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Different electromagnetic (EM) techniques have distinct resolutions for the exploration of natural resources. Integrating independent information from different EM datasets in a joint inversion results in a more precise and reliable resistivity model. To conduct an efficient joint inversion, it is crucial to understand the resolution characteristics of each EM technique and configuration. To enhance the interpretation using 1-D joint inversion of multiple EM datasets in a former mining area in eastern Thuringia (Germany), we developed a novel workflow for systematic resolution studies. Rather than focusing on limited resistivity models, our approach provides a broader perspective on the resolution characteristics of specific EM methods. The workflow is applied to helicopter-borne EM (HEM), semi-airborne EM (Semi-AEM), and long-offset transient EM datasets. Resolution studies reveal not only the advantages and limitations of each method but also demonstrate scenarios where joint inversion benefits. These insights are useful to optimally design multimethod applications. A synthetic 1-D joint inversion example successfully reconstructs the true model, while each individual method fails. This example demonstrates the added value of our presented resolution study workflow with respect to experimental design and acquiring optimal data in terms of resolution capabilities. We provide a toolkit for resolution analysis that serves as a roadmap for multimethod studies. Subsequently, we demonstrate that the accurate determination of shallow layers improves the resolution for deep targets. This is significant for designing deep exploration prospects. In the last step, our novel triple-method joint inversion scheme is validated through the application of field data.

Through-wall radar systems with single transmitter and receiver have the advantages of portability, simplicity, and independent operation; however, they cannot accomplish two-dimensional (2D) localization and tracking of targets. This paper proposes distributed wireless networking for through-wall radar systems based on a portable single transmitter and single receiver radar. Moreover, a target joint positioning method is proposed in this study, which can balance system portability, low cost, and target 2D information estimation. First, a complementary Gray code transmission waveform is utilized to overcome the issue of mutual interference when multiple radars operate simultaneously in the same frequency band, and each radar node communicates with the processing center via wireless modules, forming a distributed wireless networking radar system. In addition, a data synchronization method combines the behavioral cognition theory and template matching, which identifies identical motion states in data obtained from different radars, realizing slow-time synchronization among distributed radars and thereby eliminating the strict hardware requirements of conventional synchronization methods. Finally, a joint localization method based on Levenberg-Marquardt is proposed, which can simultaneously estimate the positions of radar nodes and targets without requiring prior radar position information. Simulation and field experiments are performed, and the results reveal that the distributed wireless networking radar system developed in this study can obtain 2D target positions and track moving targets in real time. The estimation accuracy of the radar’s own position is less than 0.06 m, and the positioning accuracy of moving human targets is less than 0.62 m.

Ultra-wideband through-wall radar, leveraging its ability to penetrate walls, can be used together with Multiple-Input Multiple-Output (MIMO) technology to image hidden targets behind walls. This approach provides rich information for detecting and locating people within buildings. This paper introduces a closed-loop interferometric calibration method based on a multitransmitter multireceiver ultrawideband wall-penetrating radar system in the Frequency Modulated Continuous Wave (FMCW) regime. This method aims to correct scattering issues caused by internal system errors. The presence of walls causes the target imaging position to deviate from the real position. To address this, this paper derives a three-Dimensional (3D) wall compensation algorithm jointing channels and pixel points. Then, a fast refocusing algorithm is proposed based on the geometric properties of the imaging area. The first step involves removing the influence of walls on delay time and determining the presence of the target. Subsequently, in view of the geometric properties of the region, a spherical coordinate grid division adapted to the region shape is selected. Localized refocusing is then performed in the subregion. This avoids the issue of electromagnetic wave attenuation, causing strong targets to mask weak ones in the imaging results. At the same time, the adoption of spherical coordinates for gridding and localized imaging greatly reduces the overall time consumption by the algorithm. Through simulation analysis and experimental verification, the proposed calibration method can effectively compensate for system errors. The fast refocusing algorithm can be used to realize multitarget 3D localization of the human body behind walls, with the localization accuracy of each dimension surpassing 10 cm and computational speeds improving by five times compared with those of existing algorithms. In terms of target detection probability, the proposed algorithm consistently identifies weak targets that other algorithms may overlook.

