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Monte Carlo algorithm for maneuvering target tracking and classification

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Monte Carlo algorithm for maneuvering target tracking and classification. / Angelova, D.; Mihaylova, L.; Semerdjiev, T.

Computational Science - ICCS 2004: 4th International Conference, Kraków, Poland, June 6-9, 2004, Proceedings, Part IV. Springer, 2004. p. 531-539.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

Harvard

Angelova, D, Mihaylova, L & Semerdjiev, T 2004, Monte Carlo algorithm for maneuvering target tracking and classification. in Computational Science - ICCS 2004: 4th International Conference, Kraków, Poland, June 6-9, 2004, Proceedings, Part IV. Springer, pp. 531-539, Lecture Notes in Computer Science from the International Conference on Computational Science (ICCS) 2004, Krakow, Poland, 6/06/04. https://doi.org/10.1007/978-3-540-25944-2_69

APA

Angelova, D., Mihaylova, L., & Semerdjiev, T. (2004). Monte Carlo algorithm for maneuvering target tracking and classification. In Computational Science - ICCS 2004: 4th International Conference, Kraków, Poland, June 6-9, 2004, Proceedings, Part IV (pp. 531-539). Springer. https://doi.org/10.1007/978-3-540-25944-2_69

Vancouver

Angelova D, Mihaylova L, Semerdjiev T. Monte Carlo algorithm for maneuvering target tracking and classification. In Computational Science - ICCS 2004: 4th International Conference, Kraków, Poland, June 6-9, 2004, Proceedings, Part IV. Springer. 2004. p. 531-539 https://doi.org/10.1007/978-3-540-25944-2_69

Author

Angelova, D. ; Mihaylova, L. ; Semerdjiev, T. / Monte Carlo algorithm for maneuvering target tracking and classification. Computational Science - ICCS 2004: 4th International Conference, Kraków, Poland, June 6-9, 2004, Proceedings, Part IV. Springer, 2004. pp. 531-539

Bibtex

@inproceedings{8c47037753124eec8a7a9ca0c063194d,
title = "Monte Carlo algorithm for maneuvering target tracking and classification",
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.",
keywords = "Monte Carlo methods, Joint tracking and classification, nonlinear systems DCS-publications-id, inproc-436, DCS-publications-personnel-id, 121",
author = "D. Angelova and L. Mihaylova and T. Semerdjiev",
note = "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; Lecture Notes in Computer Science from the International Conference on Computational Science (ICCS) 2004 ; Conference date: 06-06-2004 Through 09-06-2004",
year = "2004",
doi = "10.1007/978-3-540-25944-2_69",
language = "English",
pages = "531--539",
booktitle = "Computational Science - ICCS 2004",
publisher = "Springer",

}

RIS

TY - GEN

T1 - Monte Carlo algorithm for maneuvering target tracking and classification

AU - Angelova, D.

AU - Mihaylova, L.

AU - Semerdjiev, T.

N1 - 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

PY - 2004

Y1 - 2004

N2 - 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.

AB - 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.

KW - Monte Carlo methods

KW - Joint tracking and classification

KW - nonlinear systems DCS-publications-id

KW - inproc-436

KW - DCS-publications-personnel-id

KW - 121

U2 - 10.1007/978-3-540-25944-2_69

DO - 10.1007/978-3-540-25944-2_69

M3 - Conference contribution/Paper

SP - 531

EP - 539

BT - Computational Science - ICCS 2004

PB - Springer

T2 - Lecture Notes in Computer Science from the International Conference on Computational Science (ICCS) 2004

Y2 - 6 June 2004 through 9 June 2004

ER -