Target tracking without line of sight using range from radio

We propose a framework for utilizing fixed ultra-wideband ranging radio nodes to track a moving target radio node in an environment without guaranteed line of sight or accurate odometry. For the case where the fixed nodes’ locations are known, we derive a Bayesian room-level tracking method that takes advantage of the structural characteristics of the environment to ensure robustness to noise. For the case of unknown fixed node locations, we present a two-step approach that first reconstructs the target node’s path using Gaussian Process Latent Variable models (GPLVMs) and then uses that path to determine the locations of the fixed nodes. We present experiments verifying our algorithm in an office environment, and we compare our results to those generated by online and batch SLAM methods, as well as odometry mapping. Our algorithm is successful at tracking a moving target node without odometry and mapping the locations of fixed nodes using radio ranging data that are both noisy and intermittent.

[1]  David J. Fleet,et al.  This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE Gaussian Process Dynamical Model , 2007 .

[2]  Aly E. Fathy,et al.  Real-time UWB indoor positioning system with millimeter 3-D dynamic accuracy , 2009, 2009 IEEE Antennas and Propagation Society International Symposium.

[3]  Andreas F. Molisch,et al.  Localization via Ultra- Wideband Radios , 2005 .

[4]  Jing Shi,et al.  RFID localization algorithms and applications—a review , 2009, J. Intell. Manuf..

[5]  Geoffrey A. Hollinger,et al.  Preliminary Results in Tracking Mobile Targets Using Range Sensors from Multiple Robots , 2006, DARS.

[6]  Juan José Murillo-Fuentes,et al.  Gaussian Processes for Nonlinear Signal Processing , 2013, ArXiv.

[7]  Sebastian Thrun,et al.  Probabilistic robotics , 2002, CACM.

[8]  Geoffrey A. Hollinger,et al.  Tracking a moving target in cluttered environments with ranging radios , 2008, 2008 IEEE International Conference on Robotics and Automation.

[9]  J. Tenenbaum,et al.  A global geometric framework for nonlinear dimensionality reduction. , 2000, Science.

[10]  Edward Lloyd Snelson,et al.  Flexible and efficient Gaussian process models for machine learning , 2007 .

[11]  G.B. Giannakis,et al.  Localization via ultra-wideband radios: a look at positioning aspects for future sensor networks , 2005, IEEE Signal Processing Magazine.

[12]  Dieter Fox,et al.  Gaussian Processes for Signal Strength-Based Location Estimation , 2006, Robotics: Science and Systems.

[13]  Geoffrey A. Hollinger,et al.  Efficient Multi-robot Search for a Moving Target , 2009, Int. J. Robotics Res..

[14]  Erik D. Demaine,et al.  Mobile-assisted localization in wireless sensor networks , 2005, Proceedings IEEE 24th Annual Joint Conference of the IEEE Computer and Communications Societies..

[15]  E. Olson,et al.  Robust Range-Only Beacon Localization , 2004, IEEE Journal of Oceanic Engineering.

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

[17]  Athanasios Kehagias,et al.  Range-only SLAM with Interpolated Range Data , 2006 .

[18]  Guang-Zhong Yang,et al.  Body sensor networks , 2006 .

[19]  Sanjiv Singh,et al.  Range-only SLAM for robots operating cooperatively with sensor networks , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..

[20]  Tamio Arai,et al.  Distributed Autonomous Robotic Systems 3 , 1998 .

[21]  Cyrill Stachniss,et al.  Hierarchical optimization on manifolds for online 2D and 3D mapping , 2010, 2010 IEEE International Conference on Robotics and Automation.

[22]  Frank Dellaert,et al.  iSAM: Incremental Smoothing and Mapping , 2008, IEEE Transactions on Robotics.

[23]  Monica Nicoli,et al.  A Jump Markov Particle Filter for Localization of Moving Terminals in Multipath Indoor Scenarios , 2008, IEEE Transactions on Signal Processing.

[24]  Benjamin Grocholsky,et al.  Decentralized mapping of robot-aided sensor networks , 2008, 2008 IEEE International Conference on Robotics and Automation.

[25]  Frank Dellaert,et al.  Incremental smoothing and mapping , 2008 .

[26]  K. Kyamakya,et al.  A low-cost experimental ultra-wideband positioning system , 2005, 2005 IEEE International Conference on Ultra-Wideband.

[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]  Anton Schwaighofer,et al.  GPPS: A Gaussian Process Positioning System for Cellular Networks , 2003, NIPS.

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

[30]  David Evans,et al.  Localization for mobile sensor networks , 2004, MobiCom '04.

[31]  Wolfram Burgard,et al.  Robotics: Science and Systems XV , 2010 .

[32]  Martin David Adams,et al.  Robotic Mapping Using Measurement Likelihood Filtering , 2009, Int. J. Robotics Res..

[33]  Dieter Fox,et al.  Learning GP-BayesFilters via Gaussian process latent variable models , 2009, Auton. Robots.

[34]  Vijay Kumar,et al.  Robot and sensor networks for first responders , 2004, IEEE Pervasive Computing.

[35]  Pai H. Chou,et al.  EcoIMU: A Dual Triaxial-Accelerometer Inertial Measurement Unit for Wearable Applications , 2010, 2010 International Conference on Body Sensor Networks.

[36]  Neil D. Lawrence,et al.  Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable Models , 2005, J. Mach. Learn. Res..