  The utilization of Very Low Frequency (VLF) electromagnetic signals in
navigation systems is widespread. However, the non-stationary behavior of
lightning signals can affect VLF electromagnetic signal transmission.
Accurately classifying lightning signals is important for reducing interference
and noise in VLF, thereby improving the reliability and overall performance of
navigation systems. In recent years, the evolution of deep learning,
specifically Convolutional Neural Network (CNNs), has sparked a transformation
in lightning classification, surpassing traditional statistical methodologies.
Existing CNN models have limitations as they overlook the diverse attributes of
lightning signals across different scales and neglect the significance of
temporal sequencing in sequential signals. This study introduces an innovative
multi-scale residual transform (MRTransformer) that not only has the ability to
discern intricate fine-grained patterns while also weighing the significance of
different aspects within the input lightning signal sequence. This model
performs the attributes of the lightning signal across different scales and the
level of accuracy reached 90% in the classification. In future work, this model
has the potential applied to a comprehensive understanding of the localization
and waveform characteristics of lightning signals.
