Distortion Rejecting Magneto−inductive 3−D Localization (MagLoc)

Localization is a research area that, due to its overarching importance as an enabler for higher level services, has attracted a vast amount of research and commercial interest. For the most part, it can be claimed that GPS provides an unparalleled solution for outdoor tracking and navigation. However, the same cannot yet be said about positioning in GPSdenied or challenged environments, such as indoor environments, where obstructions such as floors and walls heavily attenuate or reflect high frequency radio signals. This has led to a plethora of competing solutions targeted towards a particular application scenario, yielding a fragmented solution landscape. In this paper, we present a fresh approach to 3-D positioning based on the use of very low frequency (kHz) magneto-inductive (MI) fields. The most important property of MI positioning is that obstacles like walls, floors and people that heavily impact the performance of competing approaches are largely “transparent” to the quasi-static magnetic fields. MI has a number of challenges to robust operation that distort positions, including the presence of ferrous materials and sensitivity to user rotation. Through signal processing and sensor fusion across multiple system layers, we show how we can overcome these challenges. We showcase its highly accurate 3-D positioning in a number of environments, with positioning accuracy below 0.8 m even in heavily distorted areas.

[1]  Agathoniki Trigoni,et al.  Encounter based sensor tracking , 2012, MobiHoc '12.

[2]  Valérie Renaudin,et al.  Step Length Estimation Using Handheld Inertial Sensors , 2012, Sensors.

[3]  Patrick Robertson,et al.  Inertial-Based Joint Mapping and Positioning for Pedestrian Navigation , 2010 .

[4]  J. Vanfleteren,et al.  3D orientation tracking based on unscented Kalman filtering of accelerometer and magnetometer data , 2009, 2009 IEEE Sensors Applications Symposium.

[5]  Johnson I. Agbinya Principles of Inductive Near Field Communications for Internet of Things , 2011 .

[6]  Christian Hoene,et al.  Measuring Round Trip Times to Determine the Distance Between WLAN Nodes , 2005, NETWORKING.

[7]  Jie Liu,et al.  Design and evaluation of a wireless magnetic-based proximity detection platform for indoor applications , 2012, IPSN '12.

[8]  Jack B. Kuipers,et al.  Quaternions and Rotation Sequences: A Primer with Applications to Orbits, Aerospace and Virtual Reality , 2002 .

[9]  Feng Zhao,et al.  A reliable and accurate indoor localization method using phone inertial sensors , 2012, UbiComp.

[10]  Young Soo Suh Orientation Estimation Using a Quaternion-Based Indirect Kalman Filter With Adaptive Estimation of External Acceleration , 2010, IEEE Transactions on Instrumentation and Measurement.

[11]  Paul Lukowicz,et al.  Robust, low cost indoor positioning using magnetic resonant coupling , 2012, UbiComp.

[12]  Thorsten Vaupel,et al.  A Hidden Markov Model for urban navigation based on fingerprinting and pedestrian dead reckoning , 2010, 2010 13th International Conference on Information Fusion.

[13]  Neil D. Lawrence,et al.  WiFi-SLAM Using Gaussian Process Latent Variable Models , 2007, IJCAI.

[14]  Xenofon D. Koutsoukos,et al.  Radio Interferometric Angle of Arrival Estimation , 2010, EWSN.

[15]  Agathoniki Trigoni,et al.  On Assessing the Accuracy of Positioning Systems in Indoor Environments , 2013, EWSN.

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

[17]  F. Seco,et al.  A comparison of Pedestrian Dead-Reckoning algorithms using a low-cost MEMS IMU , 2009, 2009 IEEE International Symposium on Intelligent Signal Processing.

[18]  Romit Roy Choudhury,et al.  Did you see Bob?: human localization using mobile phones , 2010, MobiCom.

[19]  Anshul Rai,et al.  Zee: zero-effort crowdsourcing for indoor localization , 2012, Mobicom '12.

[20]  John Krumm,et al.  Hidden Markov map matching through noise and sparseness , 2009, GIS.

[21]  Cheng Chen,et al.  INS/Wi-Fi based indoor navigation using adaptive Kalman filtering and vehicle constraints , 2012, 2012 9th Workshop on Positioning, Navigation and Communication.

