Web projektu DAR

Research Centre
Data - Algorithms - Decision Making

Established in 2005 under support of MŠMT ČR (project 1M0572)

Publications

Gaussian mixtures proposal density in particle filter for track-before-detect

Typ:

Conference paper

Authors:

Straka O., Šimandl M., Duník J.

Proceedings name:

Preprints of the 12th International Conference on Information Fusion

Serie:

Seattle, USA

Year:

2009

Pages:

270-277

ISBN:

978-0-9824438-0-4

Contact:

Ing. Jindřich Duník, Ph.D. (FAV Západočeská univerzita, katedra kybernetiky)

Keywords:

tracking, nonlinear filtering, estimation

URL (www page):

http://www.isif.org/fusion2009.htm

attachment1:

clanek_FUSION09_JD.pdf [file]

attachment2:

prezentace_FUSION09_JD.pdf [file]

Anotation:

The paper deals with state estimation for the track-before-detect approach using the particle filter. The focus is aimed at the track initiation proposal density of the particle filter which considerably affects estimate quality. The goal of the paper is to design a proposal based on a Gaussian mixture using a bank of extended Kalman filters. This leads to root mean square error lower than that achieved by usual simple track initiation proposals. Due to application of several developed techniques reducing computational requirements of the designed proposal, the Gaussian mixture particle filter also achieves lower computational requirements than ordinary particle filter. Performance of the proposed Gaussian mixture track initiation proposal in the particle filter is demonstrated in a numerical example.