This article proposes a three-dimensional (3D) human pose estimation method using through-wall radar (TWR) systems, which extends and supplements new applications in the era of the Internet of Things (IoT). TWR system can penetrate non-metallic obstacles and perceive wall-occlusive human targets, but the physical characteristics of radio frequency (RF) signals, such as poor imaging resolution and specularity effect, make the pose estimation process highly ill-posed. In this work, we propose a mutual information maximizing deeply-supervised network (MIMDSN), which aims to extract accurate and robust 3D human skeletons from TWR images. Inspired by past works, an optical system is attached to the TWR system to provide cross-modal pseudo labels. Based on a depth design philosophy of convolutional neural networks that meets radar resolution constraints, we design a resolution-guided pose estimation network for keypoint coordinate regression. To alleviate the ill-posed problem, supervising solely the network output is insufficient. The cross-modal supervision is not only built on predictions, but also on features of the network’s hidden layer. With the help of information theory, the mutual information between features and pseudo labels is maximized for feature alignment and discriminability enhancement. Experiments show competitive performance against state-of-the-art RF-based human pose estimation methods and can reconstruct accurate 3D skeletons in multi-target, low-visibility, and wall-occlusive scenes.

This letter introduces a speed-improved imaging technique for a real-aperture MIMO system engaged in off-focus imaging. The existing off-focus imaging algorithm is time-intensive due to the extensive interpolation and reliance on traditional frequency-domain back-projection algorithms (FDBPA). To address its limitation, the least squares (LS) method is used to fit the non-analytical single-trip history function into a second-order polynomial function. Both MIMO array and the wideband received chirp signal are subdivided into multiple sub-arrays and sub-bands to reconstruct the low-resolution images using FDBPA. The wavenumber spectrums of these low-resolution images are band-limited, thus they can be combined to get the global high-resolution image. Simulation and experiment confirmed the efficacy of the proposed technique called Fusion-BPA. It is a state-of-art, fast algorithm to deal with the non-linear scene imaging problem.

The rational Krylov subspace methods can improve the computational speed compared to conventional time-stepping approaches for calculating 3-D transient electromagnetic (TEM) method forward modeling. However, the rational Krylov subspace method simulates only the step-off response. Because primary source waveforms have nonnegligible effects on the induced responses, it is crucial to model the response induced by any given source waveform. The electric field (e) and the time derivative of the magnetic induction ($\mathrm {d} {\mathbf { b}} / \mathrm {d}t$) are commonly measured TEM responses. Case studies also show the magnetic induction ($\bf b$) response measured by magnetometers has a good resolution for exploring conductive mineral deposits. Therefore, modern TEM forward modeling algorithms should be able to simulate different types of responses. We present a new algorithm for TEM modeling using the rational Krylov subspace method. The following improvements are implemented in our approach: 1) the algorithm can efficiently compute the e and$\mathrm {d} {\mathbf { b}} / \mathrm {d}t$responses, and especially the$\bf b$response, which was less considered in other 3-D TEM studies; 2) a convolution approach is employed that allows the Krylov subspace method to simulate the source waveform effects on all three types of responses; and 3) we present the approach for computing the initial condition of b in cases of using galvanic sources. This work extends the flexibility of existing 3-D TEM modeling algorithms. Numerical examples demonstrate that the new algorithm is accurate and computationally efficient.

China’s Tianwen-1 successfully landed in the southern Utopian Planitia of the Martian surface on 15 May 2021. The Zhurong Rover, equipped with a high frequency full polarimetric Rover Penetrating Radar (RoPeR), travelled 1,921 m to investigate the shallow geological structure and material composition of the Martian weathered layer. In this study, we propose a new processing strategy to estimate surface relative permittivity using the HH and VV reflections of the high frequency RoPeR data. This new strategy is based on the induced field rotation effect, which occurs when orthogonally-polarised electromagnetic waves propagate into an uneven surface with incident angles. 3D time-domain finite-difference simulations were performed using random surfaces with various relative permittivities under the same geometry as the Zhurong Rover. Polarimetric alpha angle versus relative permittivity was then calculated based on the simulation results. At the same time, direct coupling removal, band-pass filtering and channel calibration were performed on the real RoPeR data and clear surface reflections were extracted. The surface reflection amplitudes of the HH and VV were then obtained and the polarimetric alpha angle calculated. Finally, relative permittivity was estimated through the relationship obtained from the simulation results. The average value of the relative permittivity estimated by the proposed approach is 3.292, with a standard deviation of 0.235. This result is consistent with that obtained by orbiting Radar Systems and the low frequency RoPeR system. This study will contribute to the further signal processing and accurate interpretation of real radar data captured by way of RoPeR on Mars.

For fine-grained human perception tasks such as pose estimation and activity recognition, radar-based sensors show advantages over optical cameras in low-visibility, privacy-aware, and wall-occlusive environments. Radar transmits radio frequency signals to irradiate the target of interest and store the target information in the echo signals. One common approach is to transform the echoes into radar images and extract the features with convolutional neural networks. This article introduces RadarFormer, the first method that introduces the self-attention (SA) mechanism to perform human perception tasks directly from radar echoes. It bypasses the imaging algorithm and realizes end-to-end signal processing. Specifically, we give constructive proof that processing radar echoes using the SA mechanism is at least as expressive as processing radar images using the convolutional layer. On this foundation, we design RadarFormer, which is a Transformer-like model to process radar signals. It benefits from the fast-/slow-time SA mechanism considering the physical characteristics of radar signals. RadarFormer extracts human representations from radar echoes and handles various downstream human perception tasks. The experimental results demonstrate that our method outperforms the state-of-the-art radar-based methods both in performance and computational cost and obtains accurate human perception results even in dark and occlusive environments.