[22]  Niki Trigoni,et al.  Magneto-Inductive NEtworked Rescue System (MINERS): Taking sensor networks underground , 2012, 2012 ACM/IEEE 11th International Conference on Information Processing in Sensor Networks (IPSN).

[23]  Koichi Ozaki,et al.  Odometry correction with localization based on landmarkless magnetic map for navigation system of indoor mobile robot , 2000, 2009 4th International Conference on Autonomous Robots and Agents.

[24]  D. Savitz,et al.  INTERNATIONAL COMMISSION ON NON-IONIZING RADIATION PROTECTION , 2011 .

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

[26]  Romit Roy Choudhury,et al.  SurroundSense: mobile phone localization via ambience fingerprinting , 2009, MobiCom '09.

[27]  Visa Koivunen,et al.  Time Synchronization and Ranging in OFDM Systems Using Time-Reversal , 2013, IEEE Transactions on Instrumentation and Measurement.

[28]  Moustafa Youssef,et al.  The Horus WLAN location determination system , 2005, MobiSys '05.

[29]  Francis C. M. Lau,et al.  Influential factors for decimetre level positioning using ultra wide band technology , 2012 .

[30]  Mikkel Baun Kjærgaard,et al.  Indoor location fingerprinting with heterogeneous clients , 2011, Pervasive Mob. Comput..

[31]  T. Kawamoto,et al.  Simple estimation of equivalent magnetic dipole moment to characterize ELF magnetic fields generated by electric appliances incorporating harmonics , 2001 .

[32]  Yin Chen,et al.  FM-based indoor localization , 2012, MobiSys '12.

[33]  Valérie Renaudin,et al.  Motion Mode Recognition and Step Detection Algorithms for Mobile Phone Users , 2013, Sensors.

[34]  Patrick Robertson,et al.  Magnetic maps of indoor environments for precise localization of legged and non-legged locomotion , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[35]  Neil J. Gordon,et al.  A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking , 2002, IEEE Trans. Signal Process..

[36]  Mohammed Khider,et al.  Improving Simultaneous Localization and Mapping for pedestrian navigation and automatic mapping of buildings by using online human-based feature labeling , 2010, IEEE/ION Position, Location and Navigation Symposium.

[37]  Agathoniki Trigoni,et al.  Lightweight map matching for indoor localisation using conditional random fields , 2014, IPSN-14 Proceedings of the 13th International Symposium on Information Processing in Sensor Networks.

[38]  S. Beauregard,et al.  Indoor PDR performance enhancement using minimal map information and particle filters , 2008, 2008 IEEE/ION Position, Location and Navigation Symposium.

[39]  Paramvir Bahl,et al.  RADAR: an in-building RF-based user location and tracking system , 2000, Proceedings IEEE INFOCOM 2000. Conference on Computer Communications. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies (Cat. No.00CH37064).

[40]  Agathoniki Trigoni,et al.  Revealing the hidden lives of underground animals using magneto-inductive tracking , 2010, SenSys '10.

[41]  Moustafa Youssef,et al.  No need to war-drive: unsupervised indoor localization , 2012, MobiSys '12.

[42]  Janne Haverinen,et al.  Global indoor self-localization based on the ambient magnetic field , 2009, Robotics Auton. Syst..

[43]  Sivan Toledo,et al.  VTrack: accurate, energy-aware road traffic delay estimation using mobile phones , 2009, SenSys '09.

[44]  Yunhao Liu,et al.  Locating in fingerprint space: wireless indoor localization with little human intervention , 2012, Mobicom '12.

[45]  Basel Kikhia,et al.  Optimal Placement of Accelerometers for the Detection of Everyday Activities , 2013, Sensors.

[46]  Prabal Dutta,et al.  AutoWitness: locating and tracking stolen property while tolerating GPS and radio outages , 2010, SenSys '10.

[47]  Arie Sheinker,et al.  Localization in 3-D Using Beacons of Low Frequency Magnetic Field , 2013, IEEE Transactions on Instrumentation and Measurement.