A hidden Markov model for distinguishing between RFID-tagged objects in adjacent areas

Distinguishing between RFID-tagged objects within different areas poses an important building block for many RFID-based applications. Existing localization techniques, however, often cannot reliably distinguish between tagged objects that are close to the border of adjacent areas. Against this backdrop, we present a hybrid approach based on an ANN and a HMM that leverages not only low-level RFID data streams but also information about physical constraints and process knowledge and thus incorporates scene dynamics. We experimentally demonstrate the performance of our approach considering a RFID-based smart fitting room which is a practically relevant application with limited process control in an environment with strong multipath reflections and non-line-of-sight effects. Our results show that our approach is able to reliably distinguish between tagged objects within different cabins. This includes objects hanging on coat hooks at partition walls of adjacent cabins, i.e., at a maximum distance of 5 centimeters to the border of an adjacent area.

[1]  Hakima Chaouchi,et al.  RFID-assisted indoor localization and the impact of interference on its performance , 2011, J. Netw. Comput. Appl..

[2]  Yang Dongkai,et al.  Flexible indoor localization and tracking system based on mobile phone , 2016 .

[3]  Frédéric Thiesse,et al.  Understanding the value of integrated RFID systems: a case study from apparel retail , 2009, Eur. J. Inf. Syst..

[4]  H. Bourlard,et al.  Links Between Markov Models and Multilayer Perceptrons , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  K. V. S. Rao,et al.  Phase based spatial identification of UHF RFID tags , 2010, 2010 IEEE International Conference on RFID (IEEE RFID 2010).

[6]  Kevin P. Murphy,et al.  Machine learning - a probabilistic perspective , 2012, Adaptive computation and machine learning series.

[7]  Richard Lippmann,et al.  Neural Network Classifiers Estimate Bayesian a posteriori Probabilities , 1991, Neural Computation.

[8]  Frédéric Thiesse,et al.  Empowering Smarter Fitting Rooms with RFID Data Analytics , 2017, Wirtschaftsinformatik.

[9]  Van Nostrand,et al.  Error Bounds for Convolutional Codes and an Asymptotically Optimum Decoding Algorithm , 1967 .

[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]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[12]  Axel Pinz,et al.  Fusing RFID and computer vision for probabilistic tag localization , 2014, 2014 IEEE International Conference on RFID (IEEE RFID).

[13]  Markus Brandner,et al.  Experimental evaluation of RFID gate concepts , 2011, 2011 IEEE International Conference on RFID.

[14]  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).

[15]  V. Padmanabhan,et al.  Enhancements to the RADAR User Location and Tracking System , 2000 .

[16]  Reinhold Häb-Umbach,et al.  Server based indoor navigation using RSSI and inertial sensor information , 2013, 2013 10th Workshop on Positioning, Navigation and Communication (WPNC).

[17]  Giovanni Romagnoli,et al.  RF-based Locating of Mobile Objects , 2016, IOT.

[18]  Frédéric Thiesse,et al.  Pushing the limits of RFID: Empowering RFID-based Electronic Article Surveillance with Data Analytics Techniques , 2015, ICIS.

[19]  Lawrence Mosley,et al.  A balanced approach to the multi-class imbalance problem , 2013 .

[20]  Mauro Brunato,et al.  Statistical learning theory for location fingerprinting in wireless LANs , 2005, Comput. Networks.

[21]  Thorsten Vaupel,et al.  A Hidden Markov Model for pedestrian navigation , 2010, 2010 7th Workshop on Positioning, Navigation and Communication.

[22]  Jr. G. Forney,et al.  The viterbi algorithm , 1973 .

[23]  Gang Li,et al.  Bandwidth dependence of CW ranging to UHF RFID tags in severe multipath environments , 2011, 2011 IEEE International Conference on RFID.

[24]  Hervé Bourlard,et al.  Connectionist Speech Recognition: A Hybrid Approach , 1993 .

[25]  Hervé Bourlard,et al.  Hybrid HMM/ANN Systems for Speech Recognition: Overview and New Research Directions , 1997, Summer School on Neural Networks.