In this study, a novel coarse-to-fine image registration algorithm based on feature matching using the Gabor filters is provided for repeat-pass SAR interferometry (InSAR). In the coarse registration, we propose a feature-based algorithm developed using the Gabor odd filter ratio-based operator (GOFRO), and the innovative feature based on the GOFRO (GOFRO-F) consists of the improved nonlinear scale space and the descriptor. The establishment of nonlinear scale spaces informed by the GOFRO enhances the stability of feature detection. Also, an improved 64-dimensional descriptor based on the GOFRO is presented to optimize the capability of feature matching. In the fine registration, we employ the normalized correlation coefficient algorithm (NCCA) and the random consensus sample (RANSAC) for determining offsets and the sub-pixel projection model calculation. The datasets of interferograms are derived from the 0.3 THz stepped-frequency radar. Compared to other algorithms, registration experiments of both simple and complex scatters show that GOFRO-F is more suitable for high precision image registration because of its enhanced robustness to the speckle noise. Furthermore, the three-dimensional (3D) reconstruction is conducted based on our proposed algorithm. The results demonstrate that our proposed algorithm facilitates the attainment of millimeter-level spatial resolutions for InSAR in THz band (THz InSAR).

Multiple-input multiple-output (MIMO) technology is widely used in the field of radar imaging. Array sparse optimization reduces the hardware cost of MIMO radar, while virtual aperture and the equivalent phase center (EPC) principle simplify the radar signal model and reduce the computation and complexity of imaging algorithms. However, the application of sparse array structure and the EPC principle produces a non-negligible phase error, which affects the imaging quality. This paper simplifies the MIMO radar signal model based on the phase center approximation, analyzes the phase error generated by this method, and proposes an improved phase error correction method to solve the problem that the target cannot be well-focused at non-reference distance during imaging. In addition, this paper designs a sparse linear MIMO array with a periodic structure, which reduces the number of transmitting and receiving units, system complexity, and hardware costs. The proposed phase correction method was combined with the wavenumber domain algorithm to simulate and experiment on the designed antenna array, and good experimental results were obtained to verify the effectiveness of the proposed method.

The effect of surface crystallization on the magnetomechanical properties is studied for amorphous ribbons with a nominal composition of Fe80Si9B11 that are prepared with the single roller melt-spinning method. Our results indicate that the magnetomechanical power efficiency of surface crystallization ribbons is twice as higher as that of its amorphous counterpart. The surface configuration for such ribbons has been investigated through a prism of microstructures and chemical compounds. The surface crystallization with quenched-in α-Fe(Si) crystallites is confirmed by means of x-ray diffraction measurements and the surface polishing process, inducing irregular in-plane domain patterns, which are observed by magneto-optical Kerr effect microscopy. The vibrational absorption peak for polymerized B–O bonds is observed by means of a Fourier transform infrared spectrometer, being ascribed to the presence of pre-precipitated boron atoms from the crystalline film, which is confirmed by x-ray photoelectron spectroscopy. The mechanism that enhances the magnetomechanical power conversion efficiency in FeSiB ribbons with quenched-in crystallites is quite different from the ones with annealing induced “surface-interior” configurations in the previous investigations.

The strength of the phonon anharmonic effect of the pristine FeSi2 and Co-doped Fe0.94Co0.06Si2 is investigated by a Raman scattering study on the vibrational properties of those materials in the temperature range of 300–1523 K. All the vibrational modes exhibit significant redshifts with increasing temperature, and their spectral widths increase simultaneously. The structure transition from the semiconducting β phase to the metallic α phase is evidenced by the sudden disappearance of the vibrational modes. The extended Klemens model is applied to study the anharmonic effect on the phonon frequency shift and damping constant, and the four-phonon decaying process is expected to be the dominant one after doping the metal Co. Such an enhancement is also suggested contributing to the reduction of the thermal conductivity in Fe0.94Co0.06Si2. In addition, the vibrational properties of the mode at 250 cm−1 are more sensitive to the anharmonicity effect than that of the mode at 195 cm−1. This work provides valuable insights for understanding the high-order anharmonic effects in thermoelectric materials, especially in chemically doped materials.

