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 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: An automated satellite data acquisition optimization platform is disclosed that dynamically manages and optimizes satellite-based data collection operations. The platform incorporates real-time analysis of multiple variables including but not limited to weather conditions, orbital parameters, sensor capabilities, and collection requirements to determine optimal data acquisition opportunities. Through continuous monitoring and assessment of physical feasibility factors such as satellite status, terrain obstructions, and environmental conditions, alongside competitive feasibility considerations including orbit availability and targeting geometries, the platform automatically selects and tasks the most suitable collection assets. The platform employs data enhancement capabilities to improve collection quality and maintains quality control through automated validation processes.

Abstract: An automated satellite data acquisition optimization platform is disclosed that dynamically manages and optimizes satellite-based data collection operations. The platform incorporates real-time analysis of multiple variables including but not limited to weather conditions, orbital parameters, sensor capabilities, and collection requirements to determine optimal data acquisition opportunities. Through continuous monitoring and assessment of physical feasibility factors such as satellite status, terrain obstructions, and environmental conditions, alongside competitive feasibility considerations including orbit availability and targeting geometries, the platform automatically selects and tasks the most suitable collection assets. The platform employs data enhancement capabilities to improve collection quality and maintains quality control through automated validation processes.

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.

Abstract: A modular open system architecture for common intelligence picture generation is disclosed. The system receives intelligence requirements through a multimodal artificial intelligence system and calculates collection feasibility across multiple intelligence sources based on physical and temporal conditions. The system develops integrated collection plans through the containerized analytics workbench using containerized analytics modules and processes intelligence through GPU-accelerated deep learning models for automated target recognition. Satellite collection is orchestrated through satellite data acquisition optimization platform by evaluating weather conditions, orbital parameters, and sensor capabilities, while space domain awareness is maintained through space domain awareness system for real-time collection asset management. Multi-source intelligence data is fused through multi-source intelligence fusion system to populate a common intelligence picture.

Abstract: A system and method for object monitoring, detection, and segmentation in electro-optical (EO) satellite imagery data comprising an image processing engine configured to prepare EO data for analysis by an inference engine which utilizes one or more trained models to perform object detection or image segmentation. The workflow begins with ingesting satellite data, followed by de-hazing to remove atmospheric interference. Image enhancement improves resolution and geo-registration ensures precise spatial alignment. The processed image is then fed into a machine learning-based object detection or image segmentation network, trained to identify specific objects of interest. This integrated approach leverages advanced technologies to extract actionable insights from satellite data, enabling efficient and precise object monitoring, detection, and segmentation.