Performance Comparison of Various Smoothing Methods For Detection of Device-Free Target

Abstract

In the device-free localization systems, it is major issue how to detect a device-free target, which does not carry any assistant electronical devices. When a target crosses the communication links, the target causes the fluctuation of received signal strength (RSS) values at the receiver side. However, the performance of the detection scheme based on RSS tends to be degraded because of noises in indoor environments. In this paper, we present various smoothing methods to mitigate the effect of noises for enhanced performance of target detection. To compare the performance of various smoothing methods, we performed experiments with IEEE 802.15.4 ZigBee devices. According to the experimental results, it is shown that the Gaussian kernel smoothing, the weighted moving average smoothing, and the exponential moving average smoothing methods can provide more suitable performance than other methods in terms of mean square error, while the exponential moving average smoothing provides more suitable performance in terms of detection accuracy and detection temporal accuracy.