Ground-penetrating radar (GPR) is a classic geophysical exploration method that utilizes the emission and reception of electromagnetic waves to non-destructively detect target objects in the target medium. It has been widely applied in various fields such as pipeline detection, cavity detection, and rebar detection. However, GPR systems are susceptible to environmental clutter interference, which poses challenges for data interpretation and subsequent processing. In this paper, the separability of clutter and target signal in the frequency-wavenumber (F-K) domain is validated through modeling, leading to the proposal of a comprehensive clutter removal method based on the F-K domain for complex scenarios. The direct coupling wave is initially eliminated by applying a peak matching mean subtraction filter, which avoids the artifacts. Subsequently, the F-K domain transformation is performed and surface clutter undulations are effectively removed using a method based on singular value decomposition and k-means clustering. Finally, an angle filter with Gaussian tapering at the edges is designed based on physical models to efficiently eliminate linear interference without undesired ringing interference. The commonly used clutter removal algorithms, including mean subtraction (MS), singular value decomposition (SVD), robust principal component analysis (RPCA), and traditional F-K filtering methods, are compared with the proposed algorithm on both the numerical simulated data and actual GPR data. The results from visual and quantitative analysis confirm that our proposed method is more effective than current commonly used clutter suppression algorithms. We have successfully enhanced the Signal-to-Clutter Ratio (SCR) of the GPR data, resulting in an Improvement Factor (IF) of 30.63 dB, 23.59 dB, and 30.60 dB for simulated data, experimental data, and TU1208 public data, respectively. The detection capability of buried targets is enhanced, thereby establishing a solid foundation for subsequent data interpretation and target identification.

Ultra-wideband (UWB) vital detection radar plays an important role in post-disaster search and rescue, but the vital signal acquired in practice is often submerged in noise. In this paper, an advanced signal processing algorithm based on low-rank block-sparse representation is proposed to enhance the vital signal in life detection radar applications. The preprocessed echo signal can be decomposed into low-rank and block-sparse parts. The alternate direction method (ADM) is employed to obtain the block-sparse part containing the desired vital signal. We solve the subproblems involved in the ADM method using the Douglas/Peaceman–Rachford (DR) monotone operator splitting method. The projection method is applied to accelerate the calculation. Simulation and experimental results show that the proposed algorithm outperforms existing methods in terms of output signal-to-noise ratio (SNR).

Life detection is important in earthquake rescue, but weak vital signal is susceptible to interference by clutters. Due to the undesirable characteristics of the hardware, there are two main types of clutter generated by the frequency modulation continuous wave (FMCW) radar when transmitting signals. One is generated by periodic nonlinearity during frequency modulation, and the other is generated by the phase-locked loop spuriousness (PLLS) in frequency division. They cause additional beat frequencies to appear beside those from the target, leading to false alarms. Since the suppression measures for them are different, it is necessary to distinguish the types of clutter and choose appropriate suppression methods. In this paper, the accurate theoretical modeling of the effects of the periodic nonlinearity and phase-locked loop spuriousness on the beat signal is performed to determine distinctions between them. The clutter occurring in the system used is identified as originating from phase-locked loop spuriousness through fiber-optic experiments. A method using long delay lines and cross-correlation is proposed to identify and remove it. In experiments, the false alarm rate is reduced from over 50 percent to nearly 0 percent, providing strong evidence for the effectiveness of the proposed method in through-wall detection.

