Executive Summary
This report explores integrating generative AI (GenAI) and large language models (LLMs) with computational models to enhance maritime Intelligence, Surveillance, and Reconnaissance (ISR) for predicting vessel destinations. A five-layer architecture was developed and validated, demonstrating successful destination prediction from incomplete satellite data. The research supports the U.S. Pacific Fleet and aims to improve maritime domain awareness for military and security operations.
Why It Matters
This document is crucial for defense analysts as it details a novel approach to enhance maritime intelligence and surveillance using generative AI, directly impacting naval operations and national security.
Key Takeaways
- Generative AI and Large Language Models (LLMs) can significantly enhance maritime Intelligence, Surveillance, and Reconnaissance (ISR) by predicting vessel destinations from incomplete satellite tracking data.
Source evidence · PDF page 3
This project investigates whether the integration of a large language model (LLM) with a local computational model can enhance the intelligence, surveillance, and reconnaissance (ISR) capabilities and advance maritime domain awareness, specifically for predicting vessel destinations from incomplete satellite tracking data.
- A five-layer architecture integrating GenAI with quantitative analysis of vessel trajectories was developed, using satellite constellations to track vessels and predict routes.
Source evidence · PDF page 3
We developed a five-layer architecture that integrates generative AI with quantitative analysis of a vessel trajectory. The system employs two low-earth orbit satellite constellations, each consisting of 10 satellites, tracking vessels across the Pacific Ocean.
- Future research should focus on extending GenAI's role to detect, identify, and track dark or deceptive vessels using multi-intelligence sources.
Source evidence · PDF page 5
Further research must extend the role of GenAI to the challenging problem of detecting, identifying, and tracking dark or deceptive vessels that seek to evade maritime ISR. This research should study the use of Multi-INT sources: Automatic Identification System (AIS), space synthetic aperture radar (SAR) imaging, space electro-optical imaging, space electronic emissions, and maritime databases.
Strategic Relevance
The integration of generative AI into maritime ISR processes offers a strategic advantage by improving real-time intelligence, enhancing maritime domain awareness, and enabling more effective threat assessment and mission planning for naval forces.