Multi-channel RTI fusion based on improved joint sparse model

Abstract

Radio tomographic imaging (RTI) is an emerging device-free localization (DFL) technology enabling the localization of people and other objects without requiring them to carry any electronic device. This paper uses the way of channel diversity to improve the RTI’s positioning accuracy against the issues which are vulnerable to environmental noise. In this paper, based on the multi-channel RTI, an improved joint sparse multi-channel fusion method (IJSM) is proposed. The method decomposes each channel’s RTI image into high frequency components and low frequency components, and designs corresponding fusion rules respectively to realize the refined fusion of multi-channel information. Experimental results show that this method can fully explore the correlation among channels and synthesize the favorable information of each channel so as to remove pseudo targets and background noise. Finally this method achieves a well positioning performance.