Full-waveform inversion (FWI) is one of the most promising techniques in current ground-penetrating radar (GPR) inversion methods. The least-squares method is usually used, minimizing the mismatch between the observed signal and the simulated signal. However, the cycle-skipping problem has become an urgent focus of this method because of the nonlinearity of the inversion problem. To mitigate the issue of local minima, the optimal transport problem has been introduced into full-waveform inversion in this study. The Wasserstein distance derived from the optimal transport problem is defined as the mismatch function in the FWI objective function, replacing the L2 norm. In this study, the Wasserstein distance is computed by using entropy regularization and the Sinkhorn algorithm to reduce computational complexity and improve efficiency. Additionally, this study presents the objective function for dual-parameter full-waveform inversion of ground-penetrating radar, with the Wasserstein distance as the mismatch function. By normalizing with the Softplus function, the electromagnetic wave signals are adjusted to meet the non-negativity and mass conservation assumptions of the Wasserstein distance, and the convexity of the method has been proven. A multi-scale frequency-domain Wasserstein distance full-waveform inversion method based on the Softplus normalization approach is proposed, enabling the simultaneous inversion of relative permittivity and conductivity from ground-penetrating radar data. Numerical simulation cases demonstrate that this method has low initial model dependency and low noise sensitivity, allowing for high-precision inversion of relative permittivity and conductivity. The inversion results show that it, in particular, significantly improves the accuracy of conductivity inversion.

Due to height limitations, the traditional handheld or vehicle-mounted Through-the-Wall Radar (TWR) cannot provide the perspective imaging of internal targets in urban high-rise buildings. Unmanned Aerial Vehicle-TWR (UAV-TWR) offers flexibility, efficiency, convenience, and no height limitations, allowing for large-scale three-Dimensional (3D) penetration detection of urban high-rise buildings. While the multibaseline scanning mode is widely used in 3D tomographic Synthetic Aperture Radar (SAR) imaging to provide resolution in the altitude direction, it often suffers from the grating lobe problem owing to under-sampling in the altitude spatial domain. Therefore, this paper proposes a trajectory planning algorithm for UAV-through-the-wall 3D SAR imaging based on a genetic algorithm to address this issue. By nonuniformizing flight trajectories, the periodic radar echo energy superposition is weakened, thereby suppressing grating lobes to achieve better imaging quality. The proposed algorithm combines the inherent relationship between the flight distance and TWR imaging quality and establishes a cost function for UAV-TWR trajectory planning. We use the genetic algorithm to encode genes for three typical flight trajectory control points and optimize the population and individuals through gene hybridization and mutation. The optimal flight trajectory for each of the three flight modes is selected by minimizing the cost function. Compared with the traditional equidistant multibaseline flight mode, the imaging results from simulations and measured data show that the proposed algorithm significantly suppresses the grating lobe effect of targets. In addition, oblique UAV flight trajectories are significantly shortened, improving the efficiency of through-the-wall SAR imaging.

The jamming in electronic warfare environment significantly degrades the performance of the radar system, especially the mainlobe jamming. The distributed array radar can suppress the mainlobe jamming by increasing the array aperture with auxiliary arrays. However, when the aperture of an auxiliary array is smaller than that of the main array, it results in residual jamming in auxiliary arrays after beamforming and affects the performance of the radar system. To tackle this issue, a mainlobe and sidelobe jamming joint suppression method with eigen-projection matrix processing (EMP) and null constraints is proposed. In the proposed method, firstly, the adaptive beamforming with EMP is performed in main and auxiliary arrays to suppress the sidelobe jamming with mainlobe maintenance. Then, the null constraints based on the Capon spectrum of main array are applied to suppress the residual jamming in auxiliary arrays. Finally, the minimum mean square error (MMSE) beamforming is utilised to cancel the mainlobe jamming. Simulation results demonstrate the jamming suppression performance of the proposed method.

