To further enhance the network's capability to handle complex and unpredictable fighter maneuvers in real-world air combat scenarios, several improvements can be considered:
Incorporating Reinforcement Learning: By integrating reinforcement learning techniques, the network can adapt and learn from the dynamic environment, making real-time adjustments to predict trajectories based on the evolving combat situations.
Utilizing Generative Adversarial Networks (GANs): GANs can be employed to generate diverse and realistic trajectories, enabling the network to anticipate a wider range of potential movements and maneuvers by enemy fighters.
Implementing Transfer Learning: Leveraging pre-trained models on a broader dataset of diverse air combat scenarios can help the network generalize better to new and unseen situations, improving its adaptability to different combat environments.
Enhancing Data Augmentation Techniques: By augmenting the training data with various perturbations and transformations, the network can learn to handle variations in fighter maneuvers more effectively, leading to improved prediction accuracy in challenging scenarios.