Satellite Behavior Discovery via AMIGO Game Reasoning
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Solution Overview
Problem
Current satellite behavior detection technologies face challenges such as partial observable actions, resident space object tracking, uncertainty modeling, real-time requirements, and computational intractability, which hinder effective space situational awareness and space superiority.
Innovation Solution
The adaptive Markov inference game optimization (AMIGO) method employs transfer learning, zero-shot learning, manifold learning, and Markov game modeling to predict satellite behavior by leveraging sensor data and uncertainty propagation, enabling rapid discovery and management of space sensing assets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional satellite tracking algorithms are used, then basic tracking functionality is achieved, but computational complexity becomes intractable and real-time performance cannot be achieved
Solution Approach 1:
The patent segments the satellite behavior discovery problem into distinct components: maneuver detection, anomaly classification, and uncertainty modeling. Each component is handled by specialized algorithms (e.g., orbit determination for tracking, machine learning classifiers for behavior recognition), avoiding the need for a single monolithic complex algorithm and enabling parallel processing for real-time performance.
Solution Approach 2:
The patent introduces intermediate representations such as orbital elements and state vectors as mediators between raw sensor data and behavior classification. These intermediaries simplify the computational complexity by transforming complex sensor measurements into standardized parameters that can be processed efficiently by subsequent algorithms.
2Loss of information
If comprehensive satellite behavior monitoring is implemented, then space situational awareness is improved, but measurement uncertainties increase due to partial observability
Solution Approach 1:
The patent implements feedback mechanisms where detection uncertainties are continuously updated based on new sensor measurements and predictions. The system uses predicted satellite states to guide sensor pointing and measurement selection, creating a closed-loop system that reduces uncertainties over time while maintaining comprehensive situation awareness through probabilistic state representations.
3Measurement precision
If machine learning methods are applied for behavior classification, then detection accuracy is improved, but computational requirements increase making real-time processing difficult
Solution Approach 1:
The patent performs preliminary feature extraction and data preprocessing to transform raw sensor measurements into compact feature vectors that capture essential behavior characteristics. This preliminary action reduces the dimensionality and complexity of input data for machine learning classifiers, enabling accurate real-time behavior classification with lower computational energy consumption during critical detection phases.
Data Source
AI summary
A method for rapid discovery of satellite behavior, applied to a pursuit-evasion system including at least one satellite and a plurality of space sensing assets. The method includes performing transfer learning and zero-shot learning to obtain a semantic layer using space data information. The space data information includes simulated space data based on a physical model. The method further includes obtaining measured space-activity data of the satellite from the space sensing assets; performing manifold learning on the measured space-activity data to obtain measured state-related parameters of the satellite; modeling the state uncertainty and the uncertainty propagation of the satellite based on the measured state-related parameters; and performing game reasoning based on a Markov game model to predict satellite behavior and management of the plurality of space sensing assets according to the semantic layer and the modeled state uncertainty and uncertainty propagation.