Abstract: A system is disclosed for generating a common intelligence picture using a modular architecture that automates multi-source intelligence fusion, behavior prediction, and custody tracking. The system receives priority intelligence requirements, extracts tasking parameters using a language model, and generates collection strategies based on feasibility, resource availability, and historical outcomes. Incoming sensor reports are normalized, correlated, and evaluated using predictive models that incorporate pattern-of-life behavior, terrain constraints, and movement feasibility. The system supports tracking of individual and group targets, assigns behavior alignment and identity confidence scores, and triggers alerts for anomalous or evasive behavior. Custody records are continuously maintained and updated, with reacquisition workflows initiated when identity continuity degrades. Collection plans are refined in real time based on predictive scoring and tasking feedback.

Abstract: A system and method for edge-deployed automated target recognition with distributed processing architecture. The system processes satellite sensor data directly at the edge using coordinated services for object detection and tracking. Detection services identify objects with geodetic localization while tracking services maintain persistent tracks across multiple frames. A message broker enables asynchronous communication between components and manages data flow for both real-time streaming and historical queries. The architecture supports track custody transfer between distributed nodes, allowing continuous object tracking across multiple satellite systems. This edge-based approach eliminates cloud processing dependencies and reduces latency from minutes to seconds. The system's distributed design enables autonomous operation at the sensor edge while maintaining coordination capabilities with other nodes, making it suitable for time-sensitive applications requiring real-time object recognition and tracking.