The Gray Analysis and Machine Learning for DeviceFree Multitarget Localization in Passive UHF RFID Environments The devicefree 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 crosssectional 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.

Target Localization using Machine Learning and Belief Functions: Application for Elderly People in Indoor Environments Target localization is an important issue in indoor environments. It is vital to assist elderly people in distress. In this paper, we propose a target localization method using machine learning and the belief functions theory. At first, the target area is partitioned into cells and a database of received signal strengths is collected in each cell. In order to consider ambiguity of data, subsets of cells are created. Machine learning is used to assign masses for these subsets. These masses are then used as inputs for the belief functions theory. The latter combines evidence and uses decisionmaking criteria to associate each cell with a confidence level of having the target, thus the person, residing at each cell. Experimental results show the efficiency of the proposed method in real case scenario, as compared with other stateoftheart techniques.