Abstract With the strengthening of global anti-terrorist measures, it is increasingly important to conduct security checks in public places to detect concealed objects carried by the human body. Research in recent years has shown that deep learning is helpful for detecting concealed objects in passive terahertz images. However, previous studies have failed to achieve superior accuracy and performance for real-time labeling. Our research aims to propose a novel method for accurate and real-time detection of concealed objects in terahertz images. To reach this goal we trained and tested a promising detector based on deep residual networks using human image data collected by passive terahertz devices. Specifically, we replaced the backbone network of the SSD (Single Shot MultiBox Detector) algorithm with a more representative residual network to reduce the difficulty of network training. Aiming at the problems of repeated detection and missed detection of small targets, a feature fusion-based terahertz image target detection algorithm was proposed. Furthermore, we introduced a hybrid attention mechanism in SSD to improve the algorithm’s ability to acquire object details and location information. Finally, the Focal Loss function was introduced to improve the robustness of the model. Experimental results show that the accuracy of the SSD algorithm is improved from 95.04 to 99.92%. Compared with other current mainstream models, such as Faster RCNN, YOLO, and RetinaNet, the proposed method can maintain high detection accuracy at a faster speed. This proposed method based on SSD achieves a mean average precision of 99.92%, an F1 score of 0.98, and a prediction speed of 17 FPS on the validation subset. This proposed method based on SSD-ResNet-50 can provide a technical reference for the application and development of deep learning technology in terahertz smart security systems. In the future, it can be widely used in some public scenarios with real-time security inspection requirements.

With the development of millimeter-wave radar systems, substrate-integrated waveguide (SIW) slot antennas have been widely used. This paper presents a computational approach for calculating the equivalent normalized admittance of slot in low sidelobe SIW longitudinal slot antennas. In contrast to the existing methods, this method takes into account the impact of the N-slots coupling on the admittance. Through this method, fitting functions for resonant length and conductance are obtained, which describe the relationship between the resonant length, resonant conductance, and offset of slots. Moreover, based on the two fitting functions, a low sidelobe SIW longitudinal slot linear antenna is designed for millimeter-wave radar applications at 77 GHz. This antenna consists of a transition from the standard WR-12 waveguide to the SIW and a set of radiating slots. The simulation results indicate an impedance bandwidth of 4.8% and a gain of 13.2 dBi. The sidelobe level in the H-plane is below −28 dB, which meets the desired specifications and confirms the accuracy of the presented method.

In this study, repeat-pass synthetic aperture radar interferometry (repeat-pass THz InSAR) is first extended to the terahertz band, and it has tremendous potential in the application of high-resolution three-dimensional (3D) imaging due to its shorter wavelength, larger bandwidth, and greater sensitivity to elevation variation. The super-resolution and high sensitivity of THz InSAR pose greater demands on the baseline calibration for high-precision digital elevation model (DEM) generation. To meet the elevation accuracy requirement of THz InSAR, we propose a baseline calibration method relying on the estimation of the azimuth fringe frequency (EAFF) of the interferometric phase. Initially, a model for non-parallel sampling path errors within the squint SAR repeat-pass interferometry was established, and then, we conducted the theoretical analysis of the phase errors induced by the non-parallel errors. Following this, using a reference DEM, the relationship between the fringe frequency of the error phase and the bias in the repeat-path positioning was established. This allowed the estimation of the position errors to be transformed into the frequency spectrum estimation based on the FFT, which would mitigate the impact of unknown SAR sampling positions. Ultimately, we investigated the accuracy of the proposed EAFF calibration method, and the simulation showed that it can achieve the theoretical accuracy when the correlation coefficient exceeds 0.3. Furthermore, we configured the repeat-pass THz InSAR system with the 0.3 THz stepped-frequency radar. Compared to the conventional calibration based on ground control points (GCPs), the 3D reconstruction of both a knife and a terrain model, calibrated using the proposed EAFF algorithm, demonstrated that the elevation accuracy can achieve millimeter-level precision across the entire image swath. The above results also proved the great potential of THz InSAR in high-precision 3D imaging and remote sensing.

The resonant magnetoelectric (ME) effect of Fe78Si9B13/Pb(Zr,Ti)O3 (FeSiB/PZT) composites with a surface-modified Fe78Si9B13 amorphous alloy has been studied. The surface-modified FeSiB can improve the ME coefficient at the resonant frequency by optimizing the magnetomechancial power conversion efficiency. The maximum ME coefficient of the surface-modified ribbons combined with soft PZT (PZT5) is two-thirds larger than that of the composites with fully amorphous ribbons. Meanwhile, the maximum value of the ME coefficient with surface-modified FeSiB ribbons and hard PZT (PZT8) is one-third higher compared with the fully amorphous composites. In addition, experimental results of magnetomechanical coupling properties of FeSiB/PZT composites with or without piezoelectric layers indicate that the power efficiency of the composites first decreases and then increases with the increase in the number of FeSiB layers. When the surface crystalline FeSiB ribbons are combined with a commercially available hard piezoelectric ceramic plate, the maximum magnetoelectric coupling coefficient of the ME composite reaches 5522 V/(Oe*cm), of which the electromechanical resonant frequency is 23.89 kHz.

