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Sequential Monte Carlo algorithms for joint target tracking and classification using kinematic radar information

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Sequential Monte Carlo algorithms for joint target tracking and classification using kinematic radar information. / Angelova, Donka; Mihaylova, L.

Proceedings of the Seventh International Conference on Information Fusion. 2004. p. 709-716.

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

Harvard

Angelova, D & Mihaylova, L 2004, Sequential Monte Carlo algorithms for joint target tracking and classification using kinematic radar information. in Proceedings of the Seventh International Conference on Information Fusion. pp. 709-716, The 7th International Conference on Information Fusion, Stockholm, Sweden, 28/06/04. <http://www.fusion2004.foi.se/proc.html>

APA

Angelova, D., & Mihaylova, L. (2004). Sequential Monte Carlo algorithms for joint target tracking and classification using kinematic radar information. In Proceedings of the Seventh International Conference on Information Fusion (pp. 709-716) http://www.fusion2004.foi.se/proc.html

Vancouver

Angelova D, Mihaylova L. Sequential Monte Carlo algorithms for joint target tracking and classification using kinematic radar information. In Proceedings of the Seventh International Conference on Information Fusion. 2004. p. 709-716

Author

Angelova, Donka ; Mihaylova, L. / Sequential Monte Carlo algorithms for joint target tracking and classification using kinematic radar information. Proceedings of the Seventh International Conference on Information Fusion. 2004. pp. 709-716

Bibtex

@inproceedings{ffddb2af9c944997add13ca482fe982d,
title = "Sequential Monte Carlo algorithms for joint target tracking and classification using kinematic radar information",
abstract = "This paper considers the problem of joint maneuvering target tracking and classification. Based on recently proposed Monte Carlo techniques, a multiple model (MM) particle filter and a mixture Kalman filter (MKF) are designed for two-class identification of air targets: commercial and military aircraft. The classification task is carried out by processing radar 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 two designed estimators are compared and evaluated over a rather complex target trajectory. The results are demonstrating the usefulness of the proposed scheme for the incorporation of an additional speed information. Both filters illustrate the opportunity of the particle filtering and MKF to incorporate constraints in a natural way, providing reliable tracking and correct classification.",
keywords = "Joint tracking and classification, particle filter, multiple model, maneuvering target tracking, mixture Kalman filtering DCS-publications-id",
author = "Donka Angelova and L. Mihaylova",
year = "2004",
month = jul,
day = "1",
language = "English",
pages = "709--716",
booktitle = "Proceedings of the Seventh International Conference on Information Fusion",
note = "The 7th International Conference on Information Fusion ; Conference date: 28-06-2004 Through 01-07-2004",

}

RIS

TY - GEN

T1 - Sequential Monte Carlo algorithms for joint target tracking and classification using kinematic radar information

AU - Angelova, Donka

AU - Mihaylova, L.

PY - 2004/7/1

Y1 - 2004/7/1

N2 - This paper considers the problem of joint maneuvering target tracking and classification. Based on recently proposed Monte Carlo techniques, a multiple model (MM) particle filter and a mixture Kalman filter (MKF) are designed for two-class identification of air targets: commercial and military aircraft. The classification task is carried out by processing radar 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 two designed estimators are compared and evaluated over a rather complex target trajectory. The results are demonstrating the usefulness of the proposed scheme for the incorporation of an additional speed information. Both filters illustrate the opportunity of the particle filtering and MKF to incorporate constraints in a natural way, providing reliable tracking and correct classification.

AB - This paper considers the problem of joint maneuvering target tracking and classification. Based on recently proposed Monte Carlo techniques, a multiple model (MM) particle filter and a mixture Kalman filter (MKF) are designed for two-class identification of air targets: commercial and military aircraft. The classification task is carried out by processing radar 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 two designed estimators are compared and evaluated over a rather complex target trajectory. The results are demonstrating the usefulness of the proposed scheme for the incorporation of an additional speed information. Both filters illustrate the opportunity of the particle filtering and MKF to incorporate constraints in a natural way, providing reliable tracking and correct classification.

KW - Joint tracking and classification

KW - particle filter

KW - multiple model

KW - maneuvering target tracking

KW - mixture Kalman filtering DCS-publications-id

M3 - Conference contribution/Paper

SP - 709

EP - 716

BT - Proceedings of the Seventh International Conference on Information Fusion

T2 - The 7th International Conference on Information Fusion

Y2 - 28 June 2004 through 1 July 2004

ER -