Ground-penetrating radar (GPR) has been widely used to detect subsurface objects. In recent years, deep learning techniques have achieved significant success in image recognition, which has potential implications for interpreting GPR data. However, reliable training of deep learning models requires massive amounts of labeled data, which can be difficult to obtain due to the high costs of data acquisition and field validation. This letter proposes a GPR data augmentation method based on generative adversarial networks (GANs) - Wasserstein GANs (WAEGAN). This proposed method utilizes a GANs model with an encoder E, a joint generator G, and a discriminator D to generate data. A pre-trained classifier C imposes target category constraints on the generated data, while a Wasserstein loss function is employed to stabilize the training process.The performance of the proposed method is evaluated in terms of the validity, diversity, and impact on the classifier performance of the generated fake GPR data. The experimental results verify the superiority of the proposed method in simultaneously generating multiple target categories and generating GPR data that conforms to reality.

Through-wall radar (TWR) can image the target of interest and capture the human sensing information. However, the poor human interpretability of TWR images and the lack of effective supervision make the extraction of complete body contour intractable. This letter proposes dual UNet, an unsupervised human contour extraction method for TWR images. Specifically, the method adopts two UNets with the same structure. One serves as the encoder to convert the TWR images into the latent representation. Another serves as the decoder to reconstruct the latent representation into the original images. Reconstruction loss and smooth normalized cut loss are optimized together to offset the dependence on labels and supplement global segment constraints. After training and post-processing, the latent representation can be used as the result of contour extraction. Experimental results show that dual UNet stands out among unsupervised human contour extraction methods in both free space and wall-occlusive scenarios, opening the possibility of learning useful human sensing information from raw TWR images without manual annotations.

The transient electromagnetic (TEM) method utilizes artificial transmitters and measures electromagnetic (EM) responses to reveal the resistivity information of the subsurface. The current waveform of transmitters has nonnegligible effects on induced fields. Therefore, 3-D TEM forward modeling algorithms need the capability of simulating arbitrary waveforms to obtain accurate responses. In time-stepping-based 3-D TEM forward modeling, the source term (ST) approach is frequently used, which employs the source current density to model the waveform variation during time-stepping. The ST approach, however, requires fine-time discretization to describe complex waveforms, which could significantly raise the computational cost. We present a robust convolution (Conv) approach that computes the convolution between the time derivative of the waveform and the step-off response to incorporate the waveform effects in 3-D TEM modeling. The Conv approach does not discretize the waveform using time steps. Hence, it is advantageous when modeling full-waveform cases. The developed algorithm is based on the finite-element (FE) method using unstructured grids and the implicit backward Euler approach. Both galvanic and inductive transmitters are incorporated. Ground and airborne TEM surveys are tested using an actual airborne TEM waveform, a full waveform of the$2^{(n)}$-sequence pseudorandom signal, and various synthetic waveforms. Accuracy is validated against the 1-D and 3-D solutions of published studies. The ST and Conv approaches are compared. Synthetic examples show that the latter approach simplifies the waveform incorporation in TEM modeling and substantially improves time-stepping efficiency without sacrificing accuracy.

China’s first Mars rover named “Zhurong” has begun its exploration at the surface of the Utopian Plain in the Martian northern hemisphere on May 22, 2021. Mars rover penetrating radar (RoPeR) is one of the key payloads onboard the Zhurong rover and one of its prima scientific objectives is to detect potential water ice and/or dry ice in the subsurface soil at the landing site. The high-frequency channel of RoPeR, which is equipped with a fully-polarized antenna array with a center frequency of 1.3 GHz, can record full polarimetric radar reflections from subsurface anomalies. In this article, a radar system with a polarimetric antenna array the same as RoPeR was set up and carefully calibrated. Laboratory experiments were carried out to test the feasibility of RoPeR in the detection and discrimination of potential dry ice and/or water ice in Martian soils. The experimental results indicate that the reflection signals from the bottom of the dry ice and water ice samples present different polarimetric scattering characterizations. The characteristic polarimetric scattering features of dry ice and water ice revealed in this article would guide the analysis and interpretation of the real data acquired by RoPeR on Mars.