Ground-penetrating radar (GPR) has been widely used in estimating the permittivity of mediums. The radar echo amplitude method is an important method used by GPR in this estimation, the basic step of which is to deduce the magnitude of the permittivity according to the relationship between the reflection coefficient and the permittivity. Based on the basic principle of the radar echo amplitude method, this paper proposes a full-wave inversion real-time permittivity estimation method that can be used for stepped-frequency GPR (SFGPR), which offers high efficiency, accuracy, and generalization ability. The characteristics of this method are mainly reflected in the following four aspects: Using the SFGPR system and introducing a layered media detection model, we can complete waveform compensation optimization with high precision. The distance between the antenna and the surface of the reflective medium is extracted from the time domain waveform without manual measurement, avoiding human measurement errors. The inversion of the total reflection waveform at the required height works under the principle of an electromagnetic field, eliminating the need for repeated metal plate calibration experiments and improving work efficiency and waveform accuracy. In a continuous measurement line, the total reflection waveform inversion on each measurement point can be efficiently completed, and the change of permittivity on the measurement line can be obtained. To evaluate the feasibility of the proposed method, we performed experiments on a wall of known thickness, and the permittivity estimation was basically consistent with that of the dielectric probe, physical model calculation, and wall penetration. We also successfully applied this method to the dielectric property analysis of adobe samples. The results indicate that the proposed method can help grasp the condition of a measured medium, which can ensure the accuracy of detection and improve subsequent data processing efficiency.

In this article, a novel three-dimensional (3-D) imaging method based on the range decomposing algorithm (RDA) is proposed for millimeter wave imaging. We combined it with binomial theory and we derive the theoretical formulation of RDA applied to single-input–single-output (SISO)/multiple-input–multiple-output (MIMO) array; meanwhile, its computational complexity and computational error are analyzed. Compared to the classical Fourier algorithm, such as the range migration algorithm (RMA) and the phase shift migration (PSM), the proposed algorithm can replace the time-consuming interpolation and accumulation operations with reasonable approximations and transformations offering a more efficient approach, while maintaining the image quality. In addition, a method based on RDA which is applicable to the transformation between MIMO and SISO, is proposed to further enhance the processing efficiency. Proof-of-principle simulation using echo data collected from a large number of antennas, verifies that the proposed algorithm has higher efficiency. In order to better verify the feasibility of the proposed algorithm, a scanning prototype located in the millimeter wave band is designed. The experimental results of different targets demonstrate that the proposed algorithm achieves significantly higher reconstruction efficiency when compared to the traditional algorithms.

An efficient spectrum reconstruction algorithm based on the Tikhonov regularization for terahertz (THz) synthetic aperture radar (SAR) azimuth non-uniform sampling is proposed in this article. The high bandwidth, high azimuth resolution, and high frame rate characteristics of THz SAR contribute to its wide application prospects in both military and civilian remote sensing fields. However, the higher azimuth sampling rate also leads to the more severe non-uniform sampling issues of THz SAR. Traditional methods based on the hardware adjustment of pulse repetition frequency (PRF) and simple interpolation for azimuth resampling struggle to meet the higher imaging quality requirements. The back projection algorithm (BPA) can accurately focus non-uniformly sampled data but requires significant computational resources. The algorithm proposed in this paper, which can reconstruct the wavenumber spectrum of SAR azimuth non-uniformly sampled signals, transforms the spectrum reconstruction problem into a linear equation system and solves it using Tikhonov regularization, thereby exhibiting higher computational efficiency compared to BPA. Furthermore, the proposed algorithm is derived from precise theoretical formulations and controls the solution error by utilizing a regularization parameter, leading to a superior imaging quality compared to the azimuth resampling algorithm. In this paper, an accurate spectrum reconstruction formula of non-uniform sampling signals with a finite length is derived, the influence of noise error on the solution is analyzed, and the THz SAR azimuth non-uniform sampling signals are processed from the wavenumber domain. Finally, simulation and experimental results verify the effectiveness of the proposed algorithm.

