Standard
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/ISSN › Conference contribution/Paper › peer-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_69APA
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_69Vancouver
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 -
