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Joint Target Tracking and Classification via Sequential Monte Carlo Filtering.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Chapter

Published

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Joint Target Tracking and Classification via Sequential Monte Carlo Filtering. / Angelova, D; Mihaylova, L.
Advances and Challenges in Multisensor Data and Information Processing. ed. / E Lefebvre. Vol. 8 the Netherlands: IOS Press, 2007. p. 33-40 (NATO Security Through Science Series: Information and Security).

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Chapter

Harvard

Angelova, D & Mihaylova, L 2007, Joint Target Tracking and Classification via Sequential Monte Carlo Filtering. in E Lefebvre (ed.), Advances and Challenges in Multisensor Data and Information Processing. vol. 8, NATO Security Through Science Series: Information and Security, IOS Press, the Netherlands, pp. 33-40.

APA

Angelova, D., & Mihaylova, L. (2007). Joint Target Tracking and Classification via Sequential Monte Carlo Filtering. In E. Lefebvre (Ed.), Advances and Challenges in Multisensor Data and Information Processing (Vol. 8, pp. 33-40). (NATO Security Through Science Series: Information and Security). IOS Press.

Vancouver

Angelova D, Mihaylova L. Joint Target Tracking and Classification via Sequential Monte Carlo Filtering. In Lefebvre E, editor, Advances and Challenges in Multisensor Data and Information Processing. Vol. 8. the Netherlands: IOS Press. 2007. p. 33-40. (NATO Security Through Science Series: Information and Security).

Author

Angelova, D ; Mihaylova, L. / Joint Target Tracking and Classification via Sequential Monte Carlo Filtering. Advances and Challenges in Multisensor Data and Information Processing. editor / E Lefebvre. Vol. 8 the Netherlands : IOS Press, 2007. pp. 33-40 (NATO Security Through Science Series: Information and Security).

Bibtex

@inbook{f85bcab2433f4a098ee5b01570b38480,
title = "Joint Target Tracking and Classification via Sequential Monte Carlo Filtering.",
abstract = "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.",
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",
author = "D Angelova and L Mihaylova",
year = "2007",
language = "English",
isbn = "978-1-58603-727-7",
volume = "8",
series = "NATO Security Through Science Series: Information and Security",
publisher = "IOS Press",
pages = "33--40",
editor = "E Lefebvre",
booktitle = "Advances and Challenges in Multisensor Data and Information Processing",

}

RIS

TY - CHAP

T1 - Joint Target Tracking and Classification via Sequential Monte Carlo Filtering.

AU - Angelova, D

AU - Mihaylova, L

PY - 2007

Y1 - 2007

N2 - 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.

AB - 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.

KW - Joint tracking and classification

KW - sequential Monte Carlo methods

KW - mixture Kalman filtering

KW - DCS-publications-id

KW - incoll-69

KW - DCS-publications-credits

KW - dsp

KW - DCS-publications-personnel-id

KW - 121

M3 - Chapter

SN - 978-1-58603-727-7

VL - 8

T3 - NATO Security Through Science Series: Information and Security

SP - 33

EP - 40

BT - Advances and Challenges in Multisensor Data and Information Processing

A2 - Lefebvre, E

PB - IOS Press

CY - the Netherlands

ER -