Satellite Trajectory Control for Forward-Looking Cloud Avoidance
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Solution Overview
Problem
Satellites face challenges in capturing imagery due to cloud cover, as existing systems lack effective methods to avoid clouds along their trajectories, leading to unusable imagery and inefficient orbit management.
Innovation Solution
A computing system on board the satellite uses a forward-looking sensor and a model, such as a convolutional neural network, to obtain image data, determine cloud coverage, and adjust the satellite's trajectory to avoid cloud-covered areas by generating command instructions based on metadata like sensor temperature and sun angle.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If satellites follow fixed trajectories to capture imagery, then orbital management is simplified, but cloud cover causes unusable imagery
Solution Approach 1:
The system performs preliminary cloud detection using forward-looking sensors and machine learning models before the satellite reaches the imaging target. This advance detection allows the satellite to adjust its trajectory proactively to avoid cloud-covered areas, ensuring imagery quality while maintaining relatively simple orbital management
Solution Approach 2:
The system continuously monitors cloud conditions using onboard sensors and feeds this information back to the trajectory control system. This feedback loop enables real-time trajectory adjustments to avoid clouds while maintaining simplified orbital management through automated control
2Reliability
If satellites adjust trajectories to avoid clouds, then imagery quality improves, but orbit management becomes inefficient
Solution Approach 1:
By detecting clouds in advance using forward-looking sensors and performing trajectory calculations before reaching imaging targets, the system minimizes mid-course corrections and maintains efficient orbital management while ensuring imagery quality
Solution Approach 2:
The satellite autonomously performs cloud detection, trajectory calculation, and course correction without requiring ground control intervention. This self-service capability maintains imagery quality while preserving orbit management efficiency through automated decision-making
3Measurement precision
If complex processing is performed onboard to detect clouds, then cloud avoidance accuracy improves, but computing resources are consumed
Solution Approach 1:
The system segments cloud detection into multiple stages: initial screening using forward-looking sensors, detailed analysis using machine learning models only for potential targets, and final verification before trajectory adjustment. This segmentation achieves high detection accuracy while consuming computing resources only when necessary
Solution Approach 2:
The system applies full computational power for cloud detection only to regions of interest identified by forward-looking sensors, rather than processing entire orbital paths. This partial action approach maintains high detection accuracy while conserving computing resources
Data Source
AI summary
Systems and methods for cloud avoidance are presented. For example, a computing system may be configured to obtain image data from a forward-looking sensor of a satellite, wherein the satellite is traveling along a current trajectory. The computing system may be configured to determine, using a model and based on the image data, an imaging target and cloud coverage associated with the imaging target. The computing system may be configured to determine a comparison between the cloud coverage associated with the imaging target and a threshold level of cloud coverage. The computing system may be configured to determine an updated trajectory for the satellite based on the current trajectory and the comparison. The computing system may be configured to generate one or more command instructions to control a motion of the satellite based on the updated trajectory.