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

Zelong Hu; Feng Yang; Xu Qiao; Fanruo Li

doi:10.1117/12.2689784

10 August 2023 A radar data compression method based on autoencoder neural network and range encoding

Zelong Hu, Feng Yang, Xu Qiao, Fanruo Li

Proceedings Volume 12748, 5th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2023); 127480V (2023) https://doi.org/10.1117/12.2689784
Event: 5th International Conference on Information Science, Electrical and Automation Engineering (ISEAE 2023), 2023, Wuhan, China

Abstract

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.

Citation Download Citation

Zelong Hu, Feng Yang, Xu Qiao, and Fanruo Li "A radar data compression method based on autoencoder neural network and range encoding", Proc. SPIE 12748, 5th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2023), 127480V (10 August 2023); https://doi.org/10.1117/12.2689784

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