Home > Research > Publications & Outputs > Extended Object Tracking Using Mixture Kalman F...

Text available via DOI:

View graph of relations

Extended Object Tracking Using Mixture Kalman Filtering.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNChapter

Published

Standard

Extended Object Tracking Using Mixture Kalman Filtering. / Angelova, D; Mihaylova, L.

Lecture Notes in Computer Science. ed. / T Boyanov; S Dimova; K Georgiev; G Nikolov. Vol. 4310 ISSN:. ed. Heidelberg : Springer-Verlag, 2007. p. 122-130.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNChapter

Harvard

Angelova, D & Mihaylova, L 2007, Extended Object Tracking Using Mixture Kalman Filtering. in T Boyanov, S Dimova, K Georgiev & G Nikolov (eds), Lecture Notes in Computer Science. ISSN: edn, vol. 4310, Springer-Verlag, Heidelberg, pp. 122-130. https://doi.org/10.1007/978-3-540-70942-8_14

APA

Angelova, D., & Mihaylova, L. (2007). Extended Object Tracking Using Mixture Kalman Filtering. In T. Boyanov, S. Dimova, K. Georgiev, & G. Nikolov (Eds.), Lecture Notes in Computer Science (ISSN: ed., Vol. 4310, pp. 122-130). Heidelberg: Springer-Verlag,. https://doi.org/10.1007/978-3-540-70942-8_14

Vancouver

Angelova D, Mihaylova L. Extended Object Tracking Using Mixture Kalman Filtering. In Boyanov T, Dimova S, Georgiev K, Nikolov G, editors, Lecture Notes in Computer Science. ISSN: ed. Vol. 4310. Heidelberg: Springer-Verlag,. 2007. p. 122-130 https://doi.org/10.1007/978-3-540-70942-8_14

Author

Angelova, D ; Mihaylova, L. / Extended Object Tracking Using Mixture Kalman Filtering. Lecture Notes in Computer Science. editor / T Boyanov ; S Dimova ; K Georgiev ; G Nikolov. Vol. 4310 ISSN:. ed. Heidelberg : Springer-Verlag, 2007. pp. 122-130

Bibtex

@inbook{baccbb97326d436abb428a2408e69206,
title = "Extended Object Tracking Using Mixture Kalman Filtering.",
abstract = "This paper addresses the problem of tracking extended objects. Examples of extended objects are ships and a convoy of vehicles. Such kind of objects have particularities which pose challenges in front of methods considering the extended object as a single point. Measurements of the object extent can be used for estimating size parameters of the object, whose shape is modeled by an ellipse. This paper proposes a solution to the extended object tracking problem by mixture Kalman filtering. The system model is formulated in a conditional dynamic linear (CDL) form. Based on the specifics of the task, two latent indicator variables are proposed, characterising the mode of maneuvering and size type, respectively. The developed Mixture Kalman filter is validated and evaluated by computer simulation.",
keywords = "nonlinear estimation, Monte Cralo methods, parameter estimation, stochastic filters, extended objects, DCS-publications-id, incoll-66, DCS-publications-credits, dsp, DCS-publications-personnel-id, 121",
author = "D Angelova and L Mihaylova",
year = "2007",
doi = "10.1007/978-3-540-70942-8_14",
language = "English",
isbn = "978-3-540-70940-4",
volume = "4310",
pages = "122--130",
editor = "T Boyanov and S Dimova and K Georgiev and G Nikolov",
booktitle = "Lecture Notes in Computer Science",
publisher = "Springer-Verlag,",
edition = "ISSN:",

}

RIS

TY - CHAP

T1 - Extended Object Tracking Using Mixture Kalman Filtering.

AU - Angelova, D

AU - Mihaylova, L

PY - 2007

Y1 - 2007

N2 - This paper addresses the problem of tracking extended objects. Examples of extended objects are ships and a convoy of vehicles. Such kind of objects have particularities which pose challenges in front of methods considering the extended object as a single point. Measurements of the object extent can be used for estimating size parameters of the object, whose shape is modeled by an ellipse. This paper proposes a solution to the extended object tracking problem by mixture Kalman filtering. The system model is formulated in a conditional dynamic linear (CDL) form. Based on the specifics of the task, two latent indicator variables are proposed, characterising the mode of maneuvering and size type, respectively. The developed Mixture Kalman filter is validated and evaluated by computer simulation.

AB - This paper addresses the problem of tracking extended objects. Examples of extended objects are ships and a convoy of vehicles. Such kind of objects have particularities which pose challenges in front of methods considering the extended object as a single point. Measurements of the object extent can be used for estimating size parameters of the object, whose shape is modeled by an ellipse. This paper proposes a solution to the extended object tracking problem by mixture Kalman filtering. The system model is formulated in a conditional dynamic linear (CDL) form. Based on the specifics of the task, two latent indicator variables are proposed, characterising the mode of maneuvering and size type, respectively. The developed Mixture Kalman filter is validated and evaluated by computer simulation.

KW - nonlinear estimation

KW - Monte Cralo methods

KW - parameter estimation

KW - stochastic filters

KW - extended objects

KW - DCS-publications-id

KW - incoll-66

KW - DCS-publications-credits

KW - dsp

KW - DCS-publications-personnel-id

KW - 121

U2 - 10.1007/978-3-540-70942-8_14

DO - 10.1007/978-3-540-70942-8_14

M3 - Chapter

SN - 978-3-540-70940-4

VL - 4310

SP - 122

EP - 130

BT - Lecture Notes in Computer Science

A2 - Boyanov, T

A2 - Dimova, S

A2 - Georgiev, K

A2 - Nikolov, G

PB - Springer-Verlag,

CY - Heidelberg

ER -