To further enhance the network's performance for military applications beyond flight trajectory prediction, the following techniques and domain-specific knowledge can be integrated:
Adversarial Training: Incorporating adversarial training methods can help the network anticipate and counter potential adversarial attacks or deceptive maneuvers by enemy fighters, enhancing its robustness in combat scenarios.
Domain-Specific Constraints: Introducing domain-specific constraints such as fuel limitations, weapon capabilities, or terrain constraints into the network's architecture can provide more contextually relevant predictions tailored to military applications.
Multi-Modal Fusion: Integrating multi-modal data sources such as radar information, communication signals, or satellite imagery into the network can enrich the input features, enabling more comprehensive and accurate predictions in complex military environments.
Explainable AI Techniques: Implementing explainable AI techniques can provide insights into the network's decision-making process, allowing military operators to understand and trust the predictions, crucial for real-world deployment in high-stakes scenarios.