Satellite Orbit Propagation for Real-Time Constellation Simulation
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Solution Overview
Problem
Traditional satellite simulation methods face challenges in accuracy, computational efficiency, scalability, and interoperability, particularly in complex mission scenarios, and lack advanced visualization and user-friendly interfaces, making them difficult to use and scale for large satellite constellations.
Innovation Solution
A system environment utilizing parallel computing and vectorization techniques for rapid calculations, integrated with machine learning for satellite attitude control, and including features for orbit propagation, link budget, light pollution, power analysis, and orientation control, with user-friendly interfaces and data storage for iterative simulations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional numerical integration techniques are used for orbit propagation, then the method is simple to implement, but accuracy deteriorates in the presence of environmental perturbations
Solution Approach 1:
The simulation system is segmented into modular components including orbit propagation engine, perturbation models, attitude control systems, and visualization modules. This allows accurate physics-based models to be integrated without requiring complete system redesign, resolving the contradiction between accuracy and complexity by enabling selective incorporation of sophisticated models only where needed.
Solution Approach 2:
The patent introduces intermediate computational layers including machine learning models that act as mediators between simple orbital mechanics and complex environmental perturbations. These intermediary models pre-process perturbation effects and feed corrected trajectories to the orbit propagation engine, improving accuracy without directly increasing the complexity of the core propagation algorithm.
2Productivity
If manual calculations are used for satellite simulation, then the approach is simple, but productivity deteriorates when addressing complex mission scenarios
Solution Approach 1:
The system replaces manual mechanical calculation processes with automated computational engines. The orbit propagation engine automatically performs numerical integration, perturbation analysis, and trajectory optimization without manual intervention. This substitution dramatically increases productivity for complex scenarios while the modular architecture prevents exponential complexity growth through systematic automation.
Solution Approach 2:
The simulation system allows dynamic adjustment of computational parameters including time step size, perturbation model fidelity, and numerical integration order. Users can optimize these parameters based on mission requirements, achieving high productivity for routine scenarios with simplified models while automatically switching to high-fidelity models only when accuracy is critical, thus balancing productivity and complexity.
3Adaptability or versatility
If conventional simulation software is used, then basic functionality is provided, but adaptability deteriorates for different operating conditions
Solution Approach 1:
The simulation system implements universal modules that can handle multiple satellite types, orbit regimes, and mission scenarios through a single unified interface. The orbit propagation engine supports both single-satellite and constellation simulations with the same core algorithms, while perturbation models automatically adapt to different environmental conditions. This universality provides high adaptability without requiring users to learn multiple specialized tools.
Solution Approach 2:
The system dynamically adjusts simulation parameters, model fidelity, and computational resources based on the specific scenario being analyzed. The interface automatically configures appropriate perturbation models and integration steps based on detected mission characteristics, maintaining ease of operation while adapting to diverse operating conditions through intelligent parameter selection rather than manual configuration.
4Productivity
If parallel processing is implemented for constellation simulation, then productivity improves, but device complexity increases
Solution Approach 1:
The simulation system segments constellation analysis into independent satellite trajectory calculations that can be processed in parallel. Each satellite's orbit propagation is computed separately using identical modular code, allowing straightforward parallelization across multiple CPU cores or distributed computing nodes. This segmentation achieves linear scalability for constellation simulations while keeping individual processor units relatively simple.
Data Source
AI summary
The disclosed technology is generally directed to a method for simulating orbit propagation of a satellite system including a constellation of satellites. In one example of the technology, the method may include receiving an input indicating whether to simulate the orbit propagation for a single satellite or a plurality of satellites of the constellation, and a set of orbital parameters associated with each satellite. Based on the input and the set of orbital parameters: simulating the orbit propagation of the single satellite or the plurality of satellites to iteratively determine a velocity and a position of the single satellite or the plurality of satellites. The method may include iteratively storing the velocity and the position in a buffer and dynamically rendering a user interface to display a visualization representing the orbit propagation of one of the single satellite and the plurality of satellites.