The Gray Analysis and Machine Learning for Device-Free Multitarget Localization in Passive UHF RFID Environments

Abstract

The device-free localization (DFL) has promising application prospects in intrusion detection, emergency rescue, and smart homes, because it does not require the target to carry any auxiliary positioning equipment. Radio tomographic imaging (RTI) is one of the most potential DFL techniques and has many advantages over other methods. However, in passive ultrahigh frequency radio frequency identification scenario, there are few researches and many problems to be solved. The difficult but urgent matter is how to identify the locations of multiple targets from many false targets and artifacts. This paper proposes a novel method based on cross-sectional scan (CSS), gray value distribution analysis (GVDA), and naive Bayes classifier to solve this problem. The CSS obtains the gray value distributions of the local maximum pixel in an RTI reconstructed image. Then, the GVDA extracts several characteristic parameters from gray value distributions, such as the size, height, and shape of the peak. Finally, the naive Bayes classifier utilizes these series of characteristics to judge whether local maximum pixels are false targets or real targets. The method can also recognize the number of targets that are very close to each other. Simulation and experimental results show that this method can accurately determine the locations and the number of targets.