Filters for RSSI-based measurements in a device-free passive localisation scenario

There are a number of techniques used in modernLocation aware systems such as Received Signal Strength Indicator(RSSI), Time of Arrival (TOA), Time Difference of Arrival(TDOA) and Angle of Arrival (AOA). However the benefit ofRSSI-based location positioning technologies, is the possibilityto develop location estimation systems without the need forspecialised hardware.The human body contains more than 70% water which iscausing changes in the RSSI measurements. It is known thatthe resonance frequency of the water is 2.4 GHz. Thus a humanpresence in an indoor environment attenuates the wireless signal.Device-free Passive (DfP) localisation is a technique to detect aperson without the need for any physical devices i.e. tags orsensors. A DfP Localisation system uses the Received SignalStrength Indicator (RSSI) for monitoring and tracking changesin a Wireless Network infrastructure. The changes in the signalalong with prior fingerprinting of a physical location allowidentification of a person’s location.This research is focused on implementing DfP Localisationbuilt using a Wireless Sensor Network (WSN). The aim of thispaper is the evaluation of various smoothing algorithms forthe RSSI recorded in a Device-free Passive (DfP) Localisationscenario in order to find an algorithm that generates the bestoutput. The best output is referred to here as results that canhelp us decide if a person entered the monitored environment.The DfP scenario considered in this paper is based on monitoringthe changes in the wireless communications due to the presenceof a human body in the environment. Thus to have a clear imageof the changes caused by human presence indoors, the wirelessrecordings need to be smoothed. The following algorithms aredemonstrated with results: five-point Triangular Smoothing Algorithm,Moving Average filter, Lowess filter, Loess filter, Rlowessfilter, Rloess filter, 1-D median filter, Savittzky-Golay filter, andKalman filter.

[1]  Larbi Talbi,et al.  Human body modelling for prediction of effect of people on indoor propagation channel , 2004 .

[2]  Lin Ma,et al.  A Novel Through-Wall Imaging Method Using Ultra WideBand Pulse System , 2006, 2006 International Conference on Intelligent Information Hiding and Multimedia.

[3]  Andy Hopper,et al.  Implementing a Sentient Computing System , 2001, Computer.

[4]  刘小利,et al.  基于Curve Fitting Toolbox的普朗克常数测定 , 2012 .

[5]  Jacek Ilow,et al.  A Testbed for Localizing Wireless LAN Devices Using Received Signal Strength , 2008, 6th Annual Communication Networks and Services Research Conference (cnsr 2008).

[6]  Kevin Curran,et al.  Pinpointing users with location estimation techniques and Wi-Fi hotspot technology , 2008, Int. J. Netw. Manag..

[7]  Charles L. Despins,et al.  Indoor Geolocation with Received Signal Strength Fingerprinting Technique and Neural Networks , 2004, ICT.

[8]  Pete Steggles,et al.  THE UBISENSE SMART SPACE PLATFORM , 2005 .

[9]  W. G. Scanlon,et al.  Pedestrian-induced fading for indoor channels at 2.45, 5.7 and 62 GHz , 2000, Vehicular Technology Conference Fall 2000. IEEE VTS Fall VTC2000. 52nd Vehicular Technology Conference (Cat. No.00CH37152).

[10]  Jukka Vanhala,et al.  TileTrack: Capacitive human tracking using floor tiles , 2009, 2009 IEEE International Conference on Pervasive Computing and Communications.

[11]  Moustafa Youssef,et al.  Analysis of a Device-Free Passive Tracking System in Typical Wireless Environments , 2009, 2009 3rd International Conference on New Technologies, Mobility and Security.

[12]  Tsenka Stoyanova,et al.  Evaluation of impact factors on RSS accuracy for localization and tracking applications in sensor networks , 2009, Telecommun. Syst..

[13]  T.A. Denidni,et al.  Modeling human body effects for indoor radio channel using UTD , 2004, Canadian Conference on Electrical and Computer Engineering 2004 (IEEE Cat. No.04CH37513).

[14]  Kevin Curran,et al.  RFID-Enabled Location Determination Within Indoor Environments , 2009, Int. J. Ambient Comput. Intell..

[15]  Prashant Krishnamurthy,et al.  Sixth Annual IEEE International Conference on Pervasive Computing and Communications Location Fingerprint Analyses Toward Efficient Indoor Positioning , 2022 .

[16]  John Krumm,et al.  Ubiquitous Computing Fundamentals , 2009 .

[17]  Neal Patwari,et al.  Through-Wall Tracking Using Variance-Based Radio Tomography Networks , 2009, ArXiv.

[18]  Axel Küpper.  Location-based Services: Fundamentals and Operation , 2005 .

[19]  Hari Balakrishnan,et al.  6th ACM/IEEE International Conference on on Mobile Computing and Networking (ACM MOBICOM ’00) The Cricket Location-Support System , 2022 .

[20]  Rogerio A. Enríquez-Caldera,et al.  Position Location Techniques and Applications , 2009 .

[21]  Kevin Curran,et al.  Ubiquitous Developments in Ambient Computing and Intelligence: Human-Centered Applications , 2011 .

[22]  Kegen Yu,et al.  Ground-Based Wireless Positioning , 2009 .

[23]  Alan Bensky,et al.  Wireless positioning technologies and applications , 2008 .

[24]  Nobuo Kawaguchi,et al.  Bayesian based location estimation system using wireless LAN , 2005, Third IEEE International Conference on Pervasive Computing and Communications Workshops.

[25]  J. Krumm,et al.  Multi-camera multi-person tracking for EasyLiving , 2000, Proceedings Third IEEE International Workshop on Visual Surveillance.

[26]  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.

[27]  J. Condell,et al.  Wireless Sensor Networks-Smoothing algorithms for RSSI-based Device-free Passive Localisation , 2010 .

[28]  Qing Huo Liu,et al.  Through-wall imaging (TWI) by radar: 2-D tomographic results and analyses , 2005, IEEE Trans. Geosci. Remote. Sens..

[29]  Kevin Curran,et al.  Evaluation of Smoothing Algorithms for a RSSI-Based Device-Free Passive Localisation , 2010, IP&C.

[30]  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).

[31]  K. Sarabandi,et al.  Through wall imaging at microwave frequencies using space-time focusing , 2004, IEEE Antennas and Propagation Society Symposium, 2004..

[32]  Johan Hjelm,et al.  Local Positioning Systems: LBS Applications and Services , 2006 .

[33]  R. Zhou,et al.  Wireless Indoor Tracking System (WITS) , 2010 .

[34]  Mikkel Baun Kjærgaard,et al.  Hyperbolic Location Fingerprinting: A Calibration-Free Solution for Handling Differences in Signal Strength (concise contribution) , 2008, 2008 Sixth Annual IEEE International Conference on Pervasive Computing and Communications (PerCom).