On target localization with communication costs via tensor completion: A multi-modal approach

The problem of active target detection using low rank methods is explored. In prior work, a strategy was proposed based on matrix completion for randomly sampling a field combined with binary search to localize a target. Herein, two innovations are explored: the consideration of tensor-completion in order to exploit multi-modal data and the examination of the costs associated with communication. In particular, the random samples are collected in neighborhoods wherein the quality of the observation is a function of the distance of the sampling point to the centroid of the neighborhood. Due to the tradeoff between communication quality and sampling quality, there is an optimal neighborhood size.