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

Content Provider	Europe PMC
Author	Wang, Siyuan Wang, Gang Fu, Qiang Song, Yafei Liu, Jiayi
Editor	Anbarjafari, Gholamreza
Copyright Year	2023
Abstract	As military technology continues to evolve and the amount of situational information available on the battlefield continues to increase, data-driven deep learning methods are becoming the primary method for air target intention recognition. Deep learning is based on a large amount of high quality data; however, in the field of intention recognition, it often faces key problems such as low data volume and unbalanced datasets due to insufficient real-world scenarios. To address these problems, we propose a new method called time-series conditional generative adversarial network with improved Hausdorff distance (IH-TCGAN). The innovation of the method is mainly reflected in three aspects: (1) Use of a transverter to map real and synthetic data into the same manifold so that they have the same intrinsic dimension; (2) Addition of a restorer and a classifier in the network structure to ensure that the model can generate high-quality multiclass temporal data; (3) An improved Hausdorff distance is proposed that can measure the time order differences between multivariate time-series data and make the generated results more reasonable. We conduct experiments using two time-series datasets, evaluate the results using various performance metrics, and visualize the results using visualization techniques. The experimental results show that IH-TCGAN is able to generate synthetic data similar to the real data and has significant advantages in the generation of time series data.
Journal	Entropy (Basel, Switzerland)
Volume Number	25
PubMed Central reference number	PMC10217029
Issue Number	5
PubMed reference number	37238537
e-ISSN	10994300
DOI	10.3390/e25050781
Language	English
Publisher	MDPI
Publisher Date	2023-05-11
Access Restriction	Open
Rights License	Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). © 2023 by the authors.
Subject Keyword	intention recognition multivariate time series data augmentation generative adversarial network Hausdorff distance
Content Type	Text
Resource Type	Article
Subject	Physics and Astronomy Information Systems Electrical and Electronic Engineering Mathematical Physics