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IH-TCGAN: Time-Series Conditional Generative Adversarial Network with Improved Hausdorff Distance for Synthesizing

Siyuan Wang¹, Gang Wang¹, Qiang Fu¹

¹Air Defense and Antimissile School, Air Force Engineering University, Xi'an 710051, China.

Entropy (Basel, Switzerland)

|May 27, 2023

Summary

This summary is machine-generated.

This study introduces IH-TCGAN, a novel deep learning method for air target intention recognition. It effectively generates realistic synthetic time-series data, overcoming limitations of low-volume and unbalanced datasets in military applications.

Keywords:

Hausdorff distance data augmentation generative adversarial network intention recognition multivariate time series

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Area of Science:

Artificial Intelligence
Machine Learning
Military Technology

Background:

Deep learning is crucial for air target intention recognition amidst evolving military technology and increasing battlefield data.
Existing deep learning methods struggle with low data volume and imbalanced datasets due to limited real-world scenarios.
Addressing data scarcity is vital for accurate intention recognition in complex military environments.

Purpose of the Study:

To propose a novel deep learning method, IH-TCGAN, to generate high-quality synthetic time-series data for air target intention recognition.
To overcome challenges of low data volume and dataset imbalance in training deep learning models for military applications.
To enhance the realism and temporal accuracy of generated synthetic data for improved model performance.

Main Methods:

Developed a time-series conditional generative adversarial network with improved Hausdorff distance (IH-TCGAN).
Employed a transverter to map real and synthetic data onto the same manifold, ensuring consistent intrinsic dimensions.
Integrated a restorer and classifier for generating high-quality, multi-class temporal data and utilized an improved Hausdorff distance for temporal accuracy.

Main Results:

IH-TCGAN successfully generates synthetic time-series data that closely resembles real-world data.
The method demonstrates significant advantages in generating time-series data compared to existing approaches.
Experimental evaluations using multiple metrics and visualization techniques confirm the effectiveness of IH-TCGAN.

Conclusions:

IH-TCGAN effectively addresses data limitations in deep learning for air target intention recognition.
The proposed method enhances the quality and realism of synthetic temporal data, improving model robustness.
IH-TCGAN offers a promising solution for data-driven military AI applications requiring accurate intention recognition.