A Novel Hypersonic Target Trajectory Estimation Method Based on Long Short-Term Memory and a Multi-Head Attention...

Area of Science:

• Aerospace Engineering
• Artificial Intelligence
• Information Theory

Background:

• Hypersonic targets exhibit complex maneuvering characteristics in adjacent space, posing challenges for traditional trajectory estimation.
• Accurate prediction of these trajectories is crucial for defense and surveillance applications.

Purpose of the Study:

• To develop an advanced trajectory estimation method for hypersonic targets.
• To improve prediction accuracy and computational efficiency by integrating Long Short-Term Memory (LSTM) networks with an attention mechanism.
• To optimize the model using information-theoretic principles.

Main Methods:

• Constructed a target dynamics model to define motion behavior parameters.
• Designed an LSTM model incorporating an attention mechanism to focus on critical historical trajectory data.
• Applied feature selection and data preprocessing to reduce redundancy and enhance information validity during model training.

Main Results:

• Achieved accurate prediction of hypersonic target trajectories, even with limited computational resources.
• Demonstrated improved prediction accuracy and robustness in complex dynamic environments.
• Validated the effectiveness of the attention mechanism in improving information utilization.

Conclusions:

• The proposed LSTM trajectory estimation method with an attention mechanism offers a robust solution for predicting hypersonic target maneuvers.
• Integrating information-theoretic principles enhances model optimization and performance.
• The method shows significant potential for real-world applications in aerospace surveillance.