expense.