Target localization using proximity binary sensors

This works presents the maximum likelihood localization (ML) algorithm for multi-target localization using proximity-based sensor networks. Proximity sensors simply report a single binary value indicating whether or not a target is near. The ML approach requires a hill climbing algorithm to find the peak, and its ability to find the global peak is determined by the initial estimates for the target locations. This paper investigates three methods to initialize the ML algorithm: 1) centroid of k-means clustering, 2) centroid of clique clustering, and 3) peak in the 1-target likelihood surface. To provide a performance bound for the initialization methods, the paper also considers the ground truth target positions as initial estimates. Simulations compare the ability of these methods to resolve and localize two targets. The simulations demonstrate that the clique clustering technique out-performs k-means clustering and is nearly as effective as the 1-target likelihood peak methods at a fraction of the computational cost.