An adaptive static-sensor network deployment strategy for detecting mobile targets

The mobile-target search problem has been, typically, addressed in the literature through the sole use of mobile agents. Recently, however, it has been shown that the use of static-sensor networks could significantly contribute to the likelihood of detecting a mobile target and in a shorter time. In this paper, thus, we propose a novel adaptive and optimal static-sensor network deployment strategy to detect un-trackable targets in unstructured environments. The strategy utilizes a probabilistic target-motion model representative of the demographic group to which the target belongs and realtime location history information to construct a target-location probability distribution function over the search region. The novelty of our strategy lies in the utilization of a time-varying target-location probability distribution in order to deploy sensors in a manner that is both maximally adaptive and optimal for every deployment. Network deployment for a wilderness search and rescue problem is also presented in detail as an example case. Furthermore, numerous factors that may influence the performance of our deployment strategy are discussed, including a network coverage comparative study.