LEO Satellite Transit Prediction for Autonomous Constellation Control
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Solution Overview
Problem
Managing and controlling satellite constellations is complex due to the large number of satellites, distance from control points, and dynamic space environment, leading to issues like delay in updates and lack of communication.
Innovation Solution
A satellite optimization management system using a neural network for intent interpretation, a communication interface for data transmission, and a micro-batch delivery module for efficient command execution, along with a virtual simulation infrastructure for trajectory planning and fault tolerance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional satellite control systems are used to manage large satellite constellations, then infrastructure stability is maintained, but communication delays and operational inefficiencies increase
Solution Approach 1:
The patent segments satellite control into autonomous satellite nodes that can independently execute commands and make decisions, dividing the centralized control function into distributed autonomous units. This eliminates communication delays by enabling satellites to operate independently rather than waiting for ground control responses.
Solution Approach 2:
The patent implements predictive systems that pre-calculate orbital trajectories and mission parameters in advance using machine learning models. By preparing commands and predictions before they are needed, the system reduces real-time communication requirements and enables faster response to dynamic space environments.
2Area of stationary object
If more satellites are added to the constellation to improve coverage, then service area expands, but system complexity increases
Solution Approach 1:
Each satellite node operates autonomously with onboard intelligence to manage its own operations, fault detection, and coordination with other satellites. This self-service capability eliminates the need for complex centralized management infrastructure, allowing the constellation to scale without proportionally increasing management complexity.
Solution Approach 2:
The patent uses machine learning models to dynamically adjust operational parameters such as orbital trajectories, communication schedules, and task allocation based on real-time conditions. This adaptive parameter optimization enables efficient management of large constellations by automatically coordinating satellite operations without manual intervention.
3Productivity
If real-time command execution is implemented to improve responsiveness, then operational efficiency increases, but error propagation risk increases
Solution Approach 1:
The patent implements fault detection and recovery systems that continuously monitor satellite operations and provide feedback to correct errors in real-time. Autonomous fault detection capabilities enable immediate identification and correction of command execution errors, maintaining high responsiveness while ensuring system reliability through continuous verification.
Solution Approach 2:
The system incorporates predictive analytics and simulation capabilities that identify potential errors and generate corrective commands in advance. By preparing backup commands and predicting potential failures before they occur, the system cushions against error propagation while maintaining real-time operational efficiency.
Data Source
AI summary
The disclosed system provides advanced mission control predictive systems for low earth orbit semi-autonomous satellites. In operation, the system may include a satellite transit behavior prediction module configured to train a neural network based on current and past satellite orbital transit paths so as to predict future transit paths. Knowing precise future satellite transit paths enables a ground-based satellite control system to more efficiently (and with greater accuracy) control certain operations of the satellites. For example, a ground-based satellite control system can prepare for communications to commence at a particular time. Legacy systems can only predict one or two days in advance However, using a neural network that not only takes into consideration historical transit data, but also learned details such as solar wind, cloud patterns, etc., the neural network predicts future satellite transit paths with statistically-certain accuracy leading to a much narrower cone of uncertainty.