This work proposes a new processing method based on orthonormal basis functions (OBFs) for two-dimensional (2D) magnetic anomaly data, which is an innovative extension of one-dimensional (1D) OBFs. In most surveys, ferromagnetic targets are usually regarded as magnetic dipoles. The magnetic anomaly field of a dipole in a horizontal plane is essentially a linear combination of linearly independent basis functions, which are then transformed into a set of 2D orthonormal basis functions (2D-OBFs) using the Gram–Schmidt algorithm. On the basis of the 2D-OBFs, a processing procedure for 2D magnetic anomaly data is established and named the 2D-OBF method. This method converts magnetic anomaly maps to energy maps, in which there is only one positive peak corresponding to the horizontal location of a magnetic dipole. As a consequence, the detection and localization are significantly simplified. To verify this new method, a total of 3000 Monte Carlo simulations were carried out, and the synthetic data were also processed using the traditional 1D-OBF method. The statistics of the 2D-OBF results showed that the localization accuracy and false alarm rate are 0.34 m and 1.9 %, respectively, at a data noise level of 10 nT, which was much better than the 1D-OBF method. Another synthetic data experiment verified the reliability of the 2D method for handling multiple targets simultaneously, even when the magnetic anomalies of adjacent targets are severely superimposed. For the measured aeromagnetic data, the 2D-OBF method also works well. Additionally, this method exhibits outstanding noise immunity in all tests. The signal-to-noise ratios (SNR) of the energy data are improved by approximately 14 dB compared to that of the magnetic anomaly data within the signal bandwidth. Developed from rigorous mathematical physics definitions, the 2D-OBF method is more conducive to the implementation of automatic data processing programs and has considerable practical promise.

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.

An efficient algorithm based on chirp scaling algorithm (CSA) for terahertz (THz) stepped-frequency (SF) synthetic aperture radar (SAR) imaging is proposed in this letter. We call this efficient algorithm SF-CSA for convenience. THz imaging techniques have good prospects in the fields of military, public safety, and nondestructive defect identification. In these fields, real-time imaging is very important. CSA is an efficient algorithm suitable for SAR real-time imaging, but it can be only used for linear frequency-modulation (LFM) SAR imaging. The main contribution of this letter is the successful application of CSA in THz SF SAR imaging. Compared with other existing THz SF SAR imaging algorithms, SF-CSA has higher efficiency and is more suitable for real-time imaging. The validity of SF-CSA is demonstrated by theoretical derivation, simulation, and experiment in this letter. In the simulation and experiment, the radar operates at 300 GHz providing a maximum bandwidth of 28.8 GHz, leading to a range resolution of 5.2 mm. The simulation and experiment results show that SF-CSA has high quality and efficiency for THz SF SAR imaging.

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.

Recent through-wall radar (TWR) systems can reconstruct the pose of human targets blocked by occlusion. They rely on the fusion of optical and radar data to avoid the painful annotation burden. However, the fusion process is not always reliable, especially for human joint coordinates that carry 3-D spatial information. Inspired by cross-modal learning and self-supervised learning, this letter proposes a two-stage 3-D human pose reconstruction method for TWR systems. In the cross-modal supervision stage, the pretrained optical model provides initial noisy labels extracted from optical images. In the self-supervision stage, supervised labels and the model weight are corrected circularly with radar images. The self-supervision enhances the robustness of the model and the reliability of labels. It can be directly extended to existing radar-based pose reconstruction methods, and hardly requires extra training time. Experiments show the model beats state of the art (SOTA) for reconstructing 3-D poses from TWR images and contains robust generalization in unseen wall-occlusive scenes.

This work presents the results of the Lunar Regolith Penetrating Radar (LRPR), installed at the bottom of the Chang’E-5 (CE-5) lander, which is the first antenna-array radar deployed for the investigation of an extraterrestrial body. Radar imaging results unveiled a hyperfine structure for the top 2.5-m-thick lunar regolith with an unprecedented high resolution of 5 cm. The interpretation of the results permitted to state that subsurface regolith is dominated by fine grains with abundant rock fragments. The radar imaging results were also used to drive the drilling process until a fragment jammed the core inlet at about 100-cm underground. Laboratory measurements of the samples returned at Earth allow to estimate the real part of relative permittivity and the loss tangent, which are 3.04 ± 0.02 and 0.014 ± 0.002, respectively.

An efficient algorithm based on the frequency scaling algorithm (FSA) for terahertz (THz) stepped-frequency (SF) synthetic aperture radar (SAR) imaging is proposed in this article. THz SF SAR has an urgent application demand in close-range medical imaging, close-range security check, and near-space military target reconnaissance. In these fields, the real-time performance of the imaging algorithm is of great significance. The back projection algorithm (BPA) is an accurate time-domain imaging algorithm, but it has a huge amount of calculation and cannot meet the requirements of real-time imaging. The frequency-domain SF SAR imaging algorithms are based on interpolation, which will cause Gibb’s phenomenon. An efficient THz SF SAR imaging algorithm named SF-FSA is proposed in this article, which uses the frequency scaling operation and phase multiplication to complete precise range cell migration correction (RCMC), thereby avoiding Gibb’s phenomenon caused by interpolation while ensuring high efficiency. In particular, compared with other interpolation-based algorithms, SF-FSA only needs the fast Fourier transform (FFT) and complex multiplication, which is easier to implement on FFT-based signal processors and fast parallel signal processors. By the formula derivation of SF-FSA, the problem on how to select parameters in the frequency scaling operation in SF-FSA is solved, the aliasing phenomenon caused by the frequency scaling operation in SF-FSA is explained, and the corresponding method to avoid the aliasing phenomenon is given. Finally, the validity and feasibility of SF-FSA are demonstrated by simulation and experimental results.

