Deak, Gabriel-Adrian, Curran, Kevin and Condell, Joan (2010) Filters for RSSI-based measurements in a Device-free Passive Localisation Scenario. International Journal of Image Processing and Communications, 15 (1). pp. 23-34. [Journal article]
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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.
|Item Type:||Journal article|
|Faculties and Schools:||Faculty of Computing & Engineering|
Faculty of Computing & Engineering > School of Computing and Intelligent Systems
|Research Institutes and Groups:||Computer Science Research Institute|
Computer Science Research Institute > Intelligent Systems Research Centre
|Deposited By:||Dr Joan Condell|
|Deposited On:||07 Mar 2011 16:31|
|Last Modified:||09 Dec 2015 10:54|
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