Indoor people tracking based on dynamic weighted MultiDimensional scaling

Accurate location of people in indoor environments is a key aspect of many applications such as resource management or security. In this paper, we explore the use of short-range radio technologies to track people indoors. The network consists of two kind of radio nodes: static nodes (anchors) and mobile nodes (people). From a set of sparse connectivity matrices (people vs. people and people vs. anchors) at each time instant and people's dynamics, we infer people's trajectories. To combine connectivity and dynamic information, we propose an extension of Multidimensional Scaling (MDS), Dynamic Weighted MDS (DWMDS), that finds an embedding of people's trajectories (x and y coordinates of people through time). DWMDS has proven to be more accurate and effective, especially for low connectivity degree networks (i.e. sparse networks), compared to existing location algorithms. Extensive simulations show the effectiveness and robustness of the proposed algorithm.