Leveraging ROC Adjustments for Optimizing UUV Risk-based Search Planning

Search planning for UUV missions involves a complex optimization problem over multiple degrees of freedom. The detection performance of individual searches is governed by the local environment and the sensor settings that determine the rate at which target objects of interest can be identified. This paper addresses some of the computational aspects of collaborative search planning when multiple search agents seek to find hidden objects (i.e. mines) in adverse local operating environments where the detection process is prone to false alarms. For these conditions, we apply a receiver operator characteristic (ROC) analysis to model detection performance and develop a Bayesian risk objective that enumerates the required number of detections for validation as part of the modeling paradigm. In this paper we consider an expanded optimization process whereby ROC operating points are optimized simultaneously for all validation criteria with the best performing combinations selected. Details of its application within a broader search planning context are discussed. An analysis of this optimization process is presented. Numerical results are provided to demonstrate the effectiveness of the approach.