A NLOS-robust TOA positioning filter based on a skew-t measurement noise model

Henri Nurminen; Tohid Ardeshiri; Robert Piche; Fredrik Gustafsson

doi:10.1109/IPIN.2015.7346786

A NLOS-robust TOA positioning filter based on a skew-t measurement noise model

Henri Nurminen
, Tohid Ardeshiri
, Robert Piche
, Fredrik Gustafsson

Tutkimustuotos: Konferenssiartikkeli › Tieteellinen › vertaisarvioitu

35 Sitaatiot (Scopus)

161 Lataukset (Pure)

Abstrakti

A skew-t variational Bayes filter (STVBF) is applied to indoor positioning with time-of-arrival (TOA) based distance measurements and pedestrian dead reckoning (PDR). The proposed filter accommodates large positive outliers caused by occasional non-line-of-sight (NLOS) conditions by using a skew-t model of measurement errors. Real-data tests using the fusion of inertial sensors based PDR and ultra-wideband based TOA ranging show that the STVBF clearly outperforms the extended Kalman filter (EKF) in positioning accuracy with the computational complexity about three times that of the EKF.

Alkuperäiskieli	Englanti
Otsikko	2015 International Conference on Indoor Positioning and Indoor Navigation (IPIN)
Kustantaja	IEEE
Sivut	1-7
Sivumäärä	7
ISBN (painettu)	978-1-4673-8402-5
DOI - pysyväislinkit	https://doi.org/10.1109/IPIN.2015.7346786
Tila	Julkaistu - 1 lokak. 2015
OKM-julkaisutyyppi	A4 Artikkeli konferenssijulkaisussa
Tapahtuma	International Conference on Indoor Positioning and Indoor Navigation - Kesto: 1 tammik. 1900 → …

Conference

Conference	International Conference on Indoor Positioning and Indoor Navigation
Ajanjakso	1/01/00 → …

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10.1109/IPIN.2015.7346786

IPIN2015_postprint
(c) 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
Accepted author manuscript, 801 KB

http://urn.fi/URN:NBN:fi:tty-201603183702

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title = "A NLOS-robust TOA positioning filter based on a skew-t measurement noise model",

abstract = "A skew-t variational Bayes filter (STVBF) is applied to indoor positioning with time-of-arrival (TOA) based distance measurements and pedestrian dead reckoning (PDR). The proposed filter accommodates large positive outliers caused by occasional non-line-of-sight (NLOS) conditions by using a skew-t model of measurement errors. Real-data tests using the fusion of inertial sensors based PDR and ultra-wideband based TOA ranging show that the STVBF clearly outperforms the extended Kalman filter (EKF) in positioning accuracy with the computational complexity about three times that of the EKF.",

keywords = "Approximation methods, Computational modeling, Distance measurement, Gaussian distribution, Measurement errors, Noise measurement, Position measurement, NLOS, TOA, UWB, indoor positioning, robust filtering, skew t, skewness, variational Bayes",

author = "Henri Nurminen and Tohid Ardeshiri and Robert Piche and Fredrik Gustafsson",

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doi = "10.1109/IPIN.2015.7346786",

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AU - Nurminen, Henri

AU - Ardeshiri, Tohid

AU - Piche, Robert

AU - Gustafsson, Fredrik

PY - 2015/10/1

Y1 - 2015/10/1

N2 - A skew-t variational Bayes filter (STVBF) is applied to indoor positioning with time-of-arrival (TOA) based distance measurements and pedestrian dead reckoning (PDR). The proposed filter accommodates large positive outliers caused by occasional non-line-of-sight (NLOS) conditions by using a skew-t model of measurement errors. Real-data tests using the fusion of inertial sensors based PDR and ultra-wideband based TOA ranging show that the STVBF clearly outperforms the extended Kalman filter (EKF) in positioning accuracy with the computational complexity about three times that of the EKF.

AB - A skew-t variational Bayes filter (STVBF) is applied to indoor positioning with time-of-arrival (TOA) based distance measurements and pedestrian dead reckoning (PDR). The proposed filter accommodates large positive outliers caused by occasional non-line-of-sight (NLOS) conditions by using a skew-t model of measurement errors. Real-data tests using the fusion of inertial sensors based PDR and ultra-wideband based TOA ranging show that the STVBF clearly outperforms the extended Kalman filter (EKF) in positioning accuracy with the computational complexity about three times that of the EKF.

KW - Approximation methods

KW - Computational modeling

KW - Distance measurement

KW - Gaussian distribution

KW - Measurement errors

KW - Noise measurement

KW - Position measurement

KW - NLOS

KW - TOA

KW - UWB

KW - indoor positioning

KW - robust filtering

KW - skew t

KW - skewness

KW - variational Bayes

U2 - 10.1109/IPIN.2015.7346786

DO - 10.1109/IPIN.2015.7346786

M3 - Conference contribution

SN - 978-1-4673-8402-5

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EP - 7

BT - 2015 International Conference on Indoor Positioning and Indoor Navigation (IPIN)

PB - IEEE

T2 - International Conference on Indoor Positioning and Indoor Navigation

Y2 - 1 January 1900

ER -

A NLOS-robust TOA positioning filter based on a skew-t measurement noise model

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