Award Data

Using the AI/ML technology of Long Short Term Memory to detect and heal data contained in Navy supply chain information systems. Training the LSTM data model to inherit the Subject Matter Expertise value to produce accurate material forecasting reports.

Predictive Analytics for NOrmalcy Reasoning and AnoMaly Analysis (PANORAMA) is a machine learning (ML) tool for automatic identification system (AIS) data that learns maritime patterns of life and detects anomalous vessel behavior. PANORAMA learns what is normal for a given ship, taking into account: (1) that ship’s past behavior, (2) the past behavior of similar ships, (3) normalcy patterns in ...

Coastal and marine navigation, as well as doing so safely, requires the correct identification of vessel type and prediction of behaviors. While the development of Automatic Identification System (AIS) has eased the burden of vessel localization and identification, errors still occur due to improperly configured messages, whether done innocently or maliciously.In order to increase the safety of ou ...

For N193-A01, Beacon is proposing to develop innovative AI and ML technologies that can predict and prescribe items for resupply within Naval Air Operations. The approach will be to build upon previous successful SBIR transitioned shipboard digital assets. The innovation proposed is to use AI / ML to inform and provide actionable intelligence into the supply chain from the operational point-of-per ...

Cybersecurity risk assessments practiced today are mostly qualitative in nature and inadequate to address the real-time cyber threat, especially advanced persistent threat (APT) in the enterprise. Cybersecurity operations rely heavily on various security tools running in silos and generating large volumes of security data that are difficult to comprehend and provide little value for actionable dec ...

Automatic Identification System (AIS) messages are a rich source of data for situational awareness of maritime surface vessels and identification of potentially anomalous vessels, however they are too voluminous and heterogeneously sequenced for a human operator and traditional compute algorithms to make sense of at a regional scale. While these challenges can limit the effectiveness of traditiona ...

Research Proposed: Research is proposed to research and design a novel Unmanned Vessel Health Monitoring System (UVHMS), providing an onboard diagnostics and prognostics system for USV and UUV, supporting the identification of active and imminent platform faults. Using onboard sensors and data processing, multi-sensor cross-correlation, pattern recognition and anomaly detection, the UVHMS will ess ...

STORMY (Storm-Aware USV Operations using Multi-Objective Autonomy) will allow a USV to weigh global mission objectives such as arrivals at a time-prioritized sequence of locations against damage to the vessel due to high seas or collision with other vessels during long-duration missions. The proposed WAVES technology addresses these issues with a two-tiered approach to vehicle motion which conside ...

To be mission-relevant, Navy unmanned surface vehicles (USVs) must perceive and behave like manned ships. Cameras are valuable for supporting human-like perception because their high-resolution detail enables ship classification, tracking, COLREGS determination, and threat characterization. Human watchstanders use a variety of cues and heuristics to scan the surroundings and contacts optimally, bu ...

The US Navy has embarked on an effort to diversify its fleet to include fully unmanned surface platforms. As theendurance of these unmanned systems extends from days to months, pre-mission planning will be insufficient to handle uncertainty in operations. Nowhere is the resultant need for adaptive planning more pressing than in weather avoidance, where the dynamics of nature limits practical forec ...