Robust pedestrian localization in indoor environments with an IMU aided TDoA system

Pedestrian localization systems require the knowledge of a user's position for manifold applications in indoor and outdoor environments. For this purpose several methods can be used, such as a Global Navigation Satellite System (GNSS). Since GNSS are not available in indoor environments or deep street canyons other techniques have to be used. This could be a time based or signal strength based radio localization system. In this paper a Time Difference of Arrival (TDoA) system is used and combined with a low cost accelerometer and gyroscope. Since the localization device is free mountable at the user's body, the raw data of the gyroscope must be rotated in a global frame. Therefore the accelerometer is used to compute the rotation angles in relation to the earth's gravity. The data of the accelerometer is also used for a step detection and a step length estimation as well. To fuse the different measurements an Extended Kalman Filter (EKF) is employed. While the system is initialized the orientation of the user is not available. Therefore an initial phase is prepended where a reduced model is used. In this phase the orientation has to develop while the user is moving. The duration of the phase is dynamic, depending on the quality of the TDoA measurements. Once the initial phase is passed the complete model is used. To evaluate the introduced algorithm experimental results in different environments are presented.

[1]  T. Kaiser,et al.  Hybrid localization using UWB and inertial sensors , 2008, 2008 IEEE International Conference on Ultra-Wideband.

[2]  A. Jazwinski Stochastic Processes and Filtering Theory , 1970 .

[3]  W. Niemeier,et al.  Set-up of a combined indoor and outdoor positioning solution and experimental results , 2010, 2010 International Conference on Indoor Positioning and Indoor Navigation.

[4]  Reiner S. Thomä,et al.  Performance comparison of TOA and TDOA based location estimation algorithms in LOS environment , 2008, 2008 5th Workshop on Positioning, Navigation and Communication.

[5]  Christian Wietfeld,et al.  A comprehensive approach for optimizing ToA-localization in harsh industrial environments , 2010, IEEE/ION Position, Location and Navigation Symposium.

[6]  T. Başar,et al.  A New Approach to Linear Filtering and Prediction Problems , 2001 .

[7]  H. Weinberg Using the ADXL202 in Pedometer and Personal Navigation Applications , 2002 .

[8]  Christof Röhrig,et al.  Localization of Autonomous Mobile Robots in a Cellular Transport System , 2012 .

[9]  R.L. Moses,et al.  Locating the nodes: cooperative localization in wireless sensor networks , 2005, IEEE Signal Processing Magazine.

[10]  Lucas Paletta,et al.  Multisensor data fusion for high accuracy positioning on mobile phones , 2010, Mobile HCI.

[11]  Koichi Kurumatani,et al.  ZigBee based indoor localization with particle filter estimation , 2010, 2010 IEEE International Conference on Systems, Man and Cybernetics.

[12]  Henry Been-Lirn Duh,et al.  A low-cost motion tracker and its error analysis , 2008, 2008 IEEE International Conference on Robotics and Automation.

[13]  M. E. Cannon,et al.  Integrated GPS/INS System for Pedestrian Navigation in a Signal Degraded Environment , 2006 .

[14]  Wilhelm Stork,et al.  An approach to infrastructure-independent person localization with an IEEE 802.15.4 WSN , 2010, 2010 International Conference on Indoor Positioning and Indoor Navigation.

[15]  M. Muller,et al.  Pedestrian localization using IEEE 802.15.4a TDoA Wireless Sensor Network , 2012, 2012 IEEE 1st International Symposium on Wireless Systems (IDAACS-SWS).

[16]  Kechu Yi,et al.  EKF localization based on TDOA/RSS in underground mines using UWB ranging , 2011, 2011 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC).

[17]  Harald Sternberg,et al.  Pedestrian smartphone-based indoor navigation using ultra portable sensory equipment , 2010, 2010 International Conference on Indoor Positioning and Indoor Navigation.