Novel Battle Damage Assessment Using Sensor Networks

Vadum will develop a Statistical Resource Flow (SRF) software application fusing sensor measurements and simulations to estimate High Power Electro-Magnetic (HPEM) weapon effectiveness. The analysis uses expert and intelligence-derived target models to provide quantitative insights such as: (a) likely HPEM attack outcomes, (b) sensor allocation to verify attack effects, and (c) target reconstitution time estimates. The SRF uses machine learning to estimate engagement outcomes similar to traditional system reliability analysis. A novel modeling approach describes target and sensor operation in terms of input and output, (e.g. system resource flows), that capture analysis uncertainties.Synthetic data will be used for feasibility assessment since actual HPEM attack outcome data is limited.This Battle Damage Assessment (BDA) approach supports both planning and attack execution by leveraging the ability of the machine learning techniques to propagate probabilities in both forward and backward directions. The system will incorporate simulated faults (forward propagation) and sensor-observed evidence (back propagation) to estimate BDA in both planning and post attack phases. Emerging HPEM weapons such as the joint Air Force/Navy HIJENKS system will require these planning and assessment capabilities to integrate into the US weapons inventory.