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.

Received Signal StrengthBased Indoor Localization Using Hierarchical Classification Commercial interests in indoor localization have been increasing in the past decade. The success of many applications relies at least partially on indoor localization that is expected to provide reliable indoor position information. WiFi received signal strength (RSS)based indoor localization techniques have attracted extensive attentions because WiFi access points (APs) are widely deployed and we can obtain the WiFi RSS measurements without extra hardware cost. In this paper, we propose a hierarchical classificationbased method as a new solution to the indoor localization problem. Within the developed approach, we first adopt an improved KMeans clustering algorithm to divide the area of interest into several zones and they are allowed to overlap with one another to improve the generalization capability of the following indoor positioning process. To find the localization result, the KNearest Neighbor (KNN) algorithm and support vector machine (SVM) with the oneversusone strategy are employed. The proposed method is implemented on a tablet, and its performance is evaluated in realworld environments. Experiment results reveal that the proposed method offers an improvement of 1.4% to 3.2% in terms of position classification accuracy and a reduction of 10% to 22% in terms of average positioning error compared with several benchmark methods.