Time-of-Flight-Based Radio Tomography for Device Free Localization

Due to its ability of realizing device free localization with wireless networks, the radio tomography becomes a promising technique that draws considerable attention. Traditional radio tomography makes use of the received signal strength (RSS) of wireless links to realize location estimation. However, the RSS measurement is particularly sensitive to noise. Inspired by the fact that similar to the RSS, the time-of-flight (TOF) measurement also changes significantly when some objects shadow the wireless link, and the fact that compared with the RSS, the TOF measurement is robust to noise, a novel TOF-based radio tomography is proposed in this paper. With the TOF measurements of the shadowed links as observation information, a modified particle filter algorithm which utilizes the compressive sensing technique to produce the importance distribution of the particle set is proposed, so as to realize localization and tracking with under-sampled measurements by making full use of the space-domain sparse and time-domain gradually changed feature of the location information. The experiments with the 802.15.4a chirp spread spectrum ranging hardware are presented to confirm the proposed scheme.

[1]  H. Vincent Poor,et al.  Non-Line-of-Sight Node Localization Based on Semi-Definite Programming in Wireless Sensor Networks , 2009, IEEE Transactions on Wireless Communications.

[2]  E.J. Candes,et al.  An Introduction To Compressive Sampling , 2008, IEEE Signal Processing Magazine.

[3]  Neal Patwari,et al.  See-Through Walls: Motion Tracking Using Variance-Based Radio Tomography Networks , 2011, IEEE Transactions on Mobile Computing.

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

[5]  Max Q.-H. Meng,et al.  Power Adaptive Localization Algorithm for Wireless Sensor Networks Using Particle Filter , 2009, IEEE Transactions on Vehicular Technology.

[6]  Richard G. Baraniuk,et al.  Compressive Sensing , 2008, Computer Vision, A Reference Guide.

[7]  P. Djurić,et al.  Particle filtering , 2003, IEEE Signal Process. Mag..

[8]  Lionel M. Ni,et al.  RASS: A real-time, accurate and scalable system for tracking transceiver-free objects , 2011, 2011 IEEE International Conference on Pervasive Computing and Communications (PerCom).

[9]  Lionel M. Ni,et al.  Dynamic clustering for tracking multiple transceiver-free objects , 2009, 2009 IEEE International Conference on Pervasive Computing and Communications.

[10]  Michael G. Rabbat,et al.  Background Subtraction for Online Calibration of Baseline RSS in RF Sensing Networks , 2012, IEEE Transactions on Mobile Computing.

[11]  David L Donoho,et al.  Compressed sensing , 2006, IEEE Transactions on Information Theory.

[12]  Bo Yang,et al.  Multi-target device-free tracking using radio frequency tomography , 2011, 2011 Seventh International Conference on Intelligent Sensors, Sensor Networks and Information Processing.

[13]  Heping Song,et al.  Feedback Based Sparse Recovery for Motion Tracking in RF Sensor Networks , 2011, 2011 IEEE Sixth International Conference on Networking, Architecture, and Storage.

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

[15]  Corey D. Cooke.  Attenuation Field Estimation Using Radio Tomography , 2011 .

[16]  Yang Zhao,et al.  Demo abstract: Histogram distance-based radio tomographic localization , 2012, 2012 ACM/IEEE 11th International Conference on Information Processing in Sensor Networks (IPSN).

[17]  Michael G. Rabbat,et al.  Compressed RF Tomography for Wireless Sensor Networks: Centralized and Decentralized Approaches , 2009, DCOSS.

[18]  Gang Zhou,et al.  Models and solutions for radio irregularity in wireless sensor networks , 2006, TOSN.

[19]  Lionel M. Ni,et al.  An RF-Based System for Tracking Transceiver-Free Objects , 2007, Fifth Annual IEEE International Conference on Pervasive Computing and Communications (PerCom'07).

[20]  Emmanuel J. Candès,et al.  Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies? , 2004, IEEE Transactions on Information Theory.

[21]  Heping Song,et al.  Target localization via correlated link inference , 2011, 2011 IEEE International Conference on Mechatronics and Automation.

[22]  S Lanzisera,et al.  Radio Frequency Time-of-Flight Distance Measurement for Low-Cost Wireless Sensor Localization , 2011, IEEE Sensors Journal.

[23]  Yang Zhao,et al.  Noise reduction for variance-based device-free localization and tracking , 2011, 2011 8th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks.

[24]  Shahrokh Valaee,et al.  Compressive Sensing Based Positioning Using RSS of WLAN Access Points , 2010, 2010 Proceedings IEEE INFOCOM.

[25]  Pramod K. Varshney,et al.  Tracking in Wireless Sensor Networks Using Particle Filtering: Physical Layer Considerations , 2009, IEEE Transactions on Signal Processing.

[26]  A. Doucet,et al.  A note on auxiliary particle filters , 2008 .

[27]  Neal Patwari,et al.  RF Sensor Networks for Device-Free Localization: Measurements, Models, and Algorithms , 2010, Proceedings of the IEEE.

[28]  R.G. Baraniuk,et al.  Compressive Sensing [Lecture Notes] , 2007, IEEE Signal Processing Magazine.

[29]  Neal Patwari,et al.  Radio Tomographic Imaging with Wireless Networks , 2010, IEEE Transactions on Mobile Computing.

[30]  Maurizio Bocca,et al.  Enhancing the accuracy of radio tomographic imaging using channel diversity , 2012, 2012 IEEE 9th International Conference on Mobile Ad-Hoc and Sensor Systems (MASS 2012).

[31]  Ryan W. Thomas,et al.  Modelling and analysis of radio tomography , 2011, 2011 4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP).

[32]  Moustafa Youssef,et al.  Synthetic Generation of Radio Maps for Device-Free Passive Localization , 2011, 2011 IEEE Global Telecommunications Conference - GLOBECOM 2011.

[33]  R. DeVore,et al.  A Simple Proof of the Restricted Isometry Property for Random Matrices , 2008 .

[34]  Xi Chen,et al.  Sequential Monte Carlo for simultaneous passive device-free tracking and sensor localization using received signal strength measurements , 2011, Proceedings of the 10th ACM/IEEE International Conference on Information Processing in Sensor Networks.

[35]  Neal Patwari,et al.  A Fade-Level Skew-Laplace Signal Strength Model for Device-Free Localization with Wireless Networks , 2012, IEEE Transactions on Mobile Computing.

[36]  Sanjay Jha,et al.  Detection and Tracking Using Particle-Filter-Based Wireless Sensor Networks , 2010, IEEE Transactions on Mobile Computing.

[37]  Kristofer S. J. Pister,et al.  RF Time of Flight Ranging for Wireless Sensor Network Localization , 2006, 2006 International Workshop on Intelligent Solutions in Embedded Systems.

[38]  Moustafa Youssef,et al.  Smart cevices for smart environments: Device-free passive detection in real environments , 2009, 2009 IEEE International Conference on Pervasive Computing and Communications.