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Joint Target Tracking and Classification via Sequential Monte Carlo Filtering.

Angelova, D and Mihaylova, L (2007) Joint Target Tracking and Classification via Sequential Monte Carlo Filtering. In: Advances and Challenges in Multisensor Data and Information Processing. NATO Security Through Science Series: Information and Security, 8 . IOS Press, the Netherlands, pp. 33-40. ISBN 978-1-58603-727-7

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A sequential Monte Carlo algorithm is suggested for joint maneuvering target tracking and classification, based on kinematic measurements. A mixture Kalman filter is designed for two-class identification of air targets: commercial and military aircraft. Speed and acceleration constraints are imposed on the target behaviour models in order to improve the classification process. The class is modeled as an independent random variable, which can take values over the discrete class space with an equal probability. As a result, the multiple-model structure in the class space, required for reliable classification, is achieved. The performance of the proposed algorithm is evaluated by simulation over typical target scenarios.

Item Type: Contribution in Book/Report/Proceedings
Uncontrolled Keywords: Joint tracking and classification ; sequential Monte Carlo methods ; mixture Kalman filtering ; DCS-publications-id ; incoll-69 ; DCS-publications-credits ; dsp ; 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
ID Code: 1012
Deposited By: Dr L Mihaylov
Deposited On: 28 Jan 2008 08:56
Refereed?: No
Published?: Published
Last Modified: 07 Apr 2018 00:04
Identification Number:

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