Dynamic Multipath Estimation by Sequential Monte Carlo Methods

Michael Lentmaier; Bernhard Krach; Thanawat Thiasiriphet

Dynamic Multipath Estimation by Sequential Monte Carlo Methods

Michael Lentmaier, Bernhard Krach, Thanawat Thiasiriphet

External Organization - Unknown

Forskningsoutput: Konferensbidrag › Konferenspaper, ej i proceeding/ej förlagsutgivet › Peer review

Sammanfattning

: A sequential Bayesian estimation algorithm for multipath mitigation is presented, with an underlying movement model that is especially designed for dynamic channel scenarios. In order to facilitate efficient integration into receiver tracking loops it builds upon complexity reduction concepts that previously have been applied within Maximum Likelihood (ML) estimators. To demonstrate its capabilities under different GNSS signal conditions, simulation results are presented for both artificially generated random channels and high resolution channel impulse responses recorded during a measurement campaign.

Originalspråk	engelska
Sidor	1712-1721
Status	Published - 2007
Externt publicerad	Ja
Evenemang	International Technical Meeting of the Institute of Navigation Satellite Division, (ION GNSS), 2007 - Forth Worth, TX, USA Varaktighet: 2007 sep. 25 → 2007 sep. 28

Konferens

Konferens	International Technical Meeting of the Institute of Navigation Satellite Division, (ION GNSS), 2007
Land/Territorium	USA
Ort	Forth Worth, TX
Period	2007/09/25 → 2007/09/28

Ämnesklassifikation (UKÄ)

Elektroteknik och elektronik

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Link to publication in Scopus

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@conference{06064b18b8da4d358692ce7bcd58cc3d,

title = "Dynamic Multipath Estimation by Sequential Monte Carlo Methods",

abstract = ": A sequential Bayesian estimation algorithm for multipath mitigation is presented, with an underlying movement model that is especially designed for dynamic channel scenarios. In order to facilitate efficient integration into receiver tracking loops it builds upon complexity reduction concepts that previously have been applied within Maximum Likelihood (ML) estimators. To demonstrate its capabilities under different GNSS signal conditions, simulation results are presented for both artificially generated random channels and high resolution channel impulse responses recorded during a measurement campaign.",

keywords = "GNSS, positioning, multipath mitigation",

author = "Michael Lentmaier and Bernhard Krach and Thanawat Thiasiriphet",

year = "2007",

language = "English",

pages = "1712--1721",

note = "International Technical Meeting of the Institute of Navigation Satellite Division, (ION GNSS), 2007 ; Conference date: 25-09-2007 Through 28-09-2007",

}

Dynamic Multipath Estimation by Sequential Monte Carlo Methods. / Lentmaier, Michael; Krach, Bernhard; Thiasiriphet, Thanawat.
2007. 1712-1721 Artikel presenterad vid International Technical Meeting of the Institute of Navigation Satellite Division, (ION GNSS), 2007, Forth Worth, TX, USA.

Forskningsoutput: Konferensbidrag › Konferenspaper, ej i proceeding/ej förlagsutgivet › Peer review

TY - CONF

T1 - Dynamic Multipath Estimation by Sequential Monte Carlo Methods

AU - Lentmaier, Michael

AU - Krach, Bernhard

AU - Thiasiriphet, Thanawat

PY - 2007

Y1 - 2007

N2 - : A sequential Bayesian estimation algorithm for multipath mitigation is presented, with an underlying movement model that is especially designed for dynamic channel scenarios. In order to facilitate efficient integration into receiver tracking loops it builds upon complexity reduction concepts that previously have been applied within Maximum Likelihood (ML) estimators. To demonstrate its capabilities under different GNSS signal conditions, simulation results are presented for both artificially generated random channels and high resolution channel impulse responses recorded during a measurement campaign.

AB - : A sequential Bayesian estimation algorithm for multipath mitigation is presented, with an underlying movement model that is especially designed for dynamic channel scenarios. In order to facilitate efficient integration into receiver tracking loops it builds upon complexity reduction concepts that previously have been applied within Maximum Likelihood (ML) estimators. To demonstrate its capabilities under different GNSS signal conditions, simulation results are presented for both artificially generated random channels and high resolution channel impulse responses recorded during a measurement campaign.

KW - GNSS

KW - positioning

KW - multipath mitigation

UR - https://www.scopus.com/pages/publications/58449134194

M3 - Paper, not in proceeding

SP - 1712

EP - 1721

T2 - International Technical Meeting of the Institute of Navigation Satellite Division, (ION GNSS), 2007

Y2 - 25 September 2007 through 28 September 2007

ER -

Dynamic Multipath Estimation by Sequential Monte Carlo Methods

Sammanfattning

Konferens

Ämnesklassifikation (UKÄ)

Andra filer och länkar

Fingeravtryck

Citera det här