Joint Sensing/Sampling Optimization for Surface Drifting Mine Detection with High-Resolution Drift Model.

摘要

Every mine countermeasure (MCM) operation is a balance of time versus risk. In attempting to reduce time and risk, it is in the interests of the MCM community to use unmanned, stationary sensors to detect and monitor drifting mines through harbor inlets and straits. A network of stationary sensors positioned along an area of interest could be critical in such a process by removing the MCM warfighter from a threat area and reducing the time required to detect a moving target. Although many studies have been conducted to optimize sensors and sensor networks for moving target detection, few of them considered the effects of the environment. In a drifting mine scenario, an oceanographic drift model could offer an estimation of surrounding environmental effects and provide time critical estimations of target movement. These approximations can be used to further optimize sensor network components and locations through a defined methodology using estimated detection probabilities. The goal of this research is to provide such a methodology by modeling idealized stationary sensors and surface drift for the Hampton Roads Inlet.