Mars Rover Penetrating Radar (RoPeR) equipped on China’s Zhurong rover has been employed for investigating Martian geology characteristics. The migration algorithm is a common tool to map subsurface structures. However, RoPeR uses a monopole antenna with a tilted angle of 16 degrees. Migration methods depending on omnidirectional radiation antennas can lead to inadequate illuminations for subsurface-inclined geological structures. To overcome this limitation, this paper proposes a radiation pattern compensation reverse time migration (RPC-RTM) method to RoPeR data, which can achieve radiation pattern compensation by an opposite-placed tilted antenna. This study first examined the radiation patterns of horizontal- and tilted-placed monopole antennas, analyzing the response characteristics of antennas to scattering points and inclined interfaces. Then, we illustrated an RPC-RTM algorithm, which employed the opposite tilted antenna to propagate backward wavefields for radiation pattern compensation. Finally, numerical simulations were implemented to explore how different antenna placements influence the illumination of RTM images. Laboratory data were utilized to validate the RPC-RTM method and demonstrate its effectiveness. The proposed RPC-RTM applied RoPeR data to image the Martian subsurface structure. The results show that the proposed method produces high-quality imaging results in insufficient illumination areas and does not require a radiation pattern compensation function. This confirms the efficacy of proposed RPC-RTM method for penetrating radar data acquired through non-standard antenna deployment.

Mini–unmanned aerial vehicles (mini-UAVs) are emerging as a promising platform for through-wall radar to sense the enclosed space in cities, especially high-rise buildings, due to their excellent maneuverability. However, due to unavoidable environmental interference such as airflow, mini-UAVs are prone to trajectory deviation thus degrading their sensing accuracy. Most of the existing approaches model the impact of trajectory deviation into a polynomial phase error on the received signal, which cannot fit the space-variant motion error well. Moreover, the large trajectory deviations of UAVs introduce the unavoidable envelope error. This article proposes an autofocusing algorithm based on the back projection (BP) image, which directly estimates the trajectory deviations between the actual and measured track. Thus, the problem of the 2D space variability of the motion error can be circumvented. The proposed method mainly consists of two steps. First, we estimate the trajectory deviation in the line-of-sight (LOS) direction by exploring the underlying linear property of the wall embedded in the BP imaging result. Then, the estimated trajectory deviation in the LOS direction is compensated for to obtain an updated BP image, followed by a Particle Swarm Optimization (PSO) approach to estimate the trajectory deviation along the track through focusing targets behind the wall. Simulations and practical experiments show that the proposed algorithm can accurately estimate the serious trajectory deviations larger than the range resolution, improving the sensing robustness of UAV-borne through-wall radar greatly.

The strong impedance interfaces of the sea surface and seabed cause significant multiples and ghost wavefield in marine seismic data. The finite-difference method of the two-way wave equation widely used in seismic data modeling cannot separate multiples and ghost wavefield of different orders. That cannot perfectly combine with the migration and parameters inversion process. Traditional methods limit the popularization and application of multiples suppression and migration methods. In this study, the marine seismic wavefield modeling method based on a close-loop one-way propagation operator was derived and the forward modeling equation of ghost wavefield was established. Firstly, one-way propagation operator is derived, and the propagation model of traditional primary reflections is given. Secondly, propagation models of surface- and internal-related multiples are obtained by loading the last round of reflected data at different depths and using closed-loop iterative calculations. The proposed method can achieve multiples simulations and separation of different orders. Then, the ghost waves propagation model is derived by placing the source below the water surface and changing the loading order of seismic sources. Surface-, internal-related multiples, and ghost wavefield of the lenticle model have been accurately simulated and analyzed. The Marmousi model was implemented to compare the accuracy of proposed mothed and finite-difference method, in which the synthetic data of different methods show similar results. The proposed method can reasonably and effectively obtain multiples and ghost wavefield of desired orders, which can be well combined with subsequent migration and inversion processes to improve imaging illumination and resolution.