Aeromagnetic noise compensation is a vital part of aerial survey measurement, and its compensation effect directly determines the quality of aeromagnetic survey data. At present, the commonly used compensation model is the T-L model, and the least squares method is used to solve for the coefficients. However, the noise source modeled in the T-L model is incomplete. Since the tail boom cannot be completely rigid, tail-boom swing is an unavoidable problem in aeromagnetic measurement. This kind of swing is the most obvious when the aircraft is maneuvering, and it will significantly interfere with the measurement data of the sensor. In this article, two causes of the swing noise are analyzed, and the nonlinear relationship between the swing displacement and the noise is derived. Since it is difficult to express the nonlinear relationship with mathematical forms to compensate for the aeromagnetic data, we propose a new compensation method that uses a 1-D convolutional neural network to perform secondary compensation on the data already compensated by the T-L model in order to remove the effect of tail-boom swing. The flight experiment data show that the proposed method can significantly improve the quality of aeromagnetic data. Compared with the T-L method, the improve ratio is increased by 60%–100%. It shows that the proposed method has a remarkable compensation effect for aeromagnetic noise.

In this paper, we propose a novel processing algorithm for FMCW ice-sounding radar which addresses the along-track focusing of internal reflecting horizons. It is a modified frequency scaling algorithm that can handle the two-layer medium imaging model as well as the Doppler impact of the motion within the sweep in a nadir-looking radar. The proposed algorithm’s comprehensive derivation and implementation processes are described. In the range-Doppler domain, a really effective formulation for radar signals is also presented, which takes into account refraction effects and electromagnetic wave propagation velocity changes at the interface of two different mediums. Point target simulations are carried out to demonstrate the algorithm’s performance. The proposed method is also tested on the real data acquired by the Shallow-Layers-Detection Ice Sounding Radar (SLDISR). The result shows the applicability of the proposed method for imaging the ice sheet.

The accumulation rate is a key parameter in computing the mass balance of glaciers and ice sheets to estimate sea level rise. A shallow-layers-detection ice sounding radar (SLDISR) is developed to measure the accumulation rate and shape of near-surface internal layers with high resolution. With a transmitting frequency from 500 to 2000 MHz, this frequency-modulated continuous wave (FMCW) radar provides a range resolution of about 16 cm in free space by using a Hanning window and a penetrating depth about 150 m under polar ice. The spectral analysis and coherent integration techniques are used to obtain a high processing gain and to improve the signal-to-noise ratio of the system. A phase-locked loop with wideband yttrium iron garnet (YIG) oscillator is applied to generate a sweeping chirp signal as an input source for the transmitter. A stable, low-frequency reference chirp signal is generated with a direct digital synthesizer (DDS) integrated in field-programmable gate array (FPGA). To reduce the high-speed requirement to the analog-to-digital converter (ADC), dechirp technology is adopted at the RF section of the receiver. The implementation of the digital unit is based on an FPGA chip. The designed radar has been successfully deployed in Antarctica during the 31st Chinese Antarctic Research Expedition (CHINARE 31) and CHINARE 33, mainly over the East Antarctic Ice Sheet (EAIS). The echograms indicate the effectiveness of the radar system on detecting clear internal reflecting horizons (IRHs) over ice sheets.

Although the through-the-wall radar imaging (TWRI) system working in the appropriate frequency band can penetrate the nonmetallic obstacles and sense the targets behind, its low imaging spatial resolution hinders the acquisition of more detailed information, such as human pose and shape. This article mainly discusses a deep learning-based human pose and shape recovery method from TWRI images. Inspired by cross-modal learning, the method follows a teacher–student learning pipeline that avoids the heavy cost of manual labeling. Specifically, a camera is attached to the self-develop radar system to simultaneously capture paired red-green-blue (RGB) images and TWRI images in a scenario without wall occlusion. A pose estimation framework (Hourglass) and a semantic segmentation framework (UNet) serve as the teacher network to convert the RGB images into the pose keypoints and the shape masks. By taking inspiration from the topological architecture of these frameworks, a student network radar pose shape network (RPSNet) is designed to extract the information from the corresponding radar images and predict the keypoints and masks that are close to the results above. Instead of learning two single-task objectives independently, multitasking learning is introduced to adaptatively learn common features. When applied to wall-occlusive scenarios, only the radar images are collected and fed into the student network for pose and shape recovery. The advantages of this method over computer vision-based methods for human recovery are demonstrated in scenarios both without and with wall occlusion.

China’s Tianwen-1 probe carrying the Zhurong rover has successfully landed on the southern Utopia Planitia of Mars. The Zhurong rover is first equipped with a full-polarimetric Mars Rover Penetrating Radar (FP-RoPeR) system, aiming to map the fine structure and potential water-ice distribution of Martian regolith. However, the ground experiment on earth indicates that the RoPeR data is severely affected by noises, clutters, and signal misalignment. More importantly, an imbalance between the data from two cross-polarized channels is observed which is considered to be the interference of the rover. The interference prevents the accurate assessment and analysis of field FP-RoPeR data and may even become the traps in the interpretations of future radar data from Mars. In this article, we firstly analyze the interference in detail and achieve the suppressions of noises, clutters, and signal misalignment; subsequently, a polarized orientation calibration method is proposed to calibrate the imbalance of cross-polarized channels. Finally, we introduce a field experiment at the Ulanhada volcanic geopark in Inner Mongolia Autonomous Region, China. Based on the field RoPeR data and the proposed processing methods, we propose three types of data processing strategies for different aims and present how to use field FP-RoPeR data to analyze subsurface structures and properties.

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).

