Home > Research > Publications & Outputs > Nonlinear filtering using measurements affected...

Research

Associated organisational units

Keywords

View graph of relations

Nonlinear filtering using measurements affected by stochastic, set-theoretic and association uncertainty

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

Published

Standard

Nonlinear filtering using measurements affected by stochastic, set-theoretic and association uncertainty. / Ristic, B; Gning, Amadou; Mihaylova, Lyudmila.
2011 Proceedings of the 14th International Conference on Information Fusion (FUSION 2011). USA, 2011. p. 1069-1076.

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

Harvard

Ristic, B, Gning, A & Mihaylova, L 2011, Nonlinear filtering using measurements affected by stochastic, set-theoretic and association uncertainty. in 2011 Proceedings of the 14th International Conference on Information Fusion (FUSION 2011). USA, pp. 1069-1076, International Conference on Information Fusion, Chicago, United States, 5/07/11.

APA

Ristic, B., Gning, A., & Mihaylova, L. (2011). Nonlinear filtering using measurements affected by stochastic, set-theoretic and association uncertainty. In 2011 Proceedings of the 14th International Conference on Information Fusion (FUSION 2011) (pp. 1069-1076).

Vancouver

Ristic B, Gning A, Mihaylova L. Nonlinear filtering using measurements affected by stochastic, set-theoretic and association uncertainty. In 2011 Proceedings of the 14th International Conference on Information Fusion (FUSION 2011). USA. 2011. p. 1069-1076

Author

Ristic, B ; Gning, Amadou ; Mihaylova, Lyudmila. / Nonlinear filtering using measurements affected by stochastic, set-theoretic and association uncertainty. 2011 Proceedings of the 14th International Conference on Information Fusion (FUSION 2011). USA, 2011. pp. 1069-1076

Bibtex

@inproceedings{4005b169da0248baae84549caa328f33,
title = "Nonlinear filtering using measurements affected by stochastic, set-theoretic and association uncertainty",
abstract = "The problem is sequential Bayesian detection and estimation of nonlinear dynamic stochastic systems using measurements affected by three sources of uncertainty: stochastic, set-theoretic and data association uncertainty. Following Mahler{\textquoteright}s framework for information fusion, the paper develops the optimal Bayes filter for this problem in the form of the Bernoulli filter for interval measurements, implemented as a particle filter. The numerical results demonstrate the filter performance: it detects the presence of targets reliably, and using a sufficient number of particles, the support of its posterior spatial PDF is guaranteed to include the true target state.",
keywords = "Sequential Bayesian estimation, random sets, particle filters, Bernoulli filter, interval measurements",
author = "B Ristic and Amadou Gning and Lyudmila Mihaylova",
note = "IEEE Catalog Number: CFP11FUS-CDR; International Conference on Information Fusion ; Conference date: 05-07-2011 Through 08-07-2011",
year = "2011",
month = jul,
day = "5",
language = "English",
pages = "1069--1076",
booktitle = "2011 Proceedings of the 14th International Conference on Information Fusion (FUSION 2011)",

}

RIS

TY - GEN

T1 - Nonlinear filtering using measurements affected by stochastic, set-theoretic and association uncertainty

AU - Ristic, B

AU - Gning, Amadou

AU - Mihaylova, Lyudmila

N1 - IEEE Catalog Number: CFP11FUS-CDR

PY - 2011/7/5

Y1 - 2011/7/5

N2 - The problem is sequential Bayesian detection and estimation of nonlinear dynamic stochastic systems using measurements affected by three sources of uncertainty: stochastic, set-theoretic and data association uncertainty. Following Mahler’s framework for information fusion, the paper develops the optimal Bayes filter for this problem in the form of the Bernoulli filter for interval measurements, implemented as a particle filter. The numerical results demonstrate the filter performance: it detects the presence of targets reliably, and using a sufficient number of particles, the support of its posterior spatial PDF is guaranteed to include the true target state.

AB - The problem is sequential Bayesian detection and estimation of nonlinear dynamic stochastic systems using measurements affected by three sources of uncertainty: stochastic, set-theoretic and data association uncertainty. Following Mahler’s framework for information fusion, the paper develops the optimal Bayes filter for this problem in the form of the Bernoulli filter for interval measurements, implemented as a particle filter. The numerical results demonstrate the filter performance: it detects the presence of targets reliably, and using a sufficient number of particles, the support of its posterior spatial PDF is guaranteed to include the true target state.

KW - Sequential Bayesian estimation

KW - random sets

KW - particle filters

KW - Bernoulli filter

KW - interval measurements

M3 - Conference contribution/Paper

SP - 1069

EP - 1076

BT - 2011 Proceedings of the 14th International Conference on Information Fusion (FUSION 2011)

CY - USA

T2 - International Conference on Information Fusion

Y2 - 5 July 2011 through 8 July 2011

ER -