In through-the-wall 3D synthetic aperture radar (SAR) imaging, under-sampling in the height direction is usually unavoidable, resulting in severe grating lobe effects. Traditional grating lobe suppression algorithms have poor performance, and existing neural network research is limited to 2D SAR images, which cannot achieve high-quality grating lobe suppression for 3D SAR. To solve this problem, this paper proposes a grating lobe suppression algorithm for through-the-wall 3D SAR based on fully convolutional networks (FCN). Simulation results demonstrate that, compared to traditional grating lobe suppression algorithms, the proposed network significantly suppresses grating lobe effects in 3D SAR, achieving optimal imaging quality. Finally, the effectiveness of the algorithm is demonstrated with measured data.

Through-Wall radar plays an important role in detecting moving targets behind walls in counter-terrorism and urban-sensing. While there has been a lot of research on moving target detection algorithm design, most of them suffer from high complexity in implementation for real-time detection, and thus limiting the practical applications. This paper proposes a real-time detection technology of moving target by through-wall radar based on GPU. This method includes multi-stream asynchronous parallel technology of the Displaced Phase Center Antenna based on GPU for clutter suppression module, block-thread parallel technology of the Back-Projection based on GPU for imaging module, and inter-block parallel and intra-block reduction technology of 2D Constant False Alarm Rate based on GPU for detection module. Based on CPU-GPU dual devices in through-wall radar system, this method can output the detection result per second, which meets the needs of real-time detection of moving targets in practical applications.

Through-the-Wall radar transmits an ultra-wideband (UWB) signal capable of penetrating the walls, which can be used to detect targets behind obstacles. Combined with unmanned aerial vehicles (UAVs), the detection of moving targets inside buildings can be realized with high flexibility. However, in the through-the-wall scenario, the target motion leads to poor imaging results and deterioration of detection. So that, a moving target detection method based on azimuthal energy accumulation is proposed in this paper. In detail, firstly, the displaced phase center antennas (DPCA) method is used to suppress the stationary clutters. Then, the keystone transform and azimuthal accumulation are employed. Finally, the detection results are derived by using 1D-CFAR. The effectiveness of the proposed method is demonstrated by experiments.

Ultra-wideband MIMO radar can provide precise range and azimuth information of multiple targets and is widely applied in military, aviation, transportation, and other fields. However, the antenna radiates electromagnetic energy in a specific direction, and the coverage area in azimuth is limited. Compared with targets in the antenna’s main lobe, the large-angle targets outside the antenna’s main lobe scatter weaker radar echoes. Therefore, weak targets will be overshadowed by strong targets. This paper proposes a near-field BP algorithm based on antenna radiation patterns compensation. The antenna radiation pattern was measured in the anechoic chamber. Then, digital beamforming technology was utilized to construct a gain compensation matrix achieving radar data compensation from different directions. Experimental results showed that the proposed algorithm effectively enhanced the energy of large-angle targets in multi-target scenes.

AbstractRotor unmanned aerial vehicles (UAVs) play an important role in both military and civilian fields nowadays. The safety risks associated with the UAVs increase the urgent need for detecting UAVs in urban environments as well. Moreover, UAVs are easily located in the non‐line‐of‐sight (NLOS) building sheltered area relative to the radar, making it very challenging to detect and localise. A novel algorithm for localising the rotor UAV in the common L‐shaped street building sheltered area is proposed. First, the authors establish a multipath signal model for a rotor UAV hovering over an L‐shaped street scene, leveraging the frequency‐modulated continuous wave signal. Then, the multipath information of the UAV is extracted by identifying the rotating periodicity of the blade embedded in the time‐frequency spectrum of the received signal. The back projection imaging is then conducted on the UAV‐related multipath. After extracting multipath ghosts in the image, the street area, where the UAV locates, can be determined, and the UAV is further localised using the path reflection characteristics of this area. Simulations and practical experiments based on millimetre waves indicate that the proposed method can enable high‐accuracy estimation of rotor UAV in the NLOS building sheltered area.