Helicopter transient airborne electromagnetics (HTEM) has become a useful tool in mineral explorations and geological or environmental detection in recent decades. This article presents the frequency-domain 3-D full-wave inversion of the electromagnetic data recorded by a newly built HTEM system. In the forward process, the secondary magnetic field is calculated through the volume electric-field integral equation (EFIE). In the inversion process, the secondary field is first extracted from the total field measured by the HTEM system. Then the Born iterative method (BIM) is adopted to solve the nonlinear inverse scattering problem for the 3-D reconstruction of conductivity. It is first applied to the synthetic models to verify their effectiveness and accuracy. The effects of adjacent underground anomalies on the 3-D inversion performed in a local region within the long flight lines are studied and discussed. Then the BIM solver is used to invert for the underground anomalies using the field measured data recorded by the newly built HTEM system. The 2-D slices from the reconstruction are compared with the nomogram. It is found that the locations of the high-conductivity regions in the BIM results are consistent with locations of the peaks in the nomogram. The reconstructed profiles are also compared with the drilling data obtained near one flight line. The good agreement shows that the 3-D BIM inversion algorithm can be used to reconstruct the underground ore in HTEM surveys.

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.

A newly designed grounded electrical-source airborne transient electromagnetics (GREATEM) system was introduced recently. Detailed data preprocessing techniques to acquire the high-precision measured magnetic field are discussed here. Different from the previous work in which the reconstruction of the underground structure is performed in 1-D, we interpret the GREATEM data in 3-D by the volume integral equation (VIE) method in the frequency domain. Therefore, the VIE in the forward electromagnetic scattering model is formulated in the low-frequency regime. It is solved by using the stabilized biconjugate gradient fast Fourier transform (BCGS-FFT) method. In the nonlinear inversion, the Born iterative method (BIM) and the conjugate gradient method are adopted to minimize the cost function. A synthetic model of GREATEM survey is used to validate the proposed 3-D forward and inversion algorithms. Then, the field data from two GREATEM surveys are used to test the effectiveness and accuracy of the proposed inversion algorithm. The reconstructed conductivity structures are consistent with geological drilling results, confirming the potential of our method for solving the 3-D GREATEM inversion problems in geophysical engineering applications. This paper represents the first application of the BCGS-FFT and BIM algorithms to a GREATEM system.

The characteristics of the range cell migration (RCM) under single-input-multiple-output (SIMO) bi-static geometry are studied and an omega-K algorithm for near-field 3-D imaging based on planar SIMO or multiple-input-multiple-output (MIMO) array is proposed. The RCM and the linear part of the range offset (RO) within SIMO data are corrected by employing a 3-D Stolt transformation. The residual range error is further compensated by phase multiplication and an image-domain interpolation. Reconstruction for a MIMO aperture can be achieved by adding together all the focusing results from its SIMO subarrays. The implementation details of the algorithm are described. The imaging resolution and the sampling requirements for a MIMO aperture are discussed. Since the RO correction is achieved by an approximate way, the spatial limitation for accurate reconstruction is also given. The imaging accuracy and the high efficiency of the algorithm are demonstrated both by simulations and experiments with various distributed targets based on planar MIMO arrays. Real-time 3-D imaging for planar SIMO/MIMO aperture is expected to be achieved by using the algorithm.

As a main payload of the lander of Chang’E-5, lunar regolith penetrating array radar (LRPR) is a multi-input and multioutput ground penetrating array radar system. The following are the main characteristics of LRPR: it works at a fixed location, the layout of the antennas is irregular, and the amount of data for imaging is very rare. A prestack depth migration imaging method based on Kirchhoff integral is presented, which overcomes the problems of irregular antenna layout and the layered medium. The issue of sparse matrix imaging for LRPR is solved successfully by the method. The laboratory experiment demonstrates that the proposed method is very suitable for LRPR imaging, in which the targets are clearly distinguished and their depths are also consistent with the reality.

A modified omega-K algorithm for planar multiple-input-multiple-output (MIMO) array-based 3-D image reconstruction is proposed. By applying several proper approximations in wavenumber domain, the complex and time-consuming bistatic Stolt transformation within the traditional MIMO-$\omega \text{K}$is changed into a relatively simple monostatic one, and therefore the amount of the interpolation given for the data is greatly reduced. Combining with multidimensional fast Fourier transformation, extremely high computation efficiency can be achieved. Implementation details are well described. The imaging accuracy and the high efficiency of the algorithm are demonstrated by a near-field imaging experiment with a flat mannequin target based on a planar MIMO array radar.

A new iterative inversion algorithm is proposed to reconstruct the electrical conductivity profile in a stratified underground medium for the grounded electrical source airborne transient electromagnetic (GREATEM) system. In forward modeling, we simplify the mathematical expressions of the magnetic fields generated by a finite line source in the layered ground to semianalytical forms in order to save the computation time. The Fréchet derivative is derived for the electromagnetic response at the receivers due to a small perturbation of the conductivity in a certain layer underground. The initial expression of the Fréchet derivative has an expensive triple integral and contains the Bessel function in the integrand. It is simplified by partially eliminating the integration along the source line and deriving the analytical expression for the integration in the vertical direction inside the perturbed layer. In the inverse solution, we use the distorted Born iterative method (DBIM). This is the first time that the DBIM is applied to data measured by the GREATEM system. Besides, the forward and inverse procedures are carried out in the frequency domain and based on the Fréchet derivative of a line source. We demonstrate the validity of our forward model, Fréchet derivative, inverse model, and the precision as well as robustness of the inversion algorithm through numerical computation and comparisons. Finally, we apply the inversion algorithm to the measured data and compare the retrieved conductivity to the actual drilling data.

