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An air target intention data extension and recognition model based on deep learning.

Bo Cao¹, Qinghua Xing², Longyue Li³

¹Graduate School, Air Force Engineering University, Xi'an, 710051, China.

Scientific Reports

|April 22, 2025

Summary

This summary is machine-generated.

This study introduces a deep learning model (IDERDL) to improve air target intention recognition by addressing data scarcity and temporal feature extraction. The IDERDL model achieves 98.73% accuracy, significantly enhancing battlefield situational awareness.

Keywords:

Denoising diffusion probabilistic model Dilated causal convolution Graph attention network Intention recognition Situation cognition

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Area of Science:

Artificial Intelligence
Aerospace Engineering
Military Science

Background:

Air target intention recognition is crucial for battlefield situational awareness in modern air operations.
Existing methods face challenges due to data scarcity and insufficient temporal feature extraction.

Purpose of the Study:

To propose a novel deep learning model, IDERDL (Intention Data Extension and Recognition based on Deep Learning), to address data scarcity and improve temporal feature extraction for air target intention recognition.

Main Methods:

Developed an intention data generation model using a denoising diffusion model with improved knowledge distillation for accelerated sampling.
Constructed a temporal block with dilated causal convolution for enhanced temporal feature extraction.
Integrated a graph attention mechanism to analyze feature relationships, feeding into a softmax layer for classification.

Main Results:

The proposed IDERDL model achieved a high intention recognition accuracy of 98.73%.
Demonstrated superior performance compared to existing air target intention recognition methods.
Effectively addressed the challenges of data scarcity and temporal feature extraction in this domain.

Conclusions:

The IDERDL model offers a significant advancement in tactical intention recognition by uniquely considering data scarcity and temporality.
The findings are highly significant for improving air target intention recognition capabilities in military applications.