2021 IEEE 4th International Conference on Electronic Information and Communication Technology (ICEICT) | 2021
Recognition of Underground Target Material Based on Average Phase Difference and Radial Basis Function Neural Network
Abstract
Recognition of the material of underground targets is always an important part of GPR (Ground Penetrating Radar) applications. According to target return time, truncating the data to remove the direct waves, and then select the typical data of the different materials target echoes. By comparing the phase of different targets with that of no target, the average phase difference is obtained. And the average phase difference is used as the input of the RBF (Radial Basis Function) neural network to realize the classification and recognition of underground target materials. Simulation result shows that this method can realize the identification and classification of common materials, such as metals, water and hole.