Bibliographic Details
| Title: |
High-precision reconstruction method based on MTS-GAN for electromagnetic environment data in SAGIoT |
| Authors: |
Lantu Guo, Yuchao Liu, Yuqian Li, Kai Yang |
| Source: |
EURASIP Journal on Advances in Signal Processing, Vol 2023, Iss 1, Pp 1-16 (2023) |
| Publisher Information: |
SpringerOpen, 2023. |
| Publication Year: |
2023 |
| Collection: |
LCC:Telecommunication
LCC:Electronics |
| Subject Terms: |
Electromagnetic environment data, High-precision reconstruction, Generative adversarial network, Multi-component time series, Telecommunication, TK5101-6720, Electronics, TK7800-8360 |
| More Details: |
Abstract Equipment failures and communication interruptions of satellites, aircraft and ground devices lead to data loss in Space-Air-Ground Integrated Internet of Things (SAGIoT). The incomplete data affect the accuracy of data modeling, decision-making and spectrum prediction. Reconstructing the incomplete data of electromagnetic environment is a significant task in the SAGIoT. Most spectral data completion algorithms have the problem of limited accuracy and slow iterative optimization. In light of these challenges, a novel high-precision reconstruction method for electromagnetic environment data based on multi-component time series generation adversarial network (MTS-GAN) is proposed in this paper. MTS-GAN transforms the reconstruction method of electromagnetic environment data into the data generation problem of multiple time series. It extracts the time–frequency joint features and the overall distribution of electromagnetic environment data. To improve the reconstruction precision, MTS-GAN simulates the time irregularity of incomplete time series by applying a gate recursive element to adapt to the attenuation effect of discontinuous time series observations. Experimental results show that the proposed MTS-GAN provides high completion accuracy and achieves better results than competitive data completion algorithms. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
1687-6180 |
| Relation: |
https://doaj.org/toc/1687-6180 |
| DOI: |
10.1186/s13634-023-01085-0 |
| Access URL: |
https://doaj.org/article/d9b37552108147e3a031256ca1f6cc78 |
| Accession Number: |
edsdoj.9b37552108147e3a031256ca1f6cc78 |
| Database: |
Directory of Open Access Journals |
