Common Intelligence Picture Architecture for Adaptive Satellite Tasking
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current satellite data acquisition systems face inefficiencies due to their inability to adapt to dynamic environmental and operational conditions, leading to suboptimal data collection and resource waste, particularly when dealing with multiple platforms and complex scenarios requiring rapid response timing.
Innovation Solution
A modular architecture integrating a multi-source intelligence fusion system, satellite data acquisition optimization platform, and containerized analytics workbench for automated data integration and analysis, enabling real-time adaptation to changing conditions and optimizing collection across multiple platforms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional fixed scheduling systems or manual coordination are used for satellite data collection, then system simplicity is maintained, but adaptability to dynamic environmental conditions and operational constraints deteriorates
Solution Approach 1:
The patent implements dynamic tasking systems that continuously monitor environmental conditions, satellite positions, and operational constraints to adjust data collection schedules in real-time. The system transitions from static pre-planned schedules to dynamic adaptive scheduling, where collection opportunities are identified and executed based on current conditions rather than predetermined timelines.
Solution Approach 2:
The system incorporates feedback mechanisms that monitor collection outcomes, environmental condition changes, and satellite availability to continuously refine tasking decisions. This closed-loop approach enables the system to learn from past collections and adapt future scheduling based on actual performance data and changing operational requirements.
2Quantity of substance
If multiple satellite platforms are coordinated without optimization, then resource availability increases, but resource waste due to suboptimal collection timing and environmental conditions worsens
Solution Approach 1:
The patent merges multiple satellite platform capabilities into a unified tasking system that coordinates collections across different satellites, sensors, and orbital positions. By consolidating tasking authority and integrating platform capabilities, the system achieves synergistic resource utilization that prevents duplicate collections and maximizes the value of each satellite pass.
Solution Approach 2:
The system dynamically adjusts collection parameters such as timing, sensor selection, and target priorities based on real-time environmental conditions including weather patterns, orbital mechanics, and target availability. This parameter optimization ensures collections occur at maximum effectiveness rather than following fixed schedules.
3Loss of information
If comprehensive multi-source data integration is implemented, then intelligence picture completeness improves, but processing complexity and time requirements worsen
Solution Approach 1:
The patent segments the intelligence processing function into specialized components including satellite data acquisition optimization, environmental condition monitoring, target custody tracking with confidence intervals, and image enhancement. This modular architecture enables parallel processing of different data types and sources, reducing overall processing time while maintaining comprehensive intelligence picture development.
Solution Approach 2:
The system performs preliminary processing and filtering of multi-source data before full integration, pre-identifying relevant collection opportunities and prioritizing data sources based on anticipated intelligence value. This advance preparation reduces the processing burden during actual intelligence picture generation and enables faster response to time-sensitive requirements.
Data Source
AI summary
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. The system implements automated workflows for intelligence analysis and dissemination while maintaining security controls, with the containerized analytics workbench providing pattern of life analysis and dynamic exploitation through containerized microservices.