This paper presents the ice thickness, fine resolution internal reflecting horizons (IRHs), and distinct bottom topography measurements of Chinese Kunlun Station and Grove Mountains, Antarctica, derived from sounding these glaciers with a high-resolution radar. To enable the development of next-generation ice-sheet models, we need information on IRHs, bottom topography, and basal conditions. To this end, we performed measurements with the progressively improved ice-sounding radar system, currently known as the high-resolution ice-sounding radar developed by the Key Laboratory of Electromagnetic Radiation and Sensing Technology of Institute of Electronics, Chinese Academy of Sciences, Beijing, China. We processed the collected data using focused synthetic aperture radar (SAR) algorithm named the modified range migration algorithm using curvelets and the modified nonlinear chirp scaling algorithm to improve radar sensitivity and reduce along-track surface clutter. Representative results from selected transects indicate that we successfully sounded 3-km-thick ice with a fine resolution of 0.75 m. In this paper, we provide a brief description of the radar system, discuss the focused SAR processing algorithms, and provide sample results to demonstrate the successful sounding of the ice sheet in Antarctica.

The polar layered deposits (PLDs) provide a wealth of information about the past climate evolution of Mars. Surface mass fluxes and ice flow mainly governed topography and layering of the PLD. China's Mars probe including an orbiter and a landing rover will be launched by 2020. A new type satellite-borne Mars penetrating radar instrument has been selected to be a part of the payloads on the orbiter. Its main scientific objectives are to map the distribution of water, water-ice and to detect the soil characteristics at global scale on the Martian crust. Compared with Mars Advanced Radar for Subsurface and Ionospheric Sounding and Shallow Radar, the biggest difference is that the antenna system of this Mars penetrating radar consists of two dipole antennas mutually perpendicular. This special configuration enables the investigation of the ice flow of PLD by detecting and analyzing the features of anisotropic crystal-orientation fabric (COF). Thus, relying on the fact that the radio waves are depolarized while passing through an anisotropic COF layer, in this paper, a method for anisotropic COF detection based on this radar system is proposed. The radar echo formulation of anisotropic COF is derived and the ratio of the signals measured by the two perpendicular antennas is used to analyze the anisotropy of COF. We demonstrate that the ratio is an ideal criterion for the detection and analysis of COF, since it contains all parameters about the anisotropy feature of COF and it is independent of the attenuation in the propagation path and the reflection coefficient. In order to verify the validity of the derived analytical expression of the ratio for the detection of COF, finite-different time-domain simulations are carried out based on a simple model of the subsurface of PLD which contains an anisotropic COF layer. The advantages of this method, the potential application scenarios, and the effects of the Martian environment are also discussed.

During airborne transient electromagnetic (EM) surveys, transmitting and receiving antennas change their attitudes as the inevitable result of pilot maneuvers and natural forces, which makes the EM response different from that of the nominal attitudes when the antennas are straight and level. Attitude changes were usually neglected or partially considered in the past, which are not adequate for a quantitative interpretation. In this letter, we first scrutinize the mechanism of how the attitude change affects the EM response and divides these effects into two parts: the pure attitude effect and the resultant translation effect. Then, we introduce a novel method to involve the full attitude change in both modeling and inversion. Our compelling results finally demonstrate that the attitude change affects the early-time response much more than the late time and involving the full change in inversion can produce a better estimate of shallow geoelectric parameters.

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.

Aeromagnetic data are usually contaminated by line-to-line errors, which are most often visible as stripe patterns in a 2-D data map. Leveling is a critical step to eliminate these errors in data processing and interpretation. The conventional tie-line leveling technique involves control lines (tie lines) which are perpendicular to flight lines to extract the leveling errors. By contrast, there are also some other methods to level without the need for tie lines, which can save considerable survey cost. However, a main setback when leveling without tie lines is to distinguish the cross-line gradients from the leveling errors, making the leveled results inferior. In this letter, we revisit tie-line leveling as a nonlinear spatial-filtering technique and analyze the inadequacy of it, based on which a new approach is developed to extract the leveling errors without additional tie lines. The approach includes two steps: designing a virtual frame and leveling based on this frame. The virtual frame is elaborately designed to ensure that each extracted point can represent the local field-intensity level of a flight line and each virtual cross line can bypass the localized areas of magnetic anomalies; hence, some difficulties when leveling without tie lines are overcome. Afterward, two real field data examples are tested to demonstrate the effectiveness of this approach.

The surface clutter simulator and the digital filter technique are developed for sounding and imaging of a subsurface stratified structure with arbitrary ice surface roughness of fast outlet glaciers and ice-sheet margins, which provides a new insight into the strong surface clutter reduction. The radar signal simulation method developed by employing Kirchhoff approximation of rough surface scattering and the ray tracing of geometric optics theory can give clear understanding of electromagnetic (EM) wave radiation and propagation through multilayer stratified media and support the interpretation of radar actual observations for retrieving good information on ice bed and internal layers. The radar sounder echo simulator can calculate the EM scattering from ice internal multilayer rough interfaces and identify whether deeper layers or ice-bed nadir echoes masked by a strong surface off-nadir echo still return to the radar receiver or not, which offers an important tool for designing and optimizing radar system performance. The numerical spline interpolation technique is proposed to discretize the 3-D real ice topography into a suitable triangulated mesh for obtaining higher accurate radar simulation data compared with the facet method. The digital filter technique can be complementary to conventional processing methods in order to distinguish an ice bed from a strong surface clutter with an extremely crevassed rough surface in Antarctica and Greenland. Ice data processing results of certain outlet glaciers' topography are demonstrated. The presented unified data analysis methodology can be also applied to planetary exploration.

