Automated pattern recognition methods to identify nuclear explosions

Reliable automated pattern recognition in the current system is limited by excessive noise and clutter combined with insufficient and/or ambiguous features. Our team consisting of AcornSI and Leidos thus propose a multi-step approach based on the combined effects of improved detection, feature extraction, phase identification, and global association. A key innovation here is creating a new classification feature and confidence based on machine vision to directly classify waveform spectrograms. Improved legacy and new features are then sent to a (possibly nested) support vector machine (SVM) to automatically determine event classifications with associated confidence scores.