The suppression of clutter and the positioning of underground pipelines are crucial steps in the processing of ground-penetrating radar (GPR) data. It is challenging to acquire clutter-free measured data during the radar detection process. As a result, the existing deep learning (DL) methods are primarily trained using simulation data, which limits their applicability to real-world scenarios. To address these challenges, this letter proposes an improved underground clutter suppression and pipeline positioning network. In the first stage, the model is trained using both measured data and simulation clutter-free data to enhance its ability to suppress clutter in measured data. Furthermore, in the second stage, the network is modified to accept paired, labeled simulation data, which enables more accurate pipeline positioning than the original unpaired network. Real-world data evidence demonstrates that the proposed network’s clutter suppression achieves a mean squared error (mse) of 0.006 and a peak signal-to-noise ratio (PSNR) of 34.73 dB. Additionally, the Euclidean distance error of the target clustering center coordinates is 0.82px. Compared to other methods, the performance of the proposed approach has been significantly enhanced.

As a nondestructive and nonintrusive geophysical electromagnetic technology, ground-penetrating radar (GPR) has been widely applied to subsurface target detection, such as landmine detection, pipeline detection, and underground cavity detection. The target response received by the GPR system is generally contaminated by clutter, which greatly affects the detection performance of the buried targets. In this letter, a novel clutter removal method combining migration and dictionary learning (DL) is presented. First, the proposed method applies the frequency–wavenumber (F–K) migration to the received GPR B-scan data. Then, since the focused target response and the clutter in the migrated GPR B-scan data present different morphological components, DL can be applied to the migrated GPR B-scan data to separate the focused target response (point-shaped structure) from the clutter (horizontal strip-shaped structure). Both numerical simulated data and experimental data collected by a real GPR system are used to evaluate the performance of the proposed clutter removal method. The experimental results demonstrate the effectiveness of the proposed clutter removal method under irregular clutter conditions, which improves the detection ability of the buried targets.

Clutter removal in ground-penetrating radar (GPR) B-scan data has been widely studied in recent years. In this letter, we propose a novel data-driven clutter suppression method in GPR data based on conditional generative adversarial nets (cGANs). The proposed method learns a function that maps the cluttered data to the clutter-free data from the training set. The training set consists of pairs of cluttered data and corresponding clutter-free data. Different from the traditional method that only uses the simulation training set, we simulate the clutter-free data and add the real collected non-target data to the simulated clutter-free data as cluttered data, so that the trained network can generalize well to the real GPR data. The proposed method is compared with the subspace method, sparse representation-based method, and low-rank and sparse matrix decomposition (LRSD) methods on both simulation data and real collected data. The results show that the proposed method has higher performance in terms of computational complexity, clutter suppression results, and applicability than those state-of-the-art methods.

Ground-penetrating radar (GPR) is a well-known geophysical electromagnetic method used to detect the underground facilities such as landmines, pipelines, and cavities. In general, the clutter presented in GPR B-scan image obscures the underground objects, thus damaging the performance of the underground object detection algorithm. In this letter, we proposed a new clutter suppression method based on robust autoencoder (RAE). The proposed algorithm decomposes a GPR B-scan image into its low-rank and sparse components. The low-rank component catches the clutter, whereas the sparse component captures the underground object responses. The commonly used clutter removal algorithms, mean subtraction (MS), singular value decomposition (SVD), robust principal component analysis (RPCA), and morphological component analysis (MCA), are compared with the proposed algorithm on both the numerical simulated data and real GPR data. The visual and quantitative results demonstrate the effectiveness of the proposed RAE-based algorithm over the widely used state-of-the-art clutter removal algorithms.