A large-aperture radar composed of a multiple-input-multiple-output (MIMO) planar array can complete 3D through-wall imaging (TWI), but the simultaneous work of the multiple transceiver channels leads to difficulties in designing the hardware. In engineering, multiple transceiver channels are usually realized by time-division multiplexing (TDM) in MIMO radar, which is called TDM MIMO radar. A time delay will be introduced when the channels are switched, which will cause high sidelobes and position deviation in the 3D imaging of moving targets, also known as range migration. This paper proposes a motion compensation algorithm based on sparse array, designed to eliminate range migration in moving targets in 3D TWI scenes. In the proposed algorithm, the coincident array elements of the equivalent array are used as the compensation channels to calculate the position difference of the target, which can correct the remaining MIMO channels. The proposed algorithm is compared with no compensation, and the reference-channel-based motion compensation algorithm (RCMCA). According to the simulation and experimental results, the proposed motion compensation algorithm can effectively eliminate sidelobes, and keep the position deviation within 0.30 m in the 3D TWI of moving targets under the TDM MIMO radar, without increasing the system complexity.

Ultra-wideband (UWB) multiple-input multiple-output (MIMO) through-wall radar is widely used in through-wall human target detection for its good penetration characteristics and resolution. However, in actual detection scenarios, weak target masking and adjacent target unresolving will occur in through-wall imaging due to factors such as resolution limitations and differences in human reflectance, which will reduce the probability of target detection. An improved U-Net model is proposed in this paper to improve the detection probability of through-wall targets. In the proposed detection method, a ResNet module and a squeeze-and-excitation (SE) module are integrated in the traditional U-Net model. The ResNet module can reduce the difficulty of feature learning and improve the accuracy of detection. The SE module allows the network to perform feature recalibration and learn to use global information to emphasize useful features selectively and suppress less useful features. The effectiveness of the proposed method is verified via simulations and experiments. Compared with the order statistics constant false alarm rate (OS-CFAR), the fully convolutional networks (FCN) and the traditional U-Net, the proposed method can detect through-wall weak targets and adjacent unresolving targets effectively. The detection precision of the through-wall target is improved, and the missed detection rate is minimized.

An ultra-wideband and high-gain Vivaldi antenna with artificial electromagnetic material, suitable for ground-penetrating radar (GPR) systems, is proposed. Directors loaded inside the antenna gradient slot direct electromagnetic waves by inducing current to improve gain. The artificial electromagnetic material, also called metamaterial, is composed of multiple “H”-shaped units arranged in a certain regular pattern, loaded at the antenna aperture. The artificial electromagnetic units affect the antenna radiation waves by changing the refractive index to improve radiation directivity. The four Vivaldi units are arranged into a horn-shaped array, and each two units are orthogonally fed to realize dual polarization. Experimental results demonstrate that the antenna has good impedance matching of S11≤−10 dB in 0.9–4 GHz, and the maximum realized gain can reach 15.2 dBi.

Multiple-input-multiple-output synthetic aperture radar (MIMO-SAR) is being studied and applied in more and more scenarios. However, there is still a certain distance away from real-time imaging using advanced algorithms. The traditional backpropagation algorithm (BPA) multi-accumulation integration is unsuitable for dealing with large-size scanning data, and the wavenumber domain algorithm requires the array to satisfy Nyquist sampling law in azimuth to avoid aliasing in imaging reconstruction. Based on these issues, a novel 3D imaging method is proposed for MIMO-SAR. An appropriate transformation and inverse Fourier transform (FT) is carried out for the frequency domain; thus, accumulation in the wavenumber domain is not required, which is easy to implement. The computational complexity of the algorithm is much lower than BPA and has better generalizability than the wavenumber domain algorithm. Coherence factor (CF) is also introduced to achieve sidelobe suppression. Proof-of-principle experiments were also carried out in the 92.5 GHz band based on the MIMO-SAR prototype system. Both simulation and experimental results of different distributed targets show good performance of imaging and do not lose the quality of image reconstruction.

