Localization in wireless networks based on jointly compressed sensing

Location sensing is fundamental for supporting wireless communications services. This paper exploits the signal correlation structure observed in an indoor localization environment in order to provide accurate position estimation by means of a limited amount of signal-strength measurements. Because the mobile devices have limited processing power and battery capacity, the proposed received signal-strength localization protocol avoids putting on extra computational overhead on the mobile device by performing the position estimation at the Access Points (APs). Since the APs observe correlated signals from the mobile devices, the introduced method exploits the common structure of the received measurements in order to jointly estimate the positions precisely. The evaluation of the proposed protocol is performed on real laboratory data through experiments that quantify the impact of the system parameters on the location error.

[1]  Jie Yang,et al.  Indoor Localization Using Improved RSS-Based Lateration Methods , 2009, GLOBECOM 2009 - 2009 IEEE Global Telecommunications Conference.

[2]  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).

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

[4]  Prashant Krishnamurthy,et al.  Properties of indoor received signal strength for WLAN location fingerprinting , 2004, The First Annual International Conference on Mobile and Ubiquitous Systems: Networking and Services, 2004. MOBIQUITOUS 2004..

[5]  Maria Papadopouli,et al.  Empirical evaluation of signal-strength fingerprint positioning in wireless LANs , 2010, MSWIM '10.

[6]  Emmanuel J. Candès,et al.  Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information , 2004, IEEE Transactions on Information Theory.

[7]  R.G. Baraniuk,et al.  Distributed Compressed Sensing of Jointly Sparse Signals , 2005, Conference Record of the Thirty-Ninth Asilomar Conference onSignals, Systems and Computers, 2005..

[8]  Andrew G. Dempster,et al.  Indoor Positioning Techniques Based on Wireless LAN , 2007 .

[9]  Venkata N. Padmanabhan,et al.  Indoor localization without the pain , 2010, MobiCom.

[10]  Panagiotis Tsakalides,et al.  Localization in wireless networks via spatial sparsity , 2010, 2010 Conference Record of the Forty Fourth Asilomar Conference on Signals, Systems and Computers.

[11]  Richard G. Baraniuk,et al.  Distributed Compressive Sensing , 2009, ArXiv.

[12]  Joel A. Tropp,et al.  Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit , 2007, IEEE Transactions on Information Theory.

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

[14]  Simo Ali-Löytty,et al.  A comparative survey of WLAN location fingerprinting methods , 2009, 2009 6th Workshop on Positioning, Navigation and Communication.

[15]  A. S. Krishnakumar,et al.  Bayesian indoor positioning systems , 2005, Proceedings IEEE 24th Annual Joint Conference of the IEEE Computer and Communications Societies..