Data-Driven Intelligent Guidance Methods
Built neural-network-based fast computation for trajectory and missile reachability, achieving average reachability error below 0.5%.
Neural NetworksParallel Data ProcessingGuidance Intelligence
Context
Conventional reachability computation is costly and often too slow for high-frequency decision loops.
Implementation
- Established a parallel data generation pipeline for efficient dataset construction.
- Performed data cleaning and feature transformation for stable model inputs.
- Trained multiple neural architectures for trajectory and reachability estimation.
Outcome
- Significantly improved reachability computation speed.
- Achieved average reachability error below 0.5%, meeting engineering-level accuracy targets.