Ultrawideband (UWB) multiple-input–multiple-output (MIMO) radar is widely used for through-wall imaging (TWI) due to its excellent penetrability and large aperture. Multichannels in the MIMO radar system are usually time-division multiplexing based on microwave switches to reduce the complexity of the system in engineering. The switching process of the channel will bring time delay, which cannot be ignored in the TWI of the moving target. The switching time delay will cause the defocus and position shift of the TWI of the moving target. This letter proposes a motion compensation method based on multiframe data fusion (MFDF) used for correcting the echo of the through-wall moving target. A geometric model is established in the proposed method through the echo of the current frame and the next frame, and the compensated signal is obtained through the geometric solution. The proposed method is compared with before compensation and the traditional single-channel motion compensation algorithm (SCMCA) through simulation and experimental data verification. The visual images and quantitative results show that the proposed motion compensation method can obtain a good focus image of the through-wall moving target and reduce the positioning error.

Low imaging spatial resolution hinders through-the-wall radar imaging (TWRI) from reconstructing complete human postures. This letter mainly discusses a convolutional neural network (CNN)-based human posture reconstruction method for TWRI. The training process follows a supervision-prediction learning pipeline inspired by the cross-modal learning technique. Specifically, optical images and TWRI signals are collected simultaneously using a self-develop radar containing an optical camera. Then, the optical images are processed with a computer-vision-based supervision network to generate ground-truth human skeletons. Next, the same type of skeleton is predicted from corresponding TWRI signals using a prediction network. After training, the model shows complete predictions in wall-occlusive scenarios solely using TWRI signals. Experiments show comparable quantitative results with the state-of-the-art vision-based methods in nonwall-occlusive scenarios and accurate qualitative results with wall occlusion.

Ground penetrating radar (GPR) is a widely used nondestructive testing tool. This letter presents a modified model for quasi-monostatic configured GPRs. In this model, the antenna effects are represented by a set of linear transfer functions and the GPR responses of the layered media are modelled by 3-D Green's functions. In order to obtain sufficient accuracy, the proposed model includes a special effect such that a part of the electromagnetic wave, propagating directly from the transmitting antenna to the receiving antenna, is reflected outside as a new transmitting signal. The experimental results of the full-waveform inversion have demonstrated that considering this special effect in the quasi-monostatic model is effective for improving accuracy. Although both monostatic and quasi-monostatic GPRs can be accurately modelled for full-waveform inversion, the results of the experiment performing inversion of a three-layer media with low permittivity contrast, proved that the latter can achieve better performance due to its intrinsic higher signal-to-noise ratio (SNR).

When sounding pavement layers using ground penetrating radar (GPR), antenna effects including dispersion and multiple reflections usually degrade the vertical resolution. In far-field conditions, these effects can be analytically filtered out by a linear method. However, for near-field operation, the antenna model tends to be nonlinear, and thus, these unwanted effects cannot be analytically removed anymore. In this letter, a method based on the radial basis function (RBF) neural network is proposed to filter out antenna effects under near-field conditions. A well-developed GPR model is used to simulate the input training data, and the corresponding zero-offset Green's function is calculated as the desired output for each training data. The trained RBF network is applied to the simulated and measured data. The results show that the proposed method is effective in filtering out the antenna effects and increasing the vertical resolution of GPR.

The sounding depth is one of the most important parameters of Ground Penetrating Radar (GPR), and it is also the main limitation of GPR's applications. A pseudo random coded GPR for deep investigation is described in this paper. It could achieve a sounding depth more than 130 meters. A long Golay sequences and TGA (time-gain amplifier) technology are utilized to increase the SNR of faint echoes from deep layer underground, and enhance the sounding depth. The platform could be either a vehicle or a large airship. Field tests were carried out on Kubuqi desert located in Inner Mongolia, north-west of china, to verify the detection ability of this GPR system. The sounding results agree well with the results from the drilling and resistivity logging.

A new ultrawideband-digital-sampling technology is presented. This novel sampling technology is the first to employ both the successive-approximation-register technology and the equivalent-time-sampling technology to improve the bandwidth of the receiver. Accordingly, a new low-cost and compact ultrawideband digital receiver for pulse ground-penetrating radar is designed. The merit of this receiver is that it can convert radio-frequency signal to digital signal without any analog sampling gate or analog-to-digital converter. The equivalent sampling rate of the receiver is up to 33 GHz. The measurements show that this receiver has more than 3-GHz bandwidth and nearly 100% conversion efficiency.