In this paper, we propose a new algorithm to address the speckle noise problem in imaging of ice sheets. It is a wave-equation-based ice-sounding imaging method using curvelets as building blocks of ice-sounding data, which successfully images the topography of ice sheets. First, theory analysis has been carried on to the proposed algorithm. Then, we give the specific steps to implement this algorithm. Finally, we apply this algorithm to the simulation point targets and High-Resolution Ice-Sounding Radar data to prove its validity of imaging of ice sheets. Furthermore, compared with five previous methods in two major aspects—the power of clutter reduction and the equivalent number of looks—the proposed algorithm reduces the speckle noise during the imaging processing without degrading the ability in clutter reduction.

In this paper, the defocus of a 2-D image reconstruction of targets illuminated by Gaussian beams in terahertz (THz) single-frequency holography was studied. An analytical point-spread function was derived to quantitatively evaluate the defocus effect, considering the deviation of the restoration distance from the real distance of the target. It is found that the cross-range image defocuses in a similar way to the diffusion of a Gaussian beam from its beam waist, which limits the depth of focus for image restoration. To improve the restored images for targets with varying range distances, an adaptive focusing algorithm based on the minimum-entropy method was proposed for single-frequency holography. Simulation results with fairly good agreement were performed to verify the theoretical results and the proposed algorithm. Proof-of-principle experiments in the 0.2-THz band were also performed based on a monostatic prototype imager with a Gaussian beam transceiver. The experimental results confirm the effectiveness of the adaptive focusing reconstruction algorithm proposed in this paper.

We develop a fast method for deriving the general closed-form perturbative solutions of bistatic scattering from layered structure with multilayer rough interfaces based on the small perturbation method (SPM). The proposed method is developed by theoretical analysis and fully formula derivation instead of considering any other approximation and equivalent process. In addition, our proposed method for completeness of SPM can give the general closed-form solution for the problem of radiation and propagation of electromagnetic fields in a hybrid layered structure with an arbitrary number of rough interfaces and planar stratified medium, which is not available in the previous work yet, and it is highly desirable. The demonstration of the consistency of the presented method is analytically provided.

We propose a new method for bistatic scattering from hybrid subsurface profiles with multilayer rough interfaces and flat boundaries, which the fast method is firstly demonstrated by strictly theoretical formulas in the framework of classical small perturbation method (SPM) without other else approximation and assumption strategy. What is more, we can obtain general compact closed-form first-order perturbation solutions of hybrid subsurface profiles with an arbitrary number of rough and smooth interfaces, which is necessary to insure a successful inversion process. We employ improved inverse algorithms and get successful inverse process based on the proposed forward model.

This letter presents a new method for localizing and identifying a target through walls with known wall parameters. A time-delay difference curve (TDDC) is utilized in this method for target localization and identification. The characteristic of the TDDC can be applied to estimate the incident angle and offset the influences in propagation path and propagation time delay brought from the wall. This letter analyzes the method and gives out the theoretical TDDC. Extensive simulations and experiments show the effectiveness of the proposed method.

Via analyzing the characteristics of life sign, a novel method is proposed to detect multiple trapped victims by ultra-wideband radar in this letter. The signal-to-noise and clutter ratio (SNCR) is greatly improved. The non-static clutter can be removed effectively. The number, range position and respiratory frequency of life signs can be detected automatically and accurately in low SNCR environment. By using experiment and numerical simulation, the good performance of the new method is proved.

The imaging resolutions derived by the time reversal (TR) and back-projection (BP) algorithms, which are widely used in electromagnetic inverse scattering problems, have been investigated in this letter. Under the Born approximation, the point spread functions (PSFs) for TR and BP are first derived and then used to compare their imaging resolutions. The PSFs are examined by numerical experiments, which show that the TR and BP algorithms have the same imaging resolutions whether in the free space or the layered media cases.

In this paper, the wave equation based on phase shift migration technique is extended for terahertz 3-D imaging with quasi-optical transceivers. An analytical expression of the reconstructed 3-D point-spread function for targets under the illumination of a terahertz Gaussian beam was derived with this reconstruction technique. The quantitative relationship between the imaging quality and the parameters of the transmitted Gaussian beam was obtained, which provides a good criterion to be followed when designing the terahertz quasi-optical transceivers in the imaging systems. Moreover, the spatial sampling criterion was derived strictly which is also quantitatively related to the parameters of the transmitted Gaussian beam. Simulation results with fairly good agreement were given to verify the theoretical results derived in this paper. Finally, a monostatic prototype imager with a Gaussian beam transceiver was designed for the proof-of-principle experiments in 0.2-THz band. The 3-D imaging results of different targets and a mannequin with concealed threat objects were given to demonstrate the theoretical results obtained in this paper and the effectiveness of the 3-D terahertz image reconstruction for security applications.

