A radar data compression method based on autoencoder neural network and range encoding

Ground Penetrating Radar (GPR) data requires a significant amount of network bandwidth and storage space for transmission and storage due to the large number of channels and vast amount of data. In this paper, we propose an improved method for compressing GPR data. Firstly, we analyze and preprocess the features of the data to enhance its compression potential. Secondly, we introduce convolutional layers into the AutoEncoder to improve its generalization ability. We then use multiple-level compression to further compress the data based on the radar data's features. Finally, we introduce range encoding for secondary compression. Simulation experiments demonstrate that our proposed algorithm can effectively compress radar data while maintaining high compression ratios and speed.