Acoustic Counter-Detection Risk Management (ACDRM) Evolutionary Machine Learning (EML)

In this SBIR project, Daniel H. Wagner Associates (DHWA) will develop Acoustic Counter-Detection Risk Management (ACDRM) Evolutionary Machine Learning (EML) algorithms and software, leveraging our existing Operational Route Planner (ORP), that will automatically produce a recommended, optimized ASW search that accurately computes cumulative counter-detection probability (CC-DP), accounts for the ability of threat submarines to react to counter-detections of friendly ASW assets, and will use multi-objective optimization techniques to provide multiple alternative, distinct, and non-dominated search plans to the Commander/search planner.  ACDM will also explicitly account for the uncertainties in estimating threat counter-detection (and friendly search) effectiveness using our recently developed techniques for estimating probabilistic passive and active signal excess.