Radio Tomographic Imaging Localization Based on Transformer Model

Abstract

Device-free localization (DFL) is an indispensable part of disaster relief and anti-terrorism operations. Radio tomographic imaging (RTI) emerges for locating targets in the area by using received signal strength (RSS) measurements from a wireless sensor network. In this paper, we briefly analyze the forward model of RTI and proposes a deep learning based RTI method to achieve multi-target location with high precision. Compared with the traditional RTI algorithm, this method has advantages in distinguishing multiple targets and computing efficiency. Simulation and experimental results verify the effectiveness of the proposed method.