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.
Research supported in part by Center of Excellence BIS21 grant ICA1-2000-70016, by the Bulgarian Foundation for Scientific Investigations under grants I-1202/02 and I-1205/02, and in part by the UK MOD Data and Information Fusion Defence Technology Center.
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© 2004 Springer-Verlag Berlin Heidelberg
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Angelova, D., Mihaylova, L., Semerdjiev, T. (2004). Monte Carlo Algorithm for Maneuvering Target Tracking and Classification. In: Bubak, M., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds) Computational Science - ICCS 2004. ICCS 2004. Lecture Notes in Computer Science, vol 3039. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-25944-2_69
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DOI: https://doi.org/10.1007/978-3-540-25944-2_69
Publisher Name: Springer, Berlin, Heidelberg
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