## 3 The Application of Conventional ML Algorithms
### Dictionary Learning (DL)
Moreover, in 2019, [PERSON] et al. proposed a GPR diffraction extraction model combining PWD method and sparse coding algorithm based on online dictionary learning to solve the problem that geological information is difficult to extract due to the shielding effect of strong reflections from underground layers [43]. Through the PWD method, a local-plane wave mathematical model is used to predict linear events, thereby removing GPR strong reflection components. The GPR diffraction with amplitude change or phase reversal and noise are filtered into the residual data, and the weak diffraction is further recovered from the residual data by the dictionary learning method, and the weak diffraction component is extracted. Compared with traditional GPR migration profiles, the algorithm provided by this study can provide more detailed information on GPR signals such as small-scale holes and edges. A 2021 study applied K-SVD dictionary learning to GPR data denoising, used the orthogonal matching pursuit (OMP) algorithm to sparsely decompose different radar signals, and trained an overcomplete dictionary with sample features. The dictionary obtained through training performs better than the fixed discrete cosine transform (DCT). The K-SVD dictionary can effectively distinguish valid data from noise in GPR data. The K-SVD dictionary learning algorithm adaptively learns the noise data and considers the intra-block information and the global observation to complete the denoising [44]. In 2020, [PERSON], [PERSON] et al. proposed a method based on robust orthogonal subspace learning (ROSL) for clutter removal. The original image is decomposed into two parts: clutter and target by ROSL. It performs better than robust principal component analysis (RPCA), go decomposition (GoDec), and other algorithms [45]. In 2022, [PERSON] and others proposed the multilevel projective dictionary learning with low-rank prior (MPDL-LR) model [46], which uses a multilevel projective dictionary method to estimate radar trace signals. MPDL-LR achieves an average classification accuracy of 94.5% when using the \(\varepsilon\)-dragging technique, which is higher than 93.8% when using a linear classifier on the same test set. If the explosive devices are classified into high-metal and low-metal in advance, the accuracy rate of MPDL-LR predicting improvised explosive devices will be further increased to 98%. At the end of 2022, a study conducted by [PERSON] et al. based on the experiments conducted by [PERSON] used fast iterative shrinkage-thresholding algorithm (FISTA) [47] instead of OMP, which further improved the denoising effect and algorithm convergence speed. The processing of echo data shows that, under different noise levels, the FISTA algorithm is better than the OMP algorithm in denoising the radar signal and, with the increase in the noise, the superiority is getting bigger and bigger. In a 2023 study on the reconstruction of 2D images of buried objects from ground-penetrating radar reflection hyperbolas, [PERSON] et al. found that DL methods outperform CNN methods in terms of noise measurement because sparse representations are more robust to image noise, while CNN methods work better when dealing with fewer trajectories [48]. Because the essence of dictionary learning is to find the similarity between the dictionary atoms and the input signal and when the number of scans becomes smaller and smaller, this similarity will become more and more difficult to find. In 2023, [PERSON] et al. analyzed the largest singular value, divided the atoms in the adaptive dictionary obtained by deep learning into target atoms and clutter atoms, and reconstructed the target component and clutter component in the GBR B-scan signal, respectively [49], thereby reducing the clutter interference caused by the target reflection and improving the detection accuracy. In terms of clutter suppression, the pro-posed algorithm is superior to MCA, OSTD, RNMF, and SVD in almost all scenarios and only slightly inferior to RAE and RPCA in metal pipeline scenarios.