SBIR/STTR Award attributes
At sea commanders must maintain situational awareness that includes a wide range of surface and subsurface contacts with multiple acoustic (AC), radio frequency (RF), optical (VIS), and thermal (IR) signatures. Automatic detection and threat classification can dramatically improve their response time and course of action decisions. This proposal demonstrates the feasibility of artificial intelligence machine learning neural nets by delivering a USV/UUV Naval Abnormal Signal Detection & Classification (NASDC) intelligent system prototype to learn operational normal background signatures and historical signatures for vessels of interest, plus other data from a varying subset of hybrid on-board sensors (e.g., differing combinations of AC, RF, VIS, and IR bands). The NASDC will be tested on historical and simulated data for in-stride detection of unknown abnormal temporal signatures and multi-spectral historical signature classification confidence scores. NASDC will also provide a categorization results trust score for each time window based upon the similarity of the test data to the full training set. NASDC will be trained and tested based upon noise models and acoustic performance simulations to characterize environments plus historical experiment data at Applied Ocean Sciences (AOS).