The atomic magnetometer is currently one of the most-sensitive sensors and plays an important role in applications for detecting weak magnetic fields. This review reports the recent progress of total-field atomic magnetometers that are one important ramification of such magnetometers, which can reach the technical level for engineering applications. The alkali-metal magnetometers, helium magnetometers, and coherent population-trapping magnetometers are included in this review. Besides, the technology trend of atomic magnetometers was analyzed for the purpose of providing a certain reference for developing the technologies in such magnetometers and for exploring their applications.

Abstract Ultra‐wideband (UWB) radar is widely used in static through‐wall imaging (TWI). There are still many imaging problems of motion through‐wall targets for complex system structure and unknown wall environment. Herein, a motion compensation method based on the reference channel and a modified Kirchhoff migration imaging algorithm are proposed for Ultra‐wideband (UWB) MIMO sparse array, which is used for high‐precision 3D TWI of moving targets. The channels of the MIMO radar are time‐division multiplexing based on microwave switch. Besides, a reference channel is added for motion compensation of moving targets. Performing motion compensation on the target before 3D imaging can effectively avoid the problems of defocus and position deviation caused by target motion. In the proposed imaging method, the backward Green function is used to replace the forward Green function to solve the problem of a large field of view with small arrays. The experiment with a static through‐wall target proves that the proposed imaging algorithm can suppress sidelobes well compared with the traditional back‐projection algorithm (BPA). Through‐wall experiments are carried out for targets with different speeds, and results show that the positioning accuracy and sidelobe level of the proposed compensation method is better than previously proposed methods.

China's Mars probe, named Tianwen-1, including an orbiter and a landing rover, will be launched during the July-August 2020 Mars launch windows. Selected to be among the rover payloads is a Subsurface Penetrating Radar module (RoSPR). The main scientific objective of the RoSPR is to characterize the thickness and sub-layer distribution of the Martian soil. The RoSPR consists of two channels. The low frequency channel of the RoSPR will penetrate the Martian soil to depths of 10 to 100 m with a resolution of a few meters. The higher frequency channel will penetrate to a depth of 3 to 10 m with a resolution of a few centimeters. This paper describes the design of the instrument and some results of field experiments.

Abstract This paper proposes the spiral phase contrast (SPC) imaging at terahertz (THz) frequencies to achieve the target edge detection. The THz SPC imaging is built based on a classical 4f imaging system, and a spiral phase plate is placed in the Fourier plane to generate the spiral phase. Owing to the unique nature of the spiral phase, edge detection can be achieved. The THz SPC imaging mechanism is analysed by the scalar diffraction theory, and its corresponding analytic expression is derived. A circle target is simulated and the resultant image shows a remarkable edge detection. A proof‐of‐principle experiment is carried out using our designed THz SPC imaging system and the effectiveness of target edge extraction on the THz images is validated. A circle same as the simulation is imaged firstly, which is consistent with the simulated result. And the letter “Z” with more edges is imaged later. All edges in experiments are extracted perfectly, which are extremely consistent with the THz SPC imaging theory.

The sixth generation (6G) communication will use the terahertz (THz) frequency band, which requires flexible regulation of THz waves. For the conventional metallic metasurface, its electromagnetic properties are hard to be changed once after being fabricated. To enrich the modulation of THz waves, we report an all-optically controlled reconfigurable electromagnetically induced transparency (EIT) effect in the hybrid metasurface integrated with a 10-nm thick MoTe2 film. The experimental results demonstrate that under the excitation of the 800 nm femtosecond laser pulse with pump fluence of 3200 μJ/cm2, the modulation depth of THz transmission amplitude at the EIT window can reach 77%. Moreover, a group delay variation up to 4.6 ps is observed to indicate an actively tunable slow light behavior. The suppression and recovery of the EIT resonance can be accomplished within sub-nanoseconds, enabling an ultrafast THz photo-switching and providing a promising candidate for the on-chip devices of the upcoming 6G communication.

