Noncooperative Localization and Tracking Through the Factorization Method

The localization and tracking of human targets are cast as linear inverse obstacle problems and solved by means of the factorization method. The proposed approach is validated against indoor monitoring and multifrequency sensing, where Green’s function pertaining to the involved realistic scenarios has been determined through full-wave simulations. The results, which include the analysis of the impact on final performance of the number of employed transceivers as well as preliminary 2-D processing of realistic data simulated in 3-D geometry, show good robustness to noise and model errors.

[1]  T. Isernia,et al.  Improved Sampling Methods for Shape Reconstruction of 3-D Buried Targets , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[2]  Biswanath Mukherjee,et al.  Wireless sensor network survey , 2008, Comput. Networks.

[3]  Xiaofeng Li,et al.  Acoustic passive localization algorithm based on wireless sensor networks , 2009, 2009 International Conference on Mechatronics and Automation.

[4]  Daqing Zhang,et al.  RT-Fall: A Real-Time and Contactless Fall Detection System with Commodity WiFi Devices , 2017, IEEE Transactions on Mobile Computing.

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

[6]  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.

[7]  Federico Viani,et al.  Electromagnetic passive localization and tracking of moving targets in a WSN-infrastructured environment , 2010 .

[8]  K. Fukunaga,et al.  Imaging the 3D temperature distributions caused by exposure of dielectric phantoms to high-frequency electromagnetic fields , 2006, IEEE Transactions on Dielectrics and Electrical Insulation.

[9]  Ugur Alkasi,et al.  Experimental Assessment of Linear Sampling and Factorization Methods for Microwave Imaging of Concealed Targets , 2015 .

[10]  Federico Viani,et al.  Learning ensemble strategy for static and dynamic localization in wireless sensor networks , 2017, Int. J. Netw. Manag..

[11]  D. Colton,et al.  The linear sampling method in inverse electromagnetic scattering theory , 2003 .

[12]  A. B. M. Musa,et al.  Tracking unmodified smartphones using wi-fi monitors , 2012, SenSys '12.

[13]  Byunghun Song,et al.  Surveillance Tracking System Using Passive Infrared Motion Sensors in Wireless Sensor Network , 2008, 2008 International Conference on Information Networking.

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

[15]  Tommaso Isernia,et al.  Electromagnetic inverse scattering: Retrievable information and measurement strategies , 1997 .

[16]  Daniele Trinchero,et al.  Localization, tracking, and imaging of targets in wireless sensor networks: An invited review , 2011 .

[17]  Dirk Pesch,et al.  Recent advances in RF-based passive device-free localisation for indoor applications , 2017, Ad Hoc Networks.

[18]  Lorenzo Crocco,et al.  Inverse Scattering Via Virtual Experiments and Contrast Source Regularization , 2015, IEEE Transactions on Antennas and Propagation.

[19]  R. Michael Buehrer,et al.  Introduction to the Special Issue on Non-Cooperative Localization Networks , 2014, IEEE Journal of Selected Topics in Signal Processing.

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

[21]  Xinrong Li,et al.  Collaborative Localization With Received-Signal Strength in Wireless Sensor Networks , 2007, IEEE Transactions on Vehicular Technology.

[22]  N I Grinberg,et al.  The Factorization Method for Inverse Problems , 2007 .

[23]  T. Isernia,et al.  Quasi — Invisibility via inverse scattering techniques , 2014, 2014 IEEE Conference on Antenna Measurements & Applications (CAMA).

[24]  Moustafa Youssef,et al.  CoSDEO 2016 Keynote: A decade later — Challenges: Device-free passive localization for wireless environments , 2007, 2016 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops).

[25]  T Isernia,et al.  Improved quantitative microwave tomography by exploiting the physical meaning of the Linear Sampling Method , 2011, Proceedings of the 5th European Conference on Antennas and Propagation (EUCAP).

[26]  Federico Viani,et al.  An accurate prediction method for moving target localization and tracking in wireless sensor networks , 2018, Ad Hoc Networks.

[27]  Federico Viani,et al.  Semantic wireless localization of WiFi terminals in smart buildings , 2016 .

[28]  Tommaso Isernia,et al.  Boundary Indicator for Aspect Limited Sensing of Hidden Dielectric Objects , 2018, IEEE Geoscience and Remote Sensing Letters.

[29]  Lorenzo Crocco,et al.  A New Linear Distorted-Wave Inversion Method for Microwave Imaging via Virtual Experiments , 2016, IEEE Transactions on Microwave Theory and Techniques.

[30]  Federico Viani,et al.  Iterative classification strategy for multi-resolution wireless sensing of passive targets , 2018 .

[31]  R. Pierri,et al.  Impossibility of Recovering a Scatterer's Shape by the First Version of the “linear sampling” Method , 2003 .

[32]  Tommaso Isernia,et al.  On the Solution of 2-D Inverse Scattering Problems via Source-Type Integral Equations , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[33]  Tommaso Isernia,et al.  SHAPE RECONSTRUCTION VIA EQUIVALENCE PRINCIPLES,CONSTRAINED INVERSE SOURCE PROBLEMS AND SPARSITY PROMOTION , 2017 .

[34]  Nan Li,et al.  Measuring and monitoring occupancy with an RFID based system for demand-driven HVAC operations , 2012 .

[35]  T. Isernia,et al.  The factorization method for virtual experiments based quantitative inverse scattering , 2016 .

[36]  Lorenzo Crocco,et al.  A Qualitative Inverse Scattering Method for Through-the-Wall Imaging , 2010, IEEE Geoscience and Remote Sensing Letters.