Pedestrian navigation in harsh environments using wireless and inertial measurements

The popularity of positioning satellite systems in open areas has led to a high demand for systems suitable for harsh environments, where the former fail. Wireless localization and inertial navigation have emerged as the most valuable alternatives to offer positioning in such environments. However, the characteristics of the wireless propagation channel in harsh environments are dynamic and unpredictable while the errors in inertial navigation rapidly increase with time. In this paper, we present a general framework and algorithms for data fusion in navigation systems. The presented techniques combine both wireless and inertial measurements. To assess the proposed methods, we collected measurements from commercial wireless devices and low-cost inertial sensors in a real indoor environment. The experimental results show the remarkable performance of the proposed method, capable of reaching a sub-meter accuracy.

[1]  Tamal Mukherjee,et al.  A Shoe-Embedded RF Sensor for Motion Detection , 2011, IEEE Microwave and Wireless Components Letters.

[2]  Fernando Seco Granja,et al.  Accurate Pedestrian Indoor Navigation by Tightly Coupling Foot-Mounted IMU and RFID Measurements , 2012, IEEE Transactions on Instrumentation and Measurement.

[3]  Yuanxin Wu,et al.  A Critique on "On Finite Rotations and the Noncommutativity Rate Vector" , 2012, IEEE Trans. Aerosp. Electron. Syst..

[4]  Tamal Mukherjee,et al.  A Low-Power Shoe-Embedded Radar for Aiding Pedestrian Inertial Navigation , 2010, IEEE Transactions on Microwave Theory and Techniques.

[5]  D. Titterton,et al.  Strapdown inertial navigation technology - 2nd edition - [Book review] , 2005, IEEE Aerospace and Electronic Systems Magazine.

[6]  Hugh F. Durrant-Whyte,et al.  A new method for the nonlinear transformation of means and covariances in filters and estimators , 2000, IEEE Trans. Autom. Control..

[7]  Santiago Mazuelas,et al.  Self-calibration of TOA/distance relationship for wireless localization in harsh environments , 2012, 2012 IEEE International Conference on Communications (ICC).

[8]  J. Bortz.  A New Mathematical Formulation for Strapdown Inertial Navigation , 1971, IEEE Transactions on Aerospace and Electronic Systems.

[9]  M. Shuster.  A survey of attitude representation , 1993 .

[10]  Thia Kirubarajan,et al.  Estimation with Applications to Tracking and Navigation: Theory, Algorithms and Software , 2001 .

[11]  Santiago Mazuelas,et al.  IEEE 802.11 Distance Estimation Based on RTS/CTS Two-Frame Exchange Mechanism , 2009, VTC Spring 2009 - IEEE 69th Vehicular Technology Conference.

[12]  Axel Küpper.  Location-based Services: Fundamentals and Operation , 2005 .

[13]  Hans-Werner Gellersen,et al.  Location and Navigation Support for Emergency Responders: A Survey , 2010, IEEE Pervasive Computing.

[14]  J. Borenstein,et al.  Non-GPS Navigation for Security Personnel and First Responders , 2007, Journal of Navigation.

[15]  James R. Wertz,et al.  Spacecraft attitude determination and control , 1978 .

[16]  Moe Z. Win,et al.  Network localization and navigation via cooperation , 2011, IEEE Communications Magazine.

[17]  P. Groves.  Principles of GNSS, Inertial, and Multisensor Integrated Navigation Systems, Second Edition , 2013 .

[18]  E. J. Lefferts,et al.  Kalman Filtering for Spacecraft Attitude Estimation , 1982 .

[19]  Eric Foxlin,et al.  Pedestrian tracking with shoe-mounted inertial sensors , 2005, IEEE Computer Graphics and Applications.

[20]  Robert Harle,et al.  Pedestrian localisation for indoor environments , 2008, UbiComp.

[21]  Yuanxin Wu,et al.  Observability of Strapdown INS Alignment: A Global Perspective , 2011, IEEE Transactions on Aerospace and Electronic Systems.

[22]  Fernando Seco Granja,et al.  Indoor pedestrian navigation using an INS/EKF framework for yaw drift reduction and a foot-mounted IMU , 2010, 2010 7th Workshop on Positioning, Navigation and Communication.

[23]  Fredrik Gustafsson,et al.  Mobile Positioning Using Wireless Networks , 2005 .

[24]  Santiago Mazuelas,et al.  Accurate and Integrated Localization System for Indoor Environments Based on IEEE 802.11 Round-Trip Time Measurements , 2010, EURASIP J. Wirel. Commun. Netw..

[25]  Hui Fang,et al.  Design of a wireless assisted pedestrian dead reckoning system - the NavMote experience , 2005, IEEE Transactions on Instrumentation and Measurement.

[26]  A. Bahillo,et al.  NLOS mitigation based on range estimation error characterization in an RTT-based IEEE 802.11, indoor location system , 2009, 2009 IEEE International Symposium on Intelligent Signal Processing.

[27]  F. Gustafsson,et al.  Mobile positioning using wireless networks: possibilities and fundamental limitations based on available wireless network measurements , 2005, IEEE Signal Processing Magazine.

[28]  Branko Ristic,et al.  Beyond the Kalman Filter: Particle Filters for Tracking Applications , 2004 .

[29]  John Weston,et al.  Strapdown Inertial Navigation Technology, Second Edition , 2005 .

[30]  Hisashi Kobayashi,et al.  Analysis of wireless geolocation in a non-line-of-sight environment , 2006, IEEE Transactions on Wireless Communications.

[31]  Konstantinos N. Plataniotis,et al.  Intelligent Dynamic Radio Tracking in Indoor Wireless Local Area Networks , 2010, IEEE Transactions on Mobile Computing.

[32]  Santiago Mazuelas,et al.  Adaptive Data Fusion for Wireless Localization in Harsh Environments , 2012, IEEE Transactions on Signal Processing.

[33]  S. Affes,et al.  Radio Wave Characterization and Modeling in Underground Mine Tunnels , 2008, IEEE Transactions on Antennas and Propagation.

[34]  Mongi A. Abidi,et al.  Pose and motion estimation from vision using dual quaternion-based extended kalman filtering , 1997 .

[35]  B. Friedland.  Analysis Strapdown Navigation Using Quaternions , 1978, IEEE Transactions on Aerospace and Electronic Systems.

[36]  Santiago Mazuelas,et al.  Robust Indoor Positioning Provided by Real-Time RSSI Values in Unmodified WLAN Networks , 2009, IEEE Journal of Selected Topics in Signal Processing.