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Bearings-Only Tracking with Particle Filtering for Joint Parameter Learning and State Estimation

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

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Bearings-Only Tracking with Particle Filtering for Joint Parameter Learning and State Estimation. / Nemeth, Christopher; Fearnhead, Paul; Mihaylova, Lyudmila et al.
Information Fusion (FUSION), 2012 15th International Conference on. IEEE, 2012. p. 824-831.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Harvard

Nemeth, C, Fearnhead, P, Mihaylova, L & Vorley, D 2012, Bearings-Only Tracking with Particle Filtering for Joint Parameter Learning and State Estimation. in Information Fusion (FUSION), 2012 15th International Conference on. IEEE, pp. 824-831, The 15th International Conference on Information Fusion, Singapore, 9/07/12. <http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6289887>

APA

Nemeth, C., Fearnhead, P., Mihaylova, L., & Vorley, D. (2012). Bearings-Only Tracking with Particle Filtering for Joint Parameter Learning and State Estimation. In Information Fusion (FUSION), 2012 15th International Conference on (pp. 824-831). IEEE. http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6289887

Vancouver

Nemeth C, Fearnhead P, Mihaylova L, Vorley D. Bearings-Only Tracking with Particle Filtering for Joint Parameter Learning and State Estimation. In Information Fusion (FUSION), 2012 15th International Conference on. IEEE. 2012. p. 824-831

Author

Nemeth, Christopher ; Fearnhead, Paul ; Mihaylova, Lyudmila et al. / Bearings-Only Tracking with Particle Filtering for Joint Parameter Learning and State Estimation. Information Fusion (FUSION), 2012 15th International Conference on. IEEE, 2012. pp. 824-831

Bibtex

@inproceedings{a59e839314c847f59728725c50baee01,
title = "Bearings-Only Tracking with Particle Filtering for Joint Parameter Learning and State Estimation",
abstract = "This paper considers the problem of bearings only tracking of manoeuvring targets. A learning particle filtering algorithm is proposed which can estimate both the unknown target states and unknown model parameters. The algorithm performance is validated and tested over a challenging scenario with abrupt manoeuvres. A comparison of the proposed algorithm with the Interacting Multiple Model (IMM) filter is presented. The learning particle filter has shown accurate estimation results and improved accuracy compared with the IMM filter.",
keywords = "particle filters, state and parameter estimation, learning algorithms, tracking, nonlinear systems, IMM filter , bearings-only tracking , hidden Markov process , interacting multiple model filter , joint parameter learning , manoeuvering targets , particle filtering , state estimation , unknown model parameters , unknown target states",
author = "Christopher Nemeth and Paul Fearnhead and Lyudmila Mihaylova and D. Vorley",
note = "pp. 824-831; The 15th International Conference on Information Fusion ; Conference date: 09-07-2012 Through 12-07-2012",
year = "2012",
month = jul,
day = "7",
language = "English",
isbn = "9781467304177",
pages = "824--831",
booktitle = "Information Fusion (FUSION), 2012 15th International Conference on",
publisher = "IEEE",

}

RIS

TY - GEN

T1 - Bearings-Only Tracking with Particle Filtering for Joint Parameter Learning and State Estimation

AU - Nemeth, Christopher

AU - Fearnhead, Paul

AU - Mihaylova, Lyudmila

AU - Vorley, D.

N1 - pp. 824-831

PY - 2012/7/7

Y1 - 2012/7/7

N2 - This paper considers the problem of bearings only tracking of manoeuvring targets. A learning particle filtering algorithm is proposed which can estimate both the unknown target states and unknown model parameters. The algorithm performance is validated and tested over a challenging scenario with abrupt manoeuvres. A comparison of the proposed algorithm with the Interacting Multiple Model (IMM) filter is presented. The learning particle filter has shown accurate estimation results and improved accuracy compared with the IMM filter.

AB - This paper considers the problem of bearings only tracking of manoeuvring targets. A learning particle filtering algorithm is proposed which can estimate both the unknown target states and unknown model parameters. The algorithm performance is validated and tested over a challenging scenario with abrupt manoeuvres. A comparison of the proposed algorithm with the Interacting Multiple Model (IMM) filter is presented. The learning particle filter has shown accurate estimation results and improved accuracy compared with the IMM filter.

KW - particle filters

KW - state and parameter estimation

KW - learning algorithms

KW - tracking

KW - nonlinear systems

KW - IMM filter

KW - bearings-only tracking

KW - hidden Markov process

KW - interacting multiple model filter

KW - joint parameter learning

KW - manoeuvering targets

KW - particle filtering

KW - state estimation

KW - unknown model parameters

KW - unknown target states

M3 - Conference contribution/Paper

SN - 9781467304177

SP - 824

EP - 831

BT - Information Fusion (FUSION), 2012 15th International Conference on

PB - IEEE

T2 - The 15th International Conference on Information Fusion

Y2 - 9 July 2012 through 12 July 2012

ER -