A Dynamic Approach to Sensor Network Deployment for Target Detection in Unstructured, Expanding Search Areas

This thesis presents a novel dynamic deployment strategy for a network of static sensors using a probability model of target motion to detect un-trackable targets. The focus herein is on the dynamic and optimal deployment of the static sensor network. The network nodes are determined at regular time intervals throughout the search based on available real-time information. Optimality is achieved based on maximizing the probability of finding the target through the use of the novel iso-cumulative curve. It is adaptable to terrain variation, presence of obstacles, and can be re-calculated whenever target information, like a clue, is found to re-locate search effort. Simulations showed that the proposed methodology increases the success rate of target interception and reduces the mean detection time compared to uniform coverage-based approaches. In addition, the methodology was applied to a wilderness search and rescue scenario, where static sensors assisted mobile sensors in intercepting a target.M.A.S