High-precision Reconstruction Method Based on MTS-GAN for Electromagnetic Environment Data in SAGIoT

Abstract Equipment failures and communication interruptions of satellites, aircraft and ground devices lead to data loss in the Sky-Ground Integrated Internet of Things (SAGIoT). The incomplete data affects 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 spectrum data completion algorithms, such as interpolation completion and tensor-based completion, are burdened by limited accuracy and sluggish iterative optimization processes. 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.