GPU Parallel Orbit Propagation for Space Object Collision Prediction

The patent replaces the traditional CPU-based Monte Carlo algorithm with a GPU-based parallel computing system. The GPU's architecture with thousands of cores enables simultaneous execution of multiple orbit propagation calculations, dramatically reducing computational time while maintaining the statistical accuracy of collision probability predictions. This substitution of computing hardware fundamentally changes the time-cost tradeoff of Monte Carlo methods.

The patent divides the collision probability prediction task into multiple independent orbit propagation calculations that can be executed in parallel. Each GPU thread handles a specific sample trajectory, segmenting the overall computation into thousands of concurrent sub-tasks. This segmentation enables the system to process large numbers of Monte Carlo samples simultaneously, reducing total computational time while preserving prediction accuracy.

The patent leverages GPU parallel architecture to handle the computational complexity of large-scale Quasi-Monte Carlo simulations. By distributing samples across thousands of GPU cores, the system can process extensive sample sets without proportionally increasing sequential computational complexity. The GPU's parallel processing capability transforms the complexity burden from sequential to concurrent execution.

The patent transitions from sequential processing (one-dimensional time progression) to parallel processing (multi-dimensional concurrent execution). By utilizing the GPU's multi-core architecture, the system adds a dimensional aspect of parallelism, allowing multiple samples to be processed simultaneously rather than sequentially, thereby managing computational complexity more efficiently.