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Mobile tracking and parameter learning in unknown non-line-of-sight conditions

Tutkimustuotosvertaisarvioitu

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Mobile tracking and parameter learning in unknown non-line-of-sight conditions. / Liang, Chen; Piche, Robert.

13th International Conference on Information Fusion, 26-29 July 2010, EICC, Edinburgh, UK. 2010. s. 1-6.

Tutkimustuotosvertaisarvioitu

Harvard

Liang, C & Piche, R 2010, Mobile tracking and parameter learning in unknown non-line-of-sight conditions. julkaisussa 13th International Conference on Information Fusion, 26-29 July 2010, EICC, Edinburgh, UK. Sivut 1-6.

APA

Liang, C., & Piche, R. (2010). Mobile tracking and parameter learning in unknown non-line-of-sight conditions. teoksessa 13th International Conference on Information Fusion, 26-29 July 2010, EICC, Edinburgh, UK (Sivut 1-6)

Vancouver

Liang C, Piche R. Mobile tracking and parameter learning in unknown non-line-of-sight conditions. julkaisussa 13th International Conference on Information Fusion, 26-29 July 2010, EICC, Edinburgh, UK. 2010. s. 1-6

Author

Liang, Chen ; Piche, Robert. / Mobile tracking and parameter learning in unknown non-line-of-sight conditions. 13th International Conference on Information Fusion, 26-29 July 2010, EICC, Edinburgh, UK. 2010. Sivut 1-6

Bibtex - Lataa

@inproceedings{14db8691f9714c6c9b1a8179db3357fa,
title = "Mobile tracking and parameter learning in unknown non-line-of-sight conditions",
abstract = "This paper studies the mobile tracking problem in mixed line-of-sight (LOS) and non-line-of-sight (NLOS) conditions, where the statistics of NLOS error is Gaussian with fixed but unknown mean and variance. A Rao-Blackwellized particle filtering and parameter learning method (RBPF-PL) is proposed, in which the particle filtering with optimal trial distribution is first applied to estimate the posterior density of sight conditions, then the decentralized extended Kalman filter (EKF) is used to estimate the mobile state. In the parameter learning step, using the conjugate prior distribution on the unknown parameters, the posterior distribution of unknown parameters is further updated according to the sufficient statistics. Simulation results show the RBPF-PL method is effective to infer the unknown NLOS parameter and could achieve good tracking performance using small number of particles.",
author = "Chen Liang and Robert Piche",
note = "Contribution: organisation=mat,FACT1=1",
year = "2010",
language = "English",
isbn = "978-0-9824438-1-1",
pages = "1--6",
booktitle = "13th International Conference on Information Fusion, 26-29 July 2010, EICC, Edinburgh, UK",

}

RIS (suitable for import to EndNote) - Lataa

TY - GEN

T1 - Mobile tracking and parameter learning in unknown non-line-of-sight conditions

AU - Liang, Chen

AU - Piche, Robert

N1 - Contribution: organisation=mat,FACT1=1

PY - 2010

Y1 - 2010

N2 - This paper studies the mobile tracking problem in mixed line-of-sight (LOS) and non-line-of-sight (NLOS) conditions, where the statistics of NLOS error is Gaussian with fixed but unknown mean and variance. A Rao-Blackwellized particle filtering and parameter learning method (RBPF-PL) is proposed, in which the particle filtering with optimal trial distribution is first applied to estimate the posterior density of sight conditions, then the decentralized extended Kalman filter (EKF) is used to estimate the mobile state. In the parameter learning step, using the conjugate prior distribution on the unknown parameters, the posterior distribution of unknown parameters is further updated according to the sufficient statistics. Simulation results show the RBPF-PL method is effective to infer the unknown NLOS parameter and could achieve good tracking performance using small number of particles.

AB - This paper studies the mobile tracking problem in mixed line-of-sight (LOS) and non-line-of-sight (NLOS) conditions, where the statistics of NLOS error is Gaussian with fixed but unknown mean and variance. A Rao-Blackwellized particle filtering and parameter learning method (RBPF-PL) is proposed, in which the particle filtering with optimal trial distribution is first applied to estimate the posterior density of sight conditions, then the decentralized extended Kalman filter (EKF) is used to estimate the mobile state. In the parameter learning step, using the conjugate prior distribution on the unknown parameters, the posterior distribution of unknown parameters is further updated according to the sufficient statistics. Simulation results show the RBPF-PL method is effective to infer the unknown NLOS parameter and could achieve good tracking performance using small number of particles.

M3 - Conference contribution

SN - 978-0-9824438-1-1

SP - 1

EP - 6

BT - 13th International Conference on Information Fusion, 26-29 July 2010, EICC, Edinburgh, UK

ER -