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History aware Device-free Passive (DFP) Localisation

Deak G., Curran K., Condell J.

Image Processing & Communications

2011

Vol. 16, no 3-4

21-30

A novel Device-free Passive (DfP) Localisation technique that monitors wireless communications and location dependent signal characteristics is presented in this paper. The human body contains more than 70% water which is causing variances in the Received Signal Strength Indicator (RSSI) measurements. DfP is a technique to detect a person without the need for any physical devices i.e. tags or sensors. This paper focuses on communication protocols such as Radiogram Protocol, Transmission Control Protocol (TCP), and User Datagram Protocol (UDP), outlining the possibility of using these protocols in Wireless Sensor Networks (WSNs). Histograms and historical data are new concepts in a DfP scenario which can improve the accuracy of location estimation in DfP Localisation.

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

Deak G., Curran K., Condell J.

Image Processing & Communications

2010

Vol. 15, no 1

23-34

There are a number of techniques used in modern Location 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 of RSSI-based location positioning technologies, is the possibility to develop location estimation systems without the need for specialised hardware. The human body contains more than 70% water which is causing changes in the RSSI measurements. It is known that the resonance frequency of the water is 2.4 GHz. Thus a human presence in an indoor environment attenuates the wireless signal. Device-free Passive (DfP) localisation is a technique to detect a person without the need for any physical devices i.e. tags or sensors. A DfP Localisation system uses the Received Signal Strength Indicator (RSSI) for monitoring and tracking changes in a Wireless Network infrastructure. The changes in the signal along with prior fingerprinting of a physical location allow identification of a person's location. This research is focused on implementing DfP Localisation built using a Wireless Sensor Network (WSN). The aim of this paper is the evaluation of various smoothing algorithms for the RSSI recorded in a Device-free Passive (DfP) Localisation scenario in order to find an algorithm that generates the best output. The best output is referred to here as results that can help us decide if a person entered the monitored environment. The DfP scenario considered in this paper is based on monitoring the changes in the wireless communications due to the presence of a human body in the environment. Thus to have a clear image of the changes caused by human presence indoors, the wireless recordings need to be smoothed. The following algorithms are demonstrated with results: five-point Triangular Smoothing Algorithm, Moving Average filter, Lowess filter, Loess filter, Rlowess filter, Rloess filter, 1-D median filter, Savittzky-Golay filter, and Kalman filter.