Multi-Satellite Imaging Sequence Optimization for Faster Delivery
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Solution Overview
Problem
Current satellite imaging mission plans do not effectively utilize the agility of low-altitude earth observation satellites, leading to inefficient image delivery times due to limited imaging opportunities during each satellite pass over a target area.
Innovation Solution
A method and system for generating an optimal imaging sequence that considers the position, target profit, and maneuverability of multiple satellites to determine the most efficient imaging sequence, using a pre-defined optimization objective function to maximize the number of images captured while accounting for satellite posture changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a sequential imaging mission plan is established by considering priority order of targets, then the imaging mission can be executed in a structured manner, but the time from imaging request to image delivery is extended
Solution Approach 1:
The system pre-calculates and stores optimal imaging sequences for multiple satellites before actual imaging requests are made. By preparing imaging sequences in advance considering satellite orbits, maneuverability, and target priorities, the system eliminates the need for time-consuming real-time optimization when imaging requests arrive, thus reducing image delivery time while maintaining structured mission execution
Solution Approach 2:
The system dynamically adjusts imaging sequences by considering the agile maneuverability of low-altitude satellites. Instead of using fixed sequential planning, the system incorporates real-time satellite position, velocity, and maneuvering capabilities to optimize the imaging sequence, allowing faster response to imaging requests while maintaining operational structure
2Device complexity
If the number of imaging targets is limited during each satellite visit, then the imaging mission plan becomes manageable, but the imaging productivity is reduced
Solution Approach 1:
The system merges the capabilities of multiple low-altitude satellites into a coordinated imaging network. By combining resources from multiple satellites and optimizing their joint imaging sequences, the system can image many more targets during each orbital pass while keeping individual satellite mission plans manageable. The coordination algorithm distributes targets across multiple satellites efficiently
Solution Approach 2:
The system transitions from single-satellite sequential planning to multi-satellite parallel imaging by adding the dimension of multiple satellites to the imaging mission plan. This allows simultaneous imaging of multiple targets by different satellites, dramatically increasing productivity while maintaining manageable complexity through coordinated scheduling algorithms
3Device complexity
If agile satellite maneuverability is not considered in the imaging mission plan, then the planning algorithm is simpler, but the imaging sequence efficiency is reduced
Solution Approach 1:
The system incorporates satellite maneuverability parameters (angular velocity, maneuvering time, posture changes) into the imaging sequence optimization algorithm. By considering these dynamic parameters, the system can generate more efficient imaging sequences that utilize satellite agility to reposition between targets faster, improving productivity while the added complexity is managed through standardized parameter integration
Data Source
AI summary
Provided are a method, a system, and an apparatus for generating an optimal imaging sequence for a plurality of satellites. More particularly, provided are a method, a system, and an apparatus for generating an optimal imaging sequence for establishing an efficient imaging mission plan for a ground target imaging mission by using a plurality of earth observation satellites.