Flexible Indoor Localization and Tracking Based on a Wearable Platform and Sensor Data Fusion

Indoor localization and tracking of moving human targets is a task of recognized importance and difficulty. In this paper, we describe a position measurement technique based on the fusion of various sensor data collected using a wearable embedded platform. Since the accumulated measurement uncertainty affecting inertial data (especially due to the on-board accelerometer) usually makes the measured position values drift away quickly, a heuristic approach is used to keep velocity estimation uncertainty in the order of a few percent. As a result, unlike other solutions proposed in the literature, localization accuracy is good when the wearable platform is worn at the waist. Unbounded uncertainty growth is prevented by injecting the position values collected at a very low rate from the nodes of an external fixed infrastructure (e.g., based on cameras) into an extended Kalman filter. If the adjustment rate is in the order of several seconds and if such corrections are performed only when the user is detected to be in movement, the infrastructure remains idle most of time with evident benefits in terms of scalability. In fact, multiple platforms could work simultaneously in the same environment without saturating the communication channels.

[1]  David Macii,et al.  Timestamping of IEEE 802.15.4a CSS Signals for Wireless Ranging and Time Synchronization , 2013, IEEE Transactions on Instrumentation and Measurement.

[2]  D. Macii,et al.  A wearable embedded inertial platform with wireless connectivity for indoor position tracking , 2011, 2011 IEEE International Instrumentation and Measurement Technology Conference.

[3]  Angelo M. Sabatini,et al.  A step toward GPS/INS personal navigation systems: real-time assessment of gait by foot inertial sensing , 2005, 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[4]  K.D. Frampton Acoustic self-localization in a distributed sensor network , 2006, IEEE Sensors Journal.

[5]  A. Sikora,et al.  Fields Tests for Ranging and Localization with Time-of-Flight-Measurements Using Chirp Spread Spectrum RF-devices , 2007, 2007 IEEE Instrumentation & Measurement Technology Conference IMTC 2007.

[6]  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.

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

[8]  Grantham Pang,et al.  Evaluation of a Low-cost MEMS Accelerometer for Distance Measurement , 2001, J. Intell. Robotic Syst..

[9]  S. Shankar Sastry,et al.  A Distributed Topological Camera Network Representation for Tracking Applications , 2010, IEEE Transactions on Image Processing.

[10]  Chris Hide,et al.  Aiding MEMS IMU with building heading for indoor pedestrian navigation , 2010, 2010 Ubiquitous Positioning Indoor Navigation and Location Based Service.

[11]  F. Chavand,et al.  3-D measurements using a video camera and a range finder , 1997 .

[12]  Roger Wattenhofer,et al.  SpiderBat: Augmenting wireless sensor networks with distance and angle information , 2011, Proceedings of the 10th ACM/IEEE International Conference on Information Processing in Sensor Networks.

[13]  Simeon Furrer,et al.  Indoor Location Tracking Using Inertial Navigation Sensors and Radio Beacons , 2008, IOT.

[14]  Sara Sulis,et al.  On the Measurement of Power Quality Indexes for Harmonic Distorsion in the Presence of Capacitors”, IEEE Transactions on Instrumentation and Measurement , 2007 .

[15]  Cesare Alippi,et al.  A RSSI-based and calibrated centralized localization technique for wireless sensor networks , 2006, Fourth Annual IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOMW'06).

[16]  Gérard Lachapelle,et al.  GNSS Indoor Location Technologies , 2004 .

[17]  Oswald Lanz,et al.  Approximate Bayesian multibody tracking , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[18]  Hiroshi Ishiguro,et al.  Laser-Based Tracking of Human Position and Orientation Using Parametric Shape Modeling , 2009, Adv. Robotics.

[19]  Max Mühlhäuser,et al.  An IR local positioning system for smart items and devices , 2003, 23rd International Conference on Distributed Computing Systems Workshops, 2003. Proceedings..

[20]  Joel Barnes,et al.  The Potential of Locata Technology for Structural Monitoring Applications , 2007 .

[21]  Billur Barshan,et al.  Evaluation of a solid-state gyroscope for robotics applications , 1992 .

[22]  Theodore S. Rappaport,et al.  Wireless communications - principles and practice , 1996 .

