Tracking algorithms based on dynamics of individuals and multidimensional scaling

Accurate location of people is a key aspect of many applications such as resource management or security. In this paper, we explore the use of radio communication technologies to track people based on their dynamics. The network consists of two types of radio nodes: static nodes (anchors) and mobile nodes (individuals). From a set of sparse dissimilarity matrices with information about proximity or estimated distances between nodes and individualspsila dynamics at each time instant, we infer individualspsila trajectories. Depending on the information available, two algorithms are proposed: dynamic weighted multidimensional scaling with binary filter (DWMDS-BF) and dynamic weighted multidimensional scaling based on distance estimations (DWMDS-DE). DWMDS-BF is an algorithm that implements a binary filter function that obtains very good tracking results when only connectivity information is available and DWMDS-DE is designed for those networks where a good estimation of distances between nearby nodes is available. Both algorithms implement a dynamic component that regularizes the obtained trajectories according to individualspsila dynamics. Extensive simulations show the effectiveness and robustness of the proposed algorithms.