Angelova, D. and Mihaylova, L. and Semerdjiev, T. (2004) Monte Carlo algorithm for maneuvering target tracking and classification. In: Computational Science - ICCS 2004. Springer, pp. 531-539.Full text not available from this repository.
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.
|Item Type:||Contribution in Book/Report/Proceedings|
|Additional Information:||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|
|Uncontrolled Keywords:||Monte Carlo methods ; Joint tracking and classification ; nonlinear systems DCS-publications-id ; inproc-436 ; DCS-publications-personnel-id ; 121|
|Subjects:||Q Science > QA Mathematics > QA75 Electronic computers. Computer science|
|Departments:||Faculty of Science and Technology > School of Computing & Communications|
|Deposited On:||28 Aug 2012 11:43|
|Last Modified:||24 Jan 2017 02:19|
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