Indoor localization using magnetic fields

Indoor localization consists of locating oneself inside new buildings. GPS does not work indoors due to multipath reflection and signal blockage. WiFi based systems assume ubiquitous availability and infrastructure based systems require expensive installations, hence making indoor localization an open problem. This dissertation consists of solving the problem of indoor localization by thoroughly exploiting the indoor ambient magnetic fields comprising mainly of disturbances termed as anomalies in the Earth’s magnetic field caused by pillars, doors and elevators in hallways which are ferromagnetic in nature. By observing uniqueness in magnetic signatures collected from different campus buildings, the work presents the identification of landmarks and guideposts from these signatures and further develops magnetic maps of buildings - all of which can be used to locate and navigate people indoors. To understand the reason behind these anomalies, first a comparison between the measured and model generated Earth’s magnetic field is made, verifying the presence of a constant field without any disturbances. Then by modeling the magnetic field behavior of different pillars such as steel reinforced concrete, solid steel, and other structures like doors and elevators, the interaction of the Earth’s field with the ferromagnetic fields is described thereby explaining the causes of the uniqueness in the signatures that comprise these disturbances. Next, by employing the dynamic time warping algorithm to account for time differences in signatures obtained from users walking at different speeds, an indoor localization application capable of classifying locations using the magnetic signatures is developed solely on the smart phone. The application required users to walk short distances of 3-6 m anywhere in hallway to be located with accuracies of 80-99%. The classification framework was further validated with over 90% accuracies using model generated magnetic signatures representing hallways with different kinds of pillars, doors and elevators. All in all, this dissertation contributes the following: 1) provides a framework for understanding the presence of ambient magnetic fields indoors and utilizing them to solve the indoor localization problem; 2) develops an application that is independent of the user and the smart phones and 3) requires no other infrastructure since it is deployed on a device that encapsulates the sensing, computing and inferring functionalities, thereby making it a novel contribution to the mobile and pervasive computing domain.

[1]  Ben P. Milner,et al.  Environmental Noise Classification for Context-Aware Applications , 2003, DEXA.

[2]  Bofeng Zhang,et al.  Feature Selection Methods in Walking Stability Analysis , 2009, 2009 Ninth IEEE International Conference on Computer and Information Technology.

[3]  Stuart Crozier,et al.  Analysis and Measurements of Magnetic Field Exposures for Healthcare Workers in SelectedMR Environments , 2008, IEEE Transactions on Biomedical Engineering.

[4]  Henry A. Kautz,et al.  Indoor wayfinding: Developing a functional interface for individuals with cognitive impairments , 2008, Disability and rehabilitation. Assistive technology.

[5]  George J. Moridis,et al.  Ferrofluid flow for TOUGH2 , 1998 .

[6]  Liviu Iftode,et al.  Indoor Localization Using Camera Phones , 2006, Seventh IEEE Workshop on Mobile Computing Systems & Applications (WMCSA'06 Supplement).

[7]  James Llinas,et al.  An introduction to multisensor data fusion , 1997, Proc. IEEE.

[8]  Oliver Amft,et al.  LuxTrace: indoor positioning using building illumination , 2007, Personal and Ubiquitous Computing.

[9]  Georg Gartner,et al.  Applications of location–based services: a selected review , 2007, J. Locat. Based Serv..

[10]  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).

[11]  Salvatore Graziani,et al.  Multisensor Strategies to Assist Blind People: A Clear-Path Indicator , 2009, IEEE Transactions on Instrumentation and Measurement.

[12]  Vit Niennattrakul,et al.  On Clustering Multimedia Time Series Data Using K-Means and Dynamic Time Warping , 2007, 2007 International Conference on Multimedia and Ubiquitous Engineering (MUE'07).

[13]  Kavitha Muthukrishnan,et al.  Wireless campus LBS : building campus - wide location based services based on WiFi technology , 2006 .

[14]  Paul Kemppi,et al.  Use of Artificial Magnetic Anomalies in Indoor Pedestrian Navigation , 2010, 2010 IEEE 72nd Vehicular Technology Conference - Fall.

[15]  Simon J. Godsill,et al.  On sequential Monte Carlo sampling methods for Bayesian filtering , 2000, Stat. Comput..

[16]  Patrick Robertson,et al.  Integration of foot-mounted inertial sensors into a Bayesian location estimation framework , 2008, 2008 5th Workshop on Positioning, Navigation and Communication.

