Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/630074
Title: A Multi-Mode Dead Reckoning System for Pedestrian Tracking Using Smartphones
Authors: Qinglin Tian;Zoran Salcic;Kevin I-Kai Wang;Yun Pan
subject: smartphone|mode-awareness|realtime tracking|pedestrian dead reckoning|light-weight positioning algorithm
Year: 2016
Publisher: IEEE
Abstract: This paper proposes an approach for pedestrian tracking using dead reckoning enhanced with a mode detection using a standard smartphone. The mode represents a specific state of carrying device, and it is automatically detected while a person is walking. This paper presents a new approach, which extends and enhances previous methods by identifying in real-time three typical modes of carrying the device and using the identified mode to enhance tracking accuracy. The way of carrying the device in all modes is unconstrained to offer reliable person-independent tracking. Based on the identification of modes, a lightweight step-based tracking algorithm is developed with a novel step length estimation model. The tracking system is implemented on a commercial off-the-shelf smartphone equipped with a built-in inertial measurement unit with 3-D accelerometer and gyroscope. It achieves real-time tracking and localization performance with an average position accuracy of 98.91%.
URI: http://localhost/handle/Hannan/164716
http://localhost/handle/Hannan/630074
ISSN: 1530-437X
1558-1748
volume: 16
issue: 7
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7360882.pdf4.49 MBAdobe PDFThumbnail
Preview File
Title: A Multi-Mode Dead Reckoning System for Pedestrian Tracking Using Smartphones
Authors: Qinglin Tian;Zoran Salcic;Kevin I-Kai Wang;Yun Pan
subject: smartphone|mode-awareness|realtime tracking|pedestrian dead reckoning|light-weight positioning algorithm
Year: 2016
Publisher: IEEE
Abstract: This paper proposes an approach for pedestrian tracking using dead reckoning enhanced with a mode detection using a standard smartphone. The mode represents a specific state of carrying device, and it is automatically detected while a person is walking. This paper presents a new approach, which extends and enhances previous methods by identifying in real-time three typical modes of carrying the device and using the identified mode to enhance tracking accuracy. The way of carrying the device in all modes is unconstrained to offer reliable person-independent tracking. Based on the identification of modes, a lightweight step-based tracking algorithm is developed with a novel step length estimation model. The tracking system is implemented on a commercial off-the-shelf smartphone equipped with a built-in inertial measurement unit with 3-D accelerometer and gyroscope. It achieves real-time tracking and localization performance with an average position accuracy of 98.91%.
URI: http://localhost/handle/Hannan/164716
http://localhost/handle/Hannan/630074
ISSN: 1530-437X
1558-1748
volume: 16
issue: 7
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7360882.pdf4.49 MBAdobe PDFThumbnail
Preview File
Title: A Multi-Mode Dead Reckoning System for Pedestrian Tracking Using Smartphones
Authors: Qinglin Tian;Zoran Salcic;Kevin I-Kai Wang;Yun Pan
subject: smartphone|mode-awareness|realtime tracking|pedestrian dead reckoning|light-weight positioning algorithm
Year: 2016
Publisher: IEEE
Abstract: This paper proposes an approach for pedestrian tracking using dead reckoning enhanced with a mode detection using a standard smartphone. The mode represents a specific state of carrying device, and it is automatically detected while a person is walking. This paper presents a new approach, which extends and enhances previous methods by identifying in real-time three typical modes of carrying the device and using the identified mode to enhance tracking accuracy. The way of carrying the device in all modes is unconstrained to offer reliable person-independent tracking. Based on the identification of modes, a lightweight step-based tracking algorithm is developed with a novel step length estimation model. The tracking system is implemented on a commercial off-the-shelf smartphone equipped with a built-in inertial measurement unit with 3-D accelerometer and gyroscope. It achieves real-time tracking and localization performance with an average position accuracy of 98.91%.
URI: http://localhost/handle/Hannan/164716
http://localhost/handle/Hannan/630074
ISSN: 1530-437X
1558-1748
volume: 16
issue: 7
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7360882.pdf4.49 MBAdobe PDFThumbnail
Preview File