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Group object structure and state estimation with evolving networks and Monte Carlo methods.

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Group object structure and state estimation with evolving networks and Monte Carlo methods. / Gning, Amadou; Mihaylova, Lyudmila; Maskell, Simon et al.
In: IEEE Transactions on Signal Processing, Vol. 59, No. 4, 01.04.2011, p. 1383-1396.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Gning, A, Mihaylova, L, Maskell, S, Pang, S & Godsill, S 2011, 'Group object structure and state estimation with evolving networks and Monte Carlo methods.', IEEE Transactions on Signal Processing, vol. 59, no. 4, pp. 1383-1396. https://doi.org/10.1109/TSP.2010.2103062

APA

Gning, A., Mihaylova, L., Maskell, S., Pang, S., & Godsill, S. (2011). Group object structure and state estimation with evolving networks and Monte Carlo methods. IEEE Transactions on Signal Processing, 59(4), 1383-1396. https://doi.org/10.1109/TSP.2010.2103062

Vancouver

Gning A, Mihaylova L, Maskell S, Pang S, Godsill S. Group object structure and state estimation with evolving networks and Monte Carlo methods. IEEE Transactions on Signal Processing. 2011 Apr 1;59(4):1383-1396. doi: 10.1109/TSP.2010.2103062

Author

Gning, Amadou ; Mihaylova, Lyudmila ; Maskell, Simon et al. / Group object structure and state estimation with evolving networks and Monte Carlo methods. In: IEEE Transactions on Signal Processing. 2011 ; Vol. 59, No. 4. pp. 1383-1396.

Bibtex

@article{06bce613c1c9477ca585c17558f8a7c0,
title = "Group object structure and state estimation with evolving networks and Monte Carlo methods.",
abstract = "This paper proposes a technique for motion estimation of groups of targets based on evolving graph networks. The main novelty over alternative group tracking techniques stems from learning the network structure for the groups. Each node of the graph corresponds to a target within the group. The uncertainty of the group structure is estimated jointly with the group target states. New group structure evolutional models are proposed for automatic graph structure initialisation, incorporation of new nodes, unexisting nodes removal and the edge update. We update both the state and the graph structure based on range and bearing measurements. This evolving graph model is propagated combined with a sequential Monte Carlo framework able to cope with measurement origin uncertainty. The effectiveness of the proposed approach is illustrated over a challenging scenario for group motion estimation in urban environments. Results with merging, splitting and crossing of groups are presented with high estimation accuracy. The performance of the algorithm is also evaluated and shown on real ground moving target indicator (GMTI) radar data and in the presence of data origin uncertainty.",
keywords = "evolving graphs, random graphs, group target tracking, nonlinear estimation, Monte Carlo methods, Metropolis-Hastings step",
author = "Amadou Gning and Lyudmila Mihaylova and Simon Maskell and Sze Pang and Simon Godsill",
year = "2011",
month = apr,
day = "1",
doi = "10.1109/TSP.2010.2103062",
language = "English",
volume = "59",
pages = "1383--1396",
journal = "IEEE Transactions on Signal Processing",
issn = "1053-587X",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "4",

}

RIS

TY - JOUR

T1 - Group object structure and state estimation with evolving networks and Monte Carlo methods.

AU - Gning, Amadou

AU - Mihaylova, Lyudmila

AU - Maskell, Simon

AU - Pang, Sze

AU - Godsill, Simon

PY - 2011/4/1

Y1 - 2011/4/1

N2 - This paper proposes a technique for motion estimation of groups of targets based on evolving graph networks. The main novelty over alternative group tracking techniques stems from learning the network structure for the groups. Each node of the graph corresponds to a target within the group. The uncertainty of the group structure is estimated jointly with the group target states. New group structure evolutional models are proposed for automatic graph structure initialisation, incorporation of new nodes, unexisting nodes removal and the edge update. We update both the state and the graph structure based on range and bearing measurements. This evolving graph model is propagated combined with a sequential Monte Carlo framework able to cope with measurement origin uncertainty. The effectiveness of the proposed approach is illustrated over a challenging scenario for group motion estimation in urban environments. Results with merging, splitting and crossing of groups are presented with high estimation accuracy. The performance of the algorithm is also evaluated and shown on real ground moving target indicator (GMTI) radar data and in the presence of data origin uncertainty.

AB - This paper proposes a technique for motion estimation of groups of targets based on evolving graph networks. The main novelty over alternative group tracking techniques stems from learning the network structure for the groups. Each node of the graph corresponds to a target within the group. The uncertainty of the group structure is estimated jointly with the group target states. New group structure evolutional models are proposed for automatic graph structure initialisation, incorporation of new nodes, unexisting nodes removal and the edge update. We update both the state and the graph structure based on range and bearing measurements. This evolving graph model is propagated combined with a sequential Monte Carlo framework able to cope with measurement origin uncertainty. The effectiveness of the proposed approach is illustrated over a challenging scenario for group motion estimation in urban environments. Results with merging, splitting and crossing of groups are presented with high estimation accuracy. The performance of the algorithm is also evaluated and shown on real ground moving target indicator (GMTI) radar data and in the presence of data origin uncertainty.

KW - evolving graphs

KW - random graphs

KW - group target tracking

KW - nonlinear estimation

KW - Monte Carlo methods

KW - Metropolis-Hastings step

UR - http://www.scopus.com/inward/record.url?scp=79952644978&partnerID=8YFLogxK

U2 - 10.1109/TSP.2010.2103062

DO - 10.1109/TSP.2010.2103062

M3 - Journal article

VL - 59

SP - 1383

EP - 1396

JO - IEEE Transactions on Signal Processing

JF - IEEE Transactions on Signal Processing

SN - 1053-587X

IS - 4

ER -