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A GRU-Based Method for Predicting Intention of Aerial Targets.

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This study introduces an advanced aerial target combat intention recognition method using bidirectional gated recurrent units (BiGRU) and an attention mechanism. The model accurately predicts enemy intentions one step ahead, enhancing real-time decision-making in air combat.

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Area of Science:

  • Aerospace Engineering
  • Artificial Intelligence
  • Computational Science

Background:

  • Traditional aerial target combat intention recognition methods are limited by relying on single-moment data, failing to capture the dynamic nature of tactical maneuvers.
  • The temporal and dynamic changes in a target's state necessitate more sophisticated approaches for accurate intention inference.

Purpose of the Study:

  • To develop a novel method for aerial target combat intention recognition that overcomes the limitations of traditional approaches.
  • To improve the accuracy and timeliness of intention recognition through deep learning and predictive modeling.

Main Methods:

  • A hierarchical approach was used to construct an air combat intention characteristic set, encoded into numeric time-series data.
  • A deep learning model based on bidirectional gated recurrent units (BiGRU) was employed for characteristic analysis.
  • An attention mechanism was integrated to adaptively assign weights to characteristics, enhancing recognition accuracy.
  • A characteristic prediction module was introduced to enable prediction of future intentions.

Main Results:

  • The proposed method achieved 89.7% accuracy in recognizing aerial target combat intentions.
  • The model demonstrated the capability to predict enemy intentions one sampling point ahead of time.
  • The integration of BiGRU and attention mechanisms significantly improved the deep learning of combat characteristics.

Conclusions:

  • The developed aerial target combat intention recognition method, incorporating BiGRU and attention mechanisms with predictive capabilities, offers a scientific and effective solution.
  • The model's ability to predict intentions in advance provides significant value for real-time decision-making assistance in air combat scenarios.