Combining unmanned aerial vehicle (UAV) with through-the-wall radar can realize moving targets detection in complex building scenes. However, clutters generated by obstacles and static objects are always stronger and non-stationary, which results in heavy impacts on moving targets detection. To address this issue, this paper proposes a moving target detection method based on Range-Doppler domain compensation and cancellation for UAV mounted dual channel radar. In the proposed method, phase compensation is performed on the dual channel in range-Doppler domain and then cancellation is utilized to achieve roughly clutters suppression. Next, a filter is constructed based on the cancellation result and the raw echoes, which is used to suppress stationary clutter furthermore. Finally, mismatch imaging is used to focus moving target for detection. Both simulation and UAV-based experiment results are analyzed to verify the efficacy and practicability of the proposed method.

Ground-penetrating radar (GPR) has been extensively utilized in deep-space exploration. However, GPR modeling commonly employs simplified antenna models and carrier-free impulse signals, resulting in reduced accuracy and interpretability. In this paper, we addressed these limitations by combining a tilted monopole antenna and linear frequency modulation continuous wave (LFMCW) to simulate real conditions. Additionally, a radiation-pattern-compensation back-propagation (RPC-BP) algorithm was developed to improve the illumination of the right-inclined structure. We first introduced the LFMCW used by the Mars Rover Penetrating Radar (RoPeR) onboard the Zhurong rover, where frequencies range from 15 to 95 MHz. Although the LFMCW signal improves radiation efficiency, it increases data processing complexity. Then, the radiation patterns and response of the tilted monopole antenna were analyzed, where the radiated signal amplitude varies with frequency. Finally, a series of numerical and laboratory experiments were conducted to interpret the real RoPeR data. The results indicate that hyperbolic echoes tilt in the opposite direction of the survey direction. This study demonstrates that forward modeling considering real transmit signals and complex antenna models can improve modeling accuracy and prevent misleading interpretations on deep-space exploration missions. Moreover, the migration process can improve imaging quality by considering radiation pattern compensation.

Elucidating the functional mechanism of biological enzyme functions is always an important topic in biochemistry because of its great importance for understanding life activities and developing potential drugs.However,ensemble methods commonly used in the analysis of enzyme kinetics can only provide average information on a large number of molecules,

Ground‐penetrating radar full‐waveform inversion is a high‐resolution method for inverting permittivity and conductivity; however, the issue of multi‐parameter crosstalk during the inversion process, in which perturbations of permittivity and conductivity can produce nearly identical observed data, poses a challenge. Additionally, full‐waveform inversion is highly nonlinear and computationally expensive. In this study, we conducted a sensitivity analysis based on permittivity and conductivity perturbations, which demonstrates that their sensitivities vary with frequency. Specifically, conductivity perturbation has a larger impact on low‐frequency data, whereas permittivity perturbation increasingly affects high‐frequency data. Based on the sensitivity analysis, we propose a modified stepped inversion strategy to mitigate multi‐parameter crosstalk. We also employed wavefield reconstruction inversion, which relaxes the wave‐equation constraint as a penalty term and, thus, can help us avoid local minima of the objective function. In contrast, full‐waveform inversion is more prone to get stuck owing to mismatches between modelled and measured data during the inversion process. Finally, we tested the proposed approach on crosshole synthetic data, which achieved significant computational savings and higher inversion efficiency for fewer forward simulations. Our results demonstrate that the proposed approach is a promising method for inverting subsurface structures and has the potential for practical applications in the future.

传统偏移成像方法是建立在波场一次反射的假设条件之上,事实上,完整的地震成像来自于地下全波场(一次波和多次波).为了能够利用全波场信息以提高成像质量,提出了基于单程波算子的全波场最小二乘偏移(FW-LSM)方法.首先,引入地层上、下界面反射系数和背景速度,推导了基于单程波算子闭循环延拓的全波场正演算子,模拟了平滑速度模型情况下的全波场信息;其次,在二范数意义下求解FW-LSM的误差泛函和梯度项表达式,构建基于反演框架的全波场最小二乘偏移方法;最后,针对透镜体与加入水层的Marmousi模型进行测试分析,验证了方法的有效性.研究表明:多轮次反演迭代压制了复杂波场产生的相干假象,多次反射波信息的利用显著改善了成像品质;该方法拓宽了地震成像的手段,尤其在多次波发育的海域地震资料处理中有着重要的作用和潜在的推广价值.