Spacecraft Imaging Scheduling Using Ground Nowcasts
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Solution Overview
Problem
Existing Earth observation spacecraft face inefficiencies due to atmospheric obstructions like clouds, which limit the ability to capture usable data, leading to wasted resources and missed imaging opportunities.
Innovation Solution
Implementing a ground-based nowcast system that predicts environmental conditions using machine learning algorithms to guide autonomous scheduling and decision-making on spacecraft, optimizing image acquisitions, communication, and operational actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If blind imaging operations are deployed during Earth observation missions, then the spacecraft can continuously capture images, but the ability to realize full value from Earth observation spacecraft assets is constrained by atmospheric obstructions
Solution Approach 1:
The system performs preliminary actions by generating nowcasts of atmospheric conditions (cloud cover, haze, ash, fog, smoke) before the spacecraft executes imaging operations. Ground-based prediction models analyze environmental data and provide advance warnings of obstructive conditions, allowing the spacecraft to pre-adjust its imaging schedule and avoid wasting resources on doomed acquisitions.
Solution Approach 2:
The system implements feedback by continuously monitoring atmospheric conditions through ground-based sensors and satellite data, then using this information to dynamically adjust spacecraft imaging operations. The nowcast system provides real-time feedback on cloud cover and atmospheric opacity, enabling closed-loop control where imaging decisions are continuously optimized based on current environmental conditions.
2Device complexity
If the spacecraft operates without cognizance of successful ground view capture likelihood, then the scheduling system remains simple, but resources are wasted on unsuccessful imaging attempts
Solution Approach 1:
The system introduces an intermediary ground-based nowcast system that acts as a mediator between atmospheric conditions and spacecraft operations. Instead of making the spacecraft complex with onboard prediction capabilities, the ground-based prediction models serve as an intermediary that processes environmental data and provides guidance to the spacecraft scheduling system, keeping onboard complexity low while enabling informed decision-making.
Solution Approach 2:
The ground-based prediction models perform preliminary assessments of imaging success likelihood before the spacecraft executes operations. By calculating cloud cover probability and atmospheric opacity in advance, the system provides提前 guidance on which imaging attempts are likely to succeed, allowing the spacecraft to prioritize successful acquisitions and avoid wasting resources on obviously failed attempts.
3Area of stationary object
If traditional forecasting systems are used, then the prediction coverage area is large, but the spatial resolution and temporal accuracy are insufficient for guiding spacecraft operations
Solution Approach 1:
The system applies segmentation by dividing the prediction domain into discrete grid cells or regions of interest, each with its own nowcast parameters. Instead of providing a single averaged forecast for a large area, the ground-based prediction models generate high-resolution spatial grids that can be queried at specific locations and times, enabling precise targeting of spacecraft imaging operations while maintaining broad coverage capability.
Solution Approach 2:
The system transitions from traditional two-dimensional spatial forecasting to a three-dimensional space-time forecast volume. By incorporating high temporal resolution (sub-hourly updates) alongside spatial detail, the nowcast system creates a四维 prediction structure that captures both the spatial distribution and temporal evolution of atmospheric conditions, allowing spacecraft to optimize imaging timing and location simultaneously.
Data Source
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AI summary
Systems and methods for autonomous space-mission coordination integrate ground-based prediction with on-orbit execution. A processing system in a ground station may ingest multi-source environmental data, produce near-term nowcasts through an prediction model, rank pending spacecraft tasks against current resource telemetry, select a high-value task subset, convert the subset into time-tagged command packets, and transmit the packets through a communications link. A processing system aboard each spacecraft may receive the packets, merge them into a persistent schedule, and at each time tag slews attitude, activate an imaging or radar sensor with specified parameters, capture data, and store the data in non-volatile memory. The spacecraft may generate quality metrics for the captured data and return the metrics to the ground station.