Maneuvering target tracking using the nonlinear non-gaussian Kalman filter

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9 Scopus citations

Abstract

The problem of maneuvering target tracking is addressed in this paper. The main challenge in maneuvering target tracking stems from the nonlinearity and non-Gaussianity of the problem. The Singer model was used to model the maneuvering target dynamics and abrupt changes in the acceleration. According to this model, the heavy-tailed Cauchy distribution driving noise is used to model the abrupt changes in the target acceleration. The nonlinear, non-Gaussian Kalman filter was applied to this problem. The algorithm is based on the Gaussian mixture model for the posterior state vector. The nonlinear, non-Gaussian Kalman filter for this problem was tested using simulations, and it is shown that it outperforms both the particle filter and the extended Kalman filter.

Original languageEnglish
Title of host publication2006 IEEE International Conference on Acoustics, Speech, and Signal Processing - Proceedings
PagesIII724-III727
StatePublished - 1 Dec 2006
Event2006 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2006 - Toulouse, France
Duration: 14 May 200619 May 2006

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume3
ISSN (Print)1520-6149

Conference

Conference2006 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2006
Country/TerritoryFrance
CityToulouse
Period14/05/0619/05/06

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