[23]  Jean-Paul Laumond,et al.  On the nonholonomic nature of human locomotion , 2008, Auton. Robots.

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

[25]  A. Harter,et al.  A distributed location system for the active office , 1994, IEEE Network.

[26]  Ignas Niemegeers,et al.  A survey of indoor positioning systems for wireless personal networks , 2009, IEEE Communications Surveys & Tutorials.

[27]  E. Iso,et al.  Measurement Uncertainty and Probability: Guide to the Expression of Uncertainty in Measurement , 1995 .

[28]  Volkan Cevher,et al.  Acoustic sensor network design for position estimation , 2009, TOSN.

[29]  Leo Eldredge,et al.  Alternative Position , Navigation , and Timing-- The Need for Robust Radionavigation , 2012 .

[30]  Alessio De Angelis,et al.  A Low-Cost Ultra-Wideband Indoor Ranging System , 2009, IEEE Transactions on Instrumentation and Measurement.

[31]  G Santinelli,et al.  Self-calibrating indoor positioning system based on ZigBee® devices , 2009, 2009 IEEE Instrumentation and Measurement Technology Conference.

[32]  Bruno Ando,et al.  A smart wireless sensor network for AAL , 2011, 2011 IEEE International Workshop on Measurements and Networking Proceedings (M&N).

[33]  Andreas Savvides,et al.  An Empirical Characterization of Radio Signal Strength Variability in 3-D IEEE 802.15.4 Networks Using Monopole Antennas , 2006, EWSN.

[34]  Daniele Fontanelli,et al.  A Data Fusion Technique for Wireless Ranging Performance Improvement , 2013, IEEE Transactions on Instrumentation and Measurement.

[35]  Francesco G. B. De Natale,et al.  Syntactic Matching of Trajectories for Ambient Intelligence Applications , 2009, IEEE Transactions on Multimedia.

[36]  Deborah Estrin,et al.  GPS-less low-cost outdoor localization for very small devices , 2000, IEEE Wirel. Commun..

[37]  H. Vincent Poor,et al.  Mobile element assisted cooperative localization for wireless sensor networks with obstacles , 2010, IEEE Transactions on Wireless Communications.

[38]  Lan truyền,et al.  Wireless Communications Principles and Practice , 2015 .

[39]  Andreas Wieser,et al.  HIGH-SENSITIVITY GNSS: THE TRADE-OFF BETWEEN AVAIL- ABILITY AND ACCURACY , 2006 .

[40]  Moe Z. Win,et al.  Ranging With Ultrawide Bandwidth Signals in Multipath Environments , 2009, Proceedings of the IEEE.

[41]  Kunikatsu Takase,et al.  Multiple mobile robot navigation using the indoor global positioning system (iGPS) , 2001, Proceedings 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems. Expanding the Societal Role of Robotics in the the Next Millennium (Cat. No.01CH37180).

[42]  Chris Rizos,et al.  A Survey of Techniques and Algorithms in Deformation Monitoring Applications and the use of the Locata Technology for Such Applications , 2009 .

[43]  Joel Barnes,et al.  Structural Deformation Monitoring Using Locata , 2004 .

[44]  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).

[45]  Xin Li,et al.  Pedestrian detection and tracking in infrared imagery using shape and appearance , 2007, Comput. Vis. Image Underst..

[46]  Dario Petri,et al.  Accuracy of RSS-Based Centroid Localization Algorithms in an Indoor Environment , 2011, IEEE Transactions on Instrumentation and Measurement.

[47]  S. Sprager,et al.  A cumulant-based method for gait identification using accelerometer data with principal component analysis and support vector machine , 2009 .

[48]  Hugh F. Durrant-Whyte,et al.  Inertial navigation systems for mobile robots , 1995, IEEE Trans. Robotics Autom..

[49]  Liu Jing,et al.  Improved Particle Filter in Sensor Fusion for Tracking Randomly Moving Object , 2006, IEEE Transactions on Instrumentation and Measurement.

[50]  Yiannos Manoli,et al.  A modular and mobile system for indoor localization , 2010, 2010 International Conference on Indoor Positioning and Indoor Navigation.

[51]  Leopoldo Angrisani,et al.  Ultrasonic time-of-flight estimation through unscented Kalman filter , 2006, IEEE Transactions on Instrumentation and Measurement.