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Monte Carlo algorithm for maneuvering target tracking and classification

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Publication date2004
Host publicationComputational Science - ICCS 2004: 4th International Conference, Kraków, Poland, June 6-9, 2004, Proceedings, Part IV
PublisherSpringer
Pages531-539
Number of pages9
<mark>Original language</mark>English

Conference

ConferenceLecture Notes in Computer Science from the International Conference on Computational Science (ICCS) 2004
CityKrakow, Poland
Period6/06/049/06/04

Conference

ConferenceLecture Notes in Computer Science from the International Conference on Computational Science (ICCS) 2004
CityKrakow, Poland
Period6/06/049/06/04

Abstract

This paper considers the problem of joint maneuvering target tracking and classification. Based on the recently proposed particle filtering approach, a multiple model particle filter is designed for two-class identification of air targets: commercial and military aircraft. The classification task is implemented by processing radar (kinematic) measurements only, no class (feature) measurements are used. A speed likelihood function for each class is defined using a priori information about speed constraints. Class-dependent speed likelihoods are calculated through the state estimates of each class-dependent tracker. They are combined with the kinematic measurement likelihoods in order to improve the process of classification. The performance of the suggested multiple model particle filter is evaluated by Monte Carlo simulations.

Bibliographic note

Vol. LNCS 3039, Springer, M. Bubak, G. Dick van Albada, P. Sloot, and J. Dongarra (Eds.), Computational Science - ICCS Proc., 2004, Part IV, pp. 531-539, 2004. doi:10.1007/b98005