# A Clutter Suppression Method Based on LSTM Network for Ground Penetrating Radar
## 1 Introduction  
Ground penetrating radar (GPR) is an electromagnetic (EM) imaging device that uses the reflection and scattering characteristics of EM waves in discontinuous media to achieve non-destructive detection [1]. It has been used in many engineering detection fields such as ground ice detection [2], underground pipeline detection, and criminal investigations [3]. Recently, space exploration probes, such as Lunar and Mars exploration, are also equipped with GPR equipment for geological stratification studies. However, due to the complex underground environment, the radar echo always contains clutter such as multiple waves, antenna coupled waves, and reflected waves from other non-detection targets, which seriously obscure the signal of the buried targets and bring great difficulty to the interpretation of GPR data. Many scholars have conducted in-depth research into methods of clutter suppression. Some research designed an appropriate antenna system to enhance the echo of a buried object and reduce the background noise. For example, [PERSON] et al. [4] developed a hybrid dual-polarization GPR system with one circularly polarized transmit antenna and two linearly polarized receive antennas to improve detection and estimation of slender tubular targets. Others are based on signal processing algorithms. For example, the reference wave method averages a few A-scans echoes without target information to find the wavelets of the detection scene, and then subtracts the mean from the original data [5]. The background matrix subtraction (BMS) method is an improvement of the average cancellationmethod [6; 7]. The background noise matrix is obtained through a series of sliding windows, sample exclusion, weighting and iteration, then it is removed from the original data to suppress the clutter. However, these methods rely heavily on prior information about the detection scene, and it is often difficult to know in advance which echoes do not contain the target signal in actual detection. Limited by wavelet extraction, the multi-scale filtering method was developed. [PERSON] [8] proposed a noise attenuation method based on curvelet transform. He extracted the background noise according to the distribution characteristics in the curvelet domain. [PERSON] [9] designed a multiscale directional bilateral filter (MDBF), which can flexibly extract the directional details corresponding to different geometric structures from the original data. Then, the inverse MDBF is applied to reconstruct the image of targets. In addition, there are some subspace projection techniques, such as morphological component analysis (MCA) [10], Singular Value Decomposition (SVD) [11], and Principal Component Analysis (PCA) [12]. These methods project the detection signals into different subspaces and remove the noise subspace to reconstruct the clutter-free signal. However, when the complex underground environment leads to serious signal distortion or strong clutter, the wavelets will be spread in multiple principal components, which causes difficulty in determining the truncation of eigenvalues. In addition, a sparse blind deconvolution (SBD) method has recently been proposed for the GPR data process [13]. However, it would likely be more effective to estimate the optimum wavelet for each individual transmitter location separately, rather than for the whole data set. Therefore, the existing methods can only be used in specific detection environments, not suitable for actual engineering wavelet extraction. This paper designs an LSTM neural network to directly predict the wavelet from the GPR A-scan echoes. Firstly, the possibility of the neural network to extract the wavelet is analyzed based on the characteristics of GPR signals and the neural network. Then, some A-scan data generated with a 3D-FDTD simulator are used to train a two-layer LSTM network. Some simulated and measured GPR A-scan echoes are used to test the performance of the trained network model.