Sequential measurement-dependent noisy search
Sung-En Chiu, Tara Javidi · 2016
Consider a target search problem on a unit interval where at any given time an agent can choose a region to probe into for the presence of the target in that region. The measurement noise is assumed to be increasing with the size of the search region the agent chooses. In this paper, a single-phase sequential and adaptive search algorithm is proposed and shown to achieve the best possible targeting rate and error exponent among all adaptive search algorithms. The proposed algorithm simply adopts a low complexity sorting operation on the posterior of the target and then pick up locations with larger posterior until the probability that the search region contains the target is closest to half.