Evaluation of Smoothing Algorithms for a RSSI-Based Device-Free Passive Localisation

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.We show results using algorithms such as five-point Triangular Smoothing Algorithm, 1-D median filter, Savittzky-Golay filter, and Kalman filter.