[17]  Chris Schmandt,et al.  Nomadic radio: speech and audio interaction for contextual messaging in nomadic environments , 2000, TCHI.

[18]  James Scott,et al.  Audio Location: Accurate Low-Cost Location Sensing , 2005, Pervasive.

[19]  B. Krach,et al.  Cascaded estimation architecture for integration of foot-mounted inertial sensors , 2008, 2008 IEEE/ION Position, Location and Navigation Symposium.

[20]  Mohammed Khider,et al.  Continuous location and direction estimation with multiple sensors using particle filtering , 2006, 2006 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems.

[21]  N. Takahashi,et al.  The analysis of magnetic disturbance due to buildings , 2003, Digest of INTERMAG 2003. International Magnetics Conference (Cat. No.03CH37401).

[22]  Sunita Ram,et al.  The people sensor: a mobility aid for the visually impaired , 1998, Digest of Papers. Second International Symposium on Wearable Computers (Cat. No.98EX215).

[23]  Romit Roy Choudhury,et al.  SurroundSense: mobile phone localization using ambient sound and light , 2009, MOCO.

[24]  Hari Balakrishnan,et al.  6th ACM/IEEE International Conference on on Mobile Computing and Networking (ACM MOBICOM ’00) The Cricket Location-Support System , 2022 .

[25]  Jing Liu,et al.  Survey of Wireless Indoor Positioning Techniques and Systems , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[26]  Danilo Navarro,et al.  Magnetic map building for mobile robot localization purpose , 2009, 2009 IEEE Conference on Emerging Technologies & Factory Automation.

[27]  S. Chiba,et al.  Dynamic programming algorithm optimization for spoken word recognition , 1978 .

[28]  G. Lachapelle,et al.  Assessment of Indoor Magnetic Field Anomalies using Multiple Magnetometers , 2010 .

[29]  Andy Hopper,et al.  The active badge location system , 1992, TOIS.

[30]  Marc O. Eberhard,et al.  Two-Dimensional Magnetic Algorithm to Detect Reinforcing Steel , 1995 .

[31]  Sim Heng Ong,et al.  Chromosome classification using dynamic time warping , 2008, Pattern Recognit. Lett..

[32]  Nathan Eagle,et al.  txteagle: Mobile Crowdsourcing , 2009, HCI.

[33]  A. Everett,et al.  Orientation and Mobility Techniques: A Guide for the Practitioner , 1976 .

[34]  Eyal de Lara,et al.  The SkyLoc Floor Localization System , 2007, Fifth Annual IEEE International Conference on Pervasive Computing and Communications (PerCom'07).

[35]  Wolfram Burgard,et al.  Mapping indoor environments based on human activity , 2010, 2010 IEEE International Conference on Robotics and Automation.

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

[37]  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.

[38]  Volkan Tuzcu,et al.  Dynamic time warping as a novel tool in pattern recognition of ECG changes in heart rhythm disturbances , 2005, 2005 IEEE International Conference on Systems, Man and Cybernetics.

[39]  J. Makhoul,et al.  Vector quantization in speech coding , 1985, Proceedings of the IEEE.

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

[41]  Juan-Luis Gorricho,et al.  Activity Recognition from Accelerometer Data on a Mobile Phone , 2009, IWANN.

[42]  Seth J. Teller,et al.  The cricket compass for context-aware mobile applications , 2001, MobiCom '01.

[43]  Dieter Fox,et al.  Bayesian Filtering for Location Estimation , 2003, IEEE Pervasive Comput..

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

[45]  Robert M. Gray,et al.  An Algorithm for Vector Quantizer Design , 1980, IEEE Trans. Commun..

[46]  Eyal de Lara,et al.  Accurate GSM Indoor Localization , 2005, UbiComp.

[47]  Richard P. Martin,et al.  Tracking vehicular speed variations by warping mobile phone signal strengths , 2011, 2011 IEEE International Conference on Pervasive Computing and Communications (PerCom).

[48]  B. Merminod,et al.  In Step with INS Navigation for the Blind, Tracking Emergency Crews , 2002 .

[49]  David A. Ross,et al.  Wearable interfaces for orientation and wayfinding , 2000, Assets '00.

[50]  R. Fox,et al.  Classical Electrodynamics, 3rd ed. , 1999 .

[51]  Neal Lesh,et al.  Indoor navigation using a diverse set of cheap, wearable sensors , 1999, Digest of Papers. Third International Symposium on Wearable Computers.

