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 978-1-58603-727-7Full text not available from this repository.
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|
|Deposited By:||Dr L Mihaylov|
|Deposited On:||28 Jan 2008 08:56|
|Last Modified:||07 Jan 2017 00:07|
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