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These studies suggest that spacecraft trajectory prediction can be improved using various methods such as grey dynamic filters, multi-objective and stochastic optimization, asymptotic expansion methods, artificial neural networks, data aggregation, 3-D algorithms, combined algorithms, simulation-based methods, vision chips, and parallel genetic algorithms.
20 papers analyzed
The calculation and prediction of spacecraft trajectories are critical for successful space missions. This involves determining the path a spacecraft will follow under the influence of various forces, such as gravitational pulls from celestial bodies and propulsion systems. Accurate trajectory prediction ensures the spacecraft reaches its intended destination efficiently and safely.
Grey Dynamic Filter (GDF) Method:
Optimization Techniques:
Asymptotic Expansion Method:
Artificial Neural Networks (ANNs):
Induced Ordered Information Aggregation Operator:
3-D Trajectory Prediction:
Global 4-D Trajectory Optimization:
Real-time Trajectory Calculation Using Parallel Processing:
Parallel Genetic Algorithm (PGA):
The prediction and calculation of spacecraft trajectories involve various advanced methods, each with its strengths. Grey Dynamic Filter (GDF) and artificial neural networks (ANNs) offer high accuracy in real-time predictions. Optimization techniques, including multi-objective and stochastic methods, are crucial for handling multiple mission objectives and uncertainties. Methods like asymptotic expansion and parallel genetic algorithms provide efficient and robust solutions for specific trajectory problems. Combining data from multiple sources and using parallel processing further enhance prediction precision and computational efficiency.
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