[52]  M. Haavisto,et al.  Time Dependence of Demagnetization and Flux Losses Occurring in Sintered Nd-Fe-B Permanent Magnets , 2010, IEEE Transactions on Magnetics.

[53]  Eamonn J. Keogh,et al.  Scaling up dynamic time warping for datamining applications , 2000, KDD '00.

[54]  William F Storms,et al.  Magnetic Field Aided Indoor Navigation , 2012 .

[55]  H. B. Mitchell,et al.  Multi-Sensor Data Fusion: An Introduction , 2007 .

[56]  Belur V. Dasarathy,et al.  Sensor fusion potential exploitation-innovative architectures and illustrative applications , 1997, Proc. IEEE.

[57]  Mark H. Hansen,et al.  Participatory sensing - eScholarship , 2006 .

[58]  D. Mitchell Wilkes,et al.  Mobile robot localization using an electronic compass for corridor environment , 2000, Smc 2000 conference proceedings. 2000 ieee international conference on systems, man and cybernetics. 'cybernetics evolving to systems, humans, organizations, and their complex interactions' (cat. no.0.

[59]  Pattie Maes,et al.  Situational Awareness from Environmental Sounds , 1997 .

[60]  E. Čermáková.  Magnetization of Steel Building Materials and Structures in the Natural Geomagnetic Field , 2005 .

[61]  L. Iftode,et al.  FiatLux : Fingerprinting Rooms Using Light Intensity , 2022 .

[62]  Vesa T. Peltonen,et al.  Computational auditory scene recognition , 2002, 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[63]  M Goldsmith,et al.  A beginner's guide to measurement. , 2010 .

[64]  John Burnett,et al.  Mitigation of extremely low frequency magnetic fields from electrical installations in high-rise buildings , 2002 .

[65]  Gaetano Borriello,et al.  WALRUS: wireless acoustic location with room-level resolution using ultrasound , 2005, MobiSys '05.

[66]  S. Evershed,et al.  Permanent magnets in theory and practice , 1920 .

[67]  Andy Hopper,et al.  Implementing a Sentient Computing System , 2001, Computer.

[68]  K. Magatani,et al.  Development of the visually impaired person guidance system using GPS , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

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

[70]  Gaetano Borriello,et al.  Particle Filters for Location Estimation in Ubiquitous Computing: A Case Study , 2004, UbiComp.

[71]  Wamadeva Balachandran,et al.  An Assessment of a Mobile Communication Link for a System to Navigate Visually Impaired People , 2009, IEEE Transactions on Instrumentation and Measurement.

[72]  Alex Pentland,et al.  Auditory Context Awareness via Wearable Computing , 1998 .

[73]  Gérard Lachapelle,et al.  Indoor Positioning System Using Accelerometry and High Accuracy Heading Sensors , 2003 .

[74]  Kenji Mase,et al.  Activity and Location Recognition Using Wearable Sensors , 2002, IEEE Pervasive Comput..

[75]  P. Caselli,et al.  Classification of Motor Activities through Derivative Dynamic Time Warping applied on Accelerometer Data , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

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

[77]  Petteri Alahuhta,et al.  Location Estimation Indoors by Means of Small Computing Power Devices, Accelerometers, Magnetic Sensors, and Map Knowledge , 2002, Pervasive.

[78]  Ram Dantu,et al.  Magnetic Maps for Indoor Navigation , 2011, IEEE Transactions on Instrumentation and Measurement.

[79]  Alireza Sahami Shirazi,et al.  Location-based crowdsourcing: extending crowdsourcing to the real world , 2010, NordiCHI.

[80]  Alexandra Millonig,et al.  Developing Landmark-Based Pedestrian-Navigation Systems , 2007, IEEE Transactions on Intelligent Transportation Systems.

[81]  Gaetano Borriello,et al.  Location Systems for Ubiquitous Computing , 2001, Computer.

[82]  E.F. El-Saadany,et al.  Disturbance classification utilizing dynamic time warping classifier , 2004, IEEE Transactions on Power Delivery.

[83]  Gaurav S. Sukhatme,et al.  Coarse In-Building Localization with Smartphones , 2009, MobiCASE.

[84]  Johann Borenstein,et al.  Non-GPS navigation with the personal dead-reckoning system , 2007, SPIE Defense + Commercial Sensing.

[85]  A. Kornhauser,et al.  An Introduction to Map Matching for Personal Navigation Assistants , 1998 .