Ground Penetrating Radar (GPR), a prevalent urban road inspection technique, encounters operational limitations due to the shallow penetration of high-frequency antennas and the low resolution of low-frequency counterparts. To surmount these obstacles while catering to the requisites of both high data resolution and substantial detection depth, the present study introduces a novel data fusion methodology for multi-frequency GPR, leveraging the two-dimensional wavelet transform. Experimental validation was carried out using 300M and 400M radar frequencies, with metrics such as the average gradient, information entropy, spatial frequency and Laplacian operator gradient serving as the benchmarks for assessment. The empirical findings affirm the efficacy of the proposed approach in amalgamating the signal attributes of multi-frequency antennas, thereby markedly enhancing both the penetration depth and precision of GPR data.
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.
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