# A Clutter Suppression Method Based on LSTM Network for Ground Penetrating Radar
## 5 Conclusions  
This paper presented a wavelet prediction method based on the LSTM network. This method takes advantage of the strong correlation of the GPR signals to construct quasi-periodic input signal by splicing the data head and tail. The network is trained to learn the commonality of the input data, while ignoring the random interference and predicting a smooth wavelet.  
Several groups of experiments (corresponding to different excitation source waveform, target location, shape, material, background geometry, etc.) show the generalization ability of the network for different detection scenarios. As long as the antenna system parameters  
Figure 14: The detection data of mines in red clay and clay. (The channel numbers of A–F are 461, 659, 701, 200, 449, and 483, respectively).  
Figure 15: Wavelet prediction of the detection data in Figure 14.  
remain unchanged, the network trained with the simulation data of one arbitrary scene can be used to predict the wavelet of many different scenes. In order to expand the applicability of the network to different media, the network is optimized by expanding the range of dielectric parameters of the simulation model. For the application of actual detection data, we directly use a small amount of measured data for network training, and then use the trained network for detection data in other different scenes. Compared with the wavelet extracted by SVD, the predicted wavelet has obvious advantages in the integrity and adaptability of the detection area, and the method can be used in large-scale underground exploration projects such as pipeline detection under urban roads, defect detection inside a tunnel, and so on.  
This method has many advantages, as follows:  
1. This method does not rely on prior knowledge and can effectively extract the wavelets of different scenes.
2. There is no need for artificial marking during network training. The A-Scan echo can be directly used as training data, and the input data is easy to obtain.
3. The trained network has good generalization ability and can solve many practical problems, such as heavy marking of a large-scale detection area, the inability to label special detection environments, and poor processing results caused by inaccurate calibration.  
The proposed neural network method has very strong generalization ability for wavelet prediction of the same antenna system. However, if the antenna system is changed, the network must be retrained. This issue needs to be further studied in the future. The probable approach is to conduct a large number of network training experiments by controlling a single variable or multiple variables, such as antenna system parameters or the environment parameters. The large number of detection data will make the network learn its internal connections. Moreover, we will also try to use the Transform network structure to predict.  
Conceptualization and Supervision, H.Y.; Writing--Original draft, J.H.; Writing--Review and editing and Software, J.G.; and Methodology and Validation, B.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by in part by the National Key RD Program of China 2021 YFB3900100 and in part by the National Natural Science Foundation of China (Grant No. 61871424).  
Not applicable.  
The authors declare no conflict of interest.