Abstract: A system is disclosed for generating a common intelligence picture through latent space fusion and threat forecasting. Priority intelligence requirements are processed using language and machine learning models to generate structured tasking and prioritize multi-modal sensor collection. Data from geospatial, cyber, radio frequency, and behavioral sources is embedded into domain-specific representations and fused into a unified latent space. The system models behavioral trajectories, detects anomalies, and maintains digital twins of entities and regions. Deviations from expected behavior may trigger alerts, causal inference, attribution hypotheses, and deterrence simulations. Adversary behavior is forecast in latent space, enabling evaluation of intervention strategies with impact and timing assessments. Visualizations, alerts, and reports are generated based on latent divergence and simulation outputs.

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.