SBIR/STTR Award attributes
Effective Anti-Submarine Warfare (ASW) operations against near-peer threats require increasing numbers of sensors and data sources, more data from each sensor, and a means of developing a live, consistent, and accurate Unified Operational Picture from this this torrent of data. Even well-trained and experienced Operators can be overwhelmed by the increasing cascade of signals from a growing number of sensors and data feeds with varying data formats and certainty levels. Our team at ThayerMahan, led by Vice Admiral (ret) Mike Connor, has extensive experience with this challenge of synthesizing a clear target picture from masses of data from multiple sensors and domains. We deploy and operate 24/7 maritime surveillance-as-a-service operations, using long-dwell autonomous sensing systems. We process data streams into threat representations on-board those systems and escalate/upload potential target contact reports to our operations center. We have performed or are currently performing on contracts with the Office of Naval Research (ONR) and the Joint Staff on behalf of US Southern Command (SOUTHCOM) and US Indo-Pacific Command (INDOPACOM) to process large volumes of sensor data from passive acoustic arrays and RF signal detection systems, which we integrate with AIS data and environmental data to form a Unified Operational picture of the activity in maritime surveillance areas. We will develop Acoustic Sense and Track (ASSENT) for this SBIR – an intelligent automation and machine learning (ML) system which fuses data and sensor streams into a persistent, 3D unified picture of the operational and threat environment. We will prototype and demonstrate this capability in Phase I using our existing data streams (acoustic, RF, AIS) to show the value of our approach. Our approach is a multi-layered AI system with four critical components: a) Featurization of sensor data – our team’s expert knowledge informs our ML pipeline and algorithms which power transformations of sensor data into data features which facilitate target identification and tracking; b) 3D Projection for Data Fusion - we project our featurized sensor outputs into entity-centric representations in 3D space, using deep associative vectors to match and enhance each threat entity representation with data from multiple sources; c) Forward Projection of Target Location – we generate trajectory projections for each of the entities in the operational environment to bridge discontinuous detections and identify signals pertaining to the same object across sensors and time; d) Flexible Data packages and Interfaces for Operators, Fire Control, and C2 – providing the right information in the right format to each user of the system for maximum ASW effectiveness, from the SQQ-89 operator to fire control and C2. Our solution delivers a clear, entity-based, fused operational picture to facilitate improved C2 understanding and tactical decision-making by system operators and ASW decision-makers.
