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Kalman filter with a linear state model for PDR+WLAN positioning and its application to assisting a particle filter

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Kalman filter with a linear state model for PDR+WLAN positioning and its application to assisting a particle filter. / Raitoharju, Matti; Nurminen, Henri; Piché, Robert.

julkaisussa: Eurasip Journal on Advances in Signal Processing, Vuosikerta 2015, Nro 1, 33, 01.12.2015.

Tutkimustuotosvertaisarvioitu

Harvard

Raitoharju, M, Nurminen, H & Piché, R 2015, 'Kalman filter with a linear state model for PDR+WLAN positioning and its application to assisting a particle filter' Eurasip Journal on Advances in Signal Processing, Vuosikerta. 2015, Nro 1, 33. https://doi.org/10.1186/s13634-015-0216-z

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Author

Raitoharju, Matti ; Nurminen, Henri ; Piché, Robert. / Kalman filter with a linear state model for PDR+WLAN positioning and its application to assisting a particle filter. Julkaisussa: Eurasip Journal on Advances in Signal Processing. 2015 ; Vuosikerta 2015, Nro 1.

Bibtex - Lataa

@article{9242726304714bc3bfb784b406720f00,
title = "Kalman filter with a linear state model for PDR+WLAN positioning and its application to assisting a particle filter",
abstract = "Indoor positioning based on wireless local area network (WLAN) signals is often enhanced using pedestrian dead reckoning (PDR) based on an inertial measurement unit. The state evolution model in PDR is usually nonlinear. We present a new linear state evolution model for PDR. In simulated-data and real-data tests of tightly coupled WLAN-PDR positioning, the positioning accuracy with this linear model is better than with the traditional models when the initial heading is not known, which is a common situation. The proposed method is computationally light and is also suitable for smoothing. Furthermore, we present modifications to WLAN positioning based on Gaussian coverage areas and show how a Kalman filter using the proposed model can be used for integrity monitoring and (re)initialization of a particle filter.",
keywords = "Computational modeling, Indoor positioning, Pedestrian dead reckoning, Wireless LAN",
author = "Matti Raitoharju and Henri Nurminen and Robert Pich{\'e}",
year = "2015",
month = "12",
day = "1",
doi = "10.1186/s13634-015-0216-z",
language = "English",
volume = "2015",
journal = "Eurasip Journal on Advances in Signal Processing",
issn = "1687-6172",
publisher = "Springer International Publishing AG",
number = "1",

}

RIS (suitable for import to EndNote) - Lataa

TY - JOUR

T1 - Kalman filter with a linear state model for PDR+WLAN positioning and its application to assisting a particle filter

AU - Raitoharju, Matti

AU - Nurminen, Henri

AU - Piché, Robert

PY - 2015/12/1

Y1 - 2015/12/1

N2 - Indoor positioning based on wireless local area network (WLAN) signals is often enhanced using pedestrian dead reckoning (PDR) based on an inertial measurement unit. The state evolution model in PDR is usually nonlinear. We present a new linear state evolution model for PDR. In simulated-data and real-data tests of tightly coupled WLAN-PDR positioning, the positioning accuracy with this linear model is better than with the traditional models when the initial heading is not known, which is a common situation. The proposed method is computationally light and is also suitable for smoothing. Furthermore, we present modifications to WLAN positioning based on Gaussian coverage areas and show how a Kalman filter using the proposed model can be used for integrity monitoring and (re)initialization of a particle filter.

AB - Indoor positioning based on wireless local area network (WLAN) signals is often enhanced using pedestrian dead reckoning (PDR) based on an inertial measurement unit. The state evolution model in PDR is usually nonlinear. We present a new linear state evolution model for PDR. In simulated-data and real-data tests of tightly coupled WLAN-PDR positioning, the positioning accuracy with this linear model is better than with the traditional models when the initial heading is not known, which is a common situation. The proposed method is computationally light and is also suitable for smoothing. Furthermore, we present modifications to WLAN positioning based on Gaussian coverage areas and show how a Kalman filter using the proposed model can be used for integrity monitoring and (re)initialization of a particle filter.

KW - Computational modeling

KW - Indoor positioning

KW - Pedestrian dead reckoning

KW - Wireless LAN

UR - http://www.scopus.com/inward/record.url?scp=84928397748&partnerID=8YFLogxK

U2 - 10.1186/s13634-015-0216-z

DO - 10.1186/s13634-015-0216-z

M3 - Article

VL - 2015

JO - Eurasip Journal on Advances in Signal Processing

JF - Eurasip Journal on Advances in Signal Processing

SN - 1687-6172

IS - 1

M1 - 33

ER -