Hybrid Deep Learning Network-Based Approach for Air Combat Maneuver Decision- Making

Sheeba D.*, Lingeshwari G.**, Anorelin A.***
*-*** Department of Computer Science and Engineering, DMI Engineering College, Aaralvaimozhi, Kanyakumari, Tamil Nadu, India.
** Department of Artificial Intelligence and Data Science, DMI Engineering College, Aaralvaimozhi, Kanyakumari, Tamil Nadu, India.
Periodicity:July - September'2025

Abstract

This paper introduces a hybrid deep learning model for maneuver decision-making in air combat scenarios. The model combines a stacked sparse autoencoder (SSAE) for dimensionality reduction of high-dimensional, dynamic time-series combat data with a long short-term memory (LSTM) network to model the quantitative relationship between the reduced data and maneuver control variables. Key features of the model include leveraging time-series data as the foundation for decision-making, which aligns closely with real-world processes, and using SSAE to enhance prediction accuracy by reducing data dimensionality. Additionally, the model outputs maneuver control variables, enabling flexible and effective control of maneuvers. Experimental results demonstrate that the proposed approach significantly improves prediction accuracy and convergence speed, making it a robust solution for autonomous air combat decision-making systems.

Keywords

Air Combat, Dimension Reduction, Deep Learning, LSTM, Time-Series Data.

How to Cite this Article?

Sheeba, D., Lingeshwari, G., and Anorelin, A. (2025). Hybrid Deep Learning Network-Based Approach for Air Combat Maneuver Decision- Making. i-manager’s Journal on Computer Science, 13(2), 83-93.

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