Sensor interceptor operational policy optimization for maritime interdiction missions

Abstract

Maritime Interdiction Missions (MIM) are of great interest and high operational importance to the U.S. Navy, the U.S. Coast Guard, and allied forces. The MIM scenario discussed in this thesis includes an area of interest with multiple neutral and hostile vessels moving through this area, and an interdiction force consisting of an unmanned aerial vehicle (UAV) and an intercepting vessel, whose objectives are to search, identify, and intercept hostile vessels within a given time frame. In this thesis, we develop Stochastic Dynamic Programming models, which represent the MIM scenario. While a theoretical method of producing an optimal decision policy for the interdiction force is presented in this thesis, it is shown that such computation is intractable. The models developed in this study are used to analyze and evaluate the performance of a heuristic decision policy that we recommend to be applied by the interdiction force. Based on a numerical case study, which includes several representative MIM scenarios, we show that the number of intercepted hostile vessels following the heuristic decision policy is at least 60% of the number of hostile vessels intercepted following the optimal decision policy. Based on the results of the heuristic performance in the numerical case studies, we recommend the implementation of our suggested heuristic in an operational decision aid for Maritime Interdiction Missions.