Applying background learning algorithms to radio tomographic imaging

Radio tomographic imaging (RTI) is an emerging technique which obtains images of passive targets (i.e., not carrying electronic device) within a wireless sensor network using received signal strength (RSS). One major problem that restricts the application of RTI is the difficulty to model the variations of RSS measurements caused by moving targets in different multipath environments. This paper proposes to apply background learning algorithm to RTI system to model variations. Compared with previous RSS-based device free localization methods, the proposed method achieves higher accuracy in multi-target and time-varying environment without offline training. Firstly, two fundamental background learning algorithms, mixture of gaussians and kernel density estimation, are introduced to calculate the probabilities of links being affected by targets using RSS measurement. Then, Tikhonov regularization is applied to the reconstruction of images using the probabilities. Experimental results show that the proposed approach achieves high accuracy and increases the RSS-network capacity considerably.