A new Wavelet Prediction method for GPR clutter elimination Based on LSTM network

Ground penetrating radar (GPR) wavelet greatly affects the inversion results of underground structures. This paper presents a new wavelet extraction method based on the LSTM neural network. The simulation data with FDTD method and the measured data collected by the self-built unmanned aerial vehicle ground penetrating radar (UAV-GPR) are used for test, and the predicted wavelets are almost all offset by the wavelet components of the original data. The results show that the LSTM neural network can effectively predict the wavelets and their tailing oscillations for different detection scenes.