Crowdsourced Satellite Tracking for Orbital Collision Threats
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Solution Overview
Problem
The increasing number of satellites and orbital debris in space makes collision detection and prevention a challenging task, with potential cascading effects that could render low Earth orbits unusable for long-term satellite operations.
Innovation Solution
A crowdsource satellite network comprising multiple satellites collaborates via edge/distributed computing to detect, track, and analyze orbiting objects, generating orbital models to predict and classify impact threats, and initiate proactive actions to avoid collisions, such as changing satellite paths or deploying debris collectors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a traditional single-satellite system tracks orbiting objects, then the system complexity is low, but the measurement precision and reliability of collision detection deteriorate due to limited coverage and data points
Solution Approach 1:
The patent merges the capabilities of multiple satellites into a unified crowdsource network that collectively performs collision detection and orbital tracking. By combining data from multiple satellites, the system achieves higher measurement precision without requiring each individual satellite to have enhanced capabilities, thus resolving the contradiction between precision and complexity.
Solution Approach 2:
The patent makes each satellite in the network multi-functional by enabling them to perform collision detection, orbital tracking, and data sharing simultaneously. This universal capability across all satellites allows the system to achieve high measurement precision through collective effort while maintaining relatively simple individual satellite designs.
2Reliability
If the satellite network continuously monitors all orbiting objects, then the reliability of collision avoidance is improved, but the network bandwidth consumption increases
Solution Approach 1:
The patent extracts and processes only the most critical collision risk data from the vast amount of orbital information. By identifying and focusing on objects and trajectories that pose actual collision risks, the system maintains high reliability for collision avoidance while significantly reducing the bandwidth required for continuous monitoring compared to processing all orbital data equally.
Solution Approach 2:
The patent performs preliminary data filtering and risk assessment at the satellite level before transmitting data to the ground station. This preliminary action ensures that only high-priority collision risk information is transmitted, maintaining collision avoidance reliability while minimizing network bandwidth consumption by avoiding transmission of low-risk or redundant data.
3Productivity
If individual satellites perform comprehensive orbital analysis independently, then the device complexity is low, but the productivity and response time for collision detection deteriorate
Solution Approach 1:
The patent merges the computational tasks of multiple satellites into a coordinated collaborative system. By combining processing capabilities and sharing computational workload, the system achieves higher productivity and faster response times for collision detection than any single satellite could achieve independently, while managing complexity through standardized collaboration protocols.
Solution Approach 2:
The patent implements feedback mechanisms where satellites receive real-time information about collision risks and orbital conditions from the network, allowing them to adjust their monitoring and computational priorities dynamically. This feedback loop enables faster response times by directing computational resources to the most critical threats, improving overall productivity while managing complexity through automated coordination.
Data Source
AI summary
Described are techniques for determining an impact threat of an orbiting object by a crowdsource satellite network. The techniques include capturing, by a satellite included in a crowdsource satellite network, information associated with an orbiting object detected by the satellite. The techniques further include providing, by the satellite, the information associated with the orbiting object to the crowdsource satellite network. The techniques further include collaborating by the satellite with other satellites in the crowdsource satellite network to track the orbiting object, generate an orbital model of the orbiting object, and to analyze a predicted path of the orbiting object represented by the orbital model to determine an impact threat associated with the predicted path of the orbiting object.