Near Surface Imaging in Soils for Archeological Assessments

Not Available To protect the US against limited nuclear attacks, the NMD system must operate successfully in nuclear-disturbed environments. A key to successfully negating nuclear threat is selecting a battleplan that ensures that NMD system performance is not too severely degraded by nuclear effects. While prompt nuclear effects are easy to plan around, persistent nuclear environments are more problematic, especially with the fast-running, lower-fidelity algorithms that BMC2 must use. MRC has long been among the nation's leaders in predicting nuclear environments and their impacts on system performance. We will combine this experience with expertise in artificial neural networks (ANN) to develop and train an ANN for use as a real-time decision aid in selecting optimal battleplans that minimize direct and collateral nuclear impacts. We will develop our decision aid so it can be used as an integral element in the BMC2 battleplanning process, dynamically responding to an evolving threat and providing risk assessments that would not otherwise be available. It will assess multiple battleplans, then inform a human in control (HIC) of the probability of success and costs/benefits of each plan so they will be adequately informed when selecting an actual battleplan. The Phase I effort will demonstrate that a neural network can be trained to predict the outcome of a candidate NMD battleplan in the presence of possible nuclear bursts. It will provide the human-in-control with an assessment of the nuclear-induced risks to the performance of vital elements in the system. Similar decision aids could be developed for other ballistic missile defense program, such as THAAD, Navy TMD, or even the Israeli Arrow program. The concept could also be applied to TMD deployment planning to minimize the risks of nuclear, biological, or chemical collateral damage to civilian or military assets.