A physics model for the electromagnetic scattering from large-scale rough surface of layered medium is established. Stratton-Chu integral equation and Kirchhoff approximation are adopted to calculate the electromagnetic scattering field. The Gaussian random rough surface is selected to simulate the actual rough surface of layered medium. The scattering echo is detected with a single-station nadir-looking radar sounder. A comparison of radar echoes from Martian mare areas with different roughness is demonstrated. A radar sounder echo simulator is developed, which could be used to validate the radar echoes processing and to support the interpretation of Martian exploration data. By using the simulator, the B-scan images of Martian highland areas with different crater density are studied. Besides, special attentions are paid to the effect of radar waveforms on the B-scan imaging of highland areas. Simulation-based results show that the method presented is feasible in the detection of Martian subsurface structure.

The research on vital information extraction meets a lot challenges due to the narrow bandwidth and low signal-to-noise ratio of a vital sign in real environment. In this paper, a novel noncontract vital sign detection method based on multiple higher order cumulant is presented. According to the characteristic of vital sign for impulse ultrawideband radar, the quasi-periodic reflected echo in slow-time is analyzed. The novel method is theoretically deduced from fourth-order cumulant. It is proved to be better than the reference fast Fourier transform method by simulation and experiment. By using the new method, the range position and frequency information of life can be extracted accurately and automatically.

Via analyzing the characteristics of life sign, a novel method of life detection based on constant false alarm ratio and clustering is proposed in this paper. The signal to noise and clutter ratio (SNCR) is greatly improved. The nonstatic clutter can be removed effectively. The good performance of the new method is proved numerically and experimentally. The range location and frequency information of life sign can be estimated automatically and accurately in low SNCR environment.

A two-dimensional Multi-Resolution Time-Domain iterative formula based on the basis function of compactly supported wavelet is derived, and a basis function selectable electromagnetic scattering forward model is established according to the derived formula. By calculating the electromagnetic fields of target scattering object, the simulation results show that the proposed model has the advantages of high computing accuracy and speed, and requires few number of grids compared with the conventional FDTD algorithm.

This paper proposes a new algorithm to obtain the target shape imaging and track it synchronously using triangle localization method and the difference of time delay in the application of through wall imaging (TWI). The proposed algorithm based on a system model built in the near field can effectively estimate the velocity and track of moving target with the imaging result. Simulation results show that the effect of the wall can be removed visibly and the purpose of the imaging and tracking of moving target can be realized well.

The factors influencing on vital signal detection are discussed by introducing Cramer-Rao Low Bound (CRLB) for Ultra Wideband (UWB) in this paper. The CRLB is described on the basis of the impulse UWB sequential signal module and the Maximum Likelihood rule with Fisher Information Matrix (FIM). A novel detection method based on Multifold High Order Cumulant Fast Fourier Transform (MHOC-FFT) is used to process the simulated noisy signal and to estimate respiratory frequency. Simulated results verify the influence of the key parameters on vital detection. And the novel detection method has high Detectability with the capability to improve SNR and reduce higher harmonics.

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.

A novel ultrawideband (UWB) synchronous receiver for a UWB radar system is presented. The developed receiver is the first to employ a sampling phase detector, which integrates a step recovery diode and a pair of Schottky diodes, to sample the UWB signal of a radar, enabling the miniaturization of the design. It achieves a 6-GHz sampling bandwidth, a dynamic range of more than 50 dB, and low harmonic distortion of the output baseband signal. The receiver's down-converted IF signals closely match with their original shape, demonstrating its good performance to reconstruct the microwave signals from a receiving antenna. These IF signals can be converted to digital signals by a low-cost A/D. For verification, the performance of the UWB receiver is tested through the experiments of buried steels at different thicknesses in the sand when it is applied to the ground-penetrating radar (GPR). The results show that the GPR system has good horizontal resolution and large detection depth. Therefore, this GPR system is very attractive and effective for applications of short-range and high-resolution detection.

To achieve high-resolution 2-D images, through-wall imaging (TWI) radar with ultra-wideband and long antenna arrays faces considerable technical challenges such as a prolonged data collection time, a huge amount of data, and a high hardware complexity. This paper presents a novel data acquisition scheme and an imaging algorithm for TWI radar based on compressive sensing (CS), which states that a signal having a sparse representation can be reconstructed from a small number of nonadaptive randomized projections by solving a tractable convex program. Instead of measuring all spatial-frequency data, a few samples, by employing an overcomplete dictionary, are sufficient to obtain reliable target space images even at high noise levels. Preliminary simulated and experimental results show that the proposed algorithm outperforms the conventional delay-and-sum beamforming method even though many fewer CS measurements are used.

In the above titled paper (ibid., vol. 48, no. 3, pp. 1408-1415, Mar. 2010), there are two errors in the top line of (13) which we correct here.

Most of the landmine detection techniques by GPR use a monostatic type radar system, which is suitable for detecting landmines buried in shallow soil, which is normally less than 10 cm. The shallow target is difficult to detect by conventional GPR due to its low range resolution. We developed a broadband Vivaldi type antenna, which operates at 1GHz-10GHz. Using this antenna, we made a prototype of a GPR system, which operates at 2GHz-5GHz. By scanning the antenna on a 2D plane, we could obtain clear 3-D images of mine-like targets buried in dry sand. We proposed an array signal processing technique using CMP method, and found it is effective for rejection of the ground surface clutter, even if the ground surface has roughness.