Fe-based amorphous alloys have advantages of low iron loss and high effective permeability, which are widely used in sensors and actuators. Power efficiency is one of the most important indicators among power conversion applications. We compared the magnetomechancial power conversion factors of metallic glassy ribbons FeCoSiB (Vitrovac 7600) and FeSiB (Metglas 2605SA1). We investigated the crystallization process under different annealing temperatures and tested the magnetomechancial coupling factors (k) and quality factors (Q) by using resonant and anti-resonant methods. We found that the maximum coupling factor of the annealed Vitrovac ribbons was 23% and the figure of merits k2Q was 4–7; however, the maximum coupling factor of the annealed Metglas ribbons was 73% and the maximum value of k2Q was 16. We can observe that the Metglas 2605SA1 ribbons have higher values of the magnetomechanical power efficiency than those of the Vitrovac 7600 ribbons, which means they are better to be used in subsequent research regarding acoustically driven antennas.

The group velocity of lightning electromagnetic signals plays an important role in lightning location systems using the time difference of arrival (TDOA) method. Accurate estimation of group velocity is difficult due to the space- and time-varying properties of the Earth-ionosphere waveguide. Besides, the analytical solution of the group velocity is difficult to obtain from the classic mode theory, especially when higher-order modes, anisotropic geomagnetic background, diffuse ionosphere profile, and propagation path segmentation are all taken into consideration. To overcome these challenges, a novel numerical method is proposed in this paper to estimate the group velocity of the lightning signal during ionospheric quiet periods. The well-known Long Wavelength Propagation Capability (LWPC) code is used to model the propagation of VLF/LF radio waves. Since LWPC uses a simplified ionospheric model which is unable to describe the subtle variations of ionospheric parameters over time and space, the IRI-2016 model is incorporated into the numerical modeling process to provide more accurate ionosphere parameters. Experimental results of a VLF/LF lightning location network are demonstrated and analyzed to show the effectiveness of our method. The proposed method is also applicable when there is a sudden ionospheric disturbance as long as the parameters of the ionosphere are obtained in real time by remote sensing methods.

In this paper, the extended phase shift migration (PSM) for three-dimensional (3-D) multi-input-multi-output synthetic aperture radar (MIMO-SAR) imaging in terahertz (THz) band was studied. Based on one-dimensional MIMO arrays combined with synthetic aperture scan along another dimension, MIMO-SAR imaging scheme allows the number of array elements to be greatly reduced compared with the two-dimensional (2-D) MIMO arrays. By analyzing the derived analytical expression of the scattered waves in the frequency-wavenumber domain, the MIMO-SAR data in a certain frequency is mapped to another frequency of the `explode fields' of the monostatic form in accordance with a newly defined 3-D dispersion relations. By multiplying the modified phase shift terms in each frequency of `explode fields', the `explode fields' at different range planes can be reconstructed. Finally, the `explode fields' at time t = 0 can be successfully derived to realize fast imaging reconstruction for MIMO-SAR, with great reduction on the time consuming as compared with the BP algorithm and better accuracy compared with the Stolt migration. Additionally, due to its iterative nature in the range direction, the proposed algorithm is more flexible in treating more complicated scenarios, such as the image reconstruction in the multi-layer medium. A bistatic prototype imager was designed for the proof-of-principle experiments in THz band. The 3-D imaging results of different targets and computational complexity were also given to demonstrate the good performance of the proposed algorithm for THz MIMO-SAR imaging.

In this paper we introduce an end-to-end deep learning (DL) framework for magnetic anomaly detection (MAD) and denoising. This framework consists of two neural networks: a binary classification network for magnetic anomaly detection and a regression network for geomagnetic noise suppression. The two networks work in a cascade mode: the magnetic field measurement is first sent to the detection network to check the existence of the anomaly signal, and then to the denoising network for extracting the signal from the geomagnetic noise if the detection result is positive. The core idea of our proposed method is that the characteristics of both the magnetic anomaly signal and the geomagnetic noise can be learned from massive training data. The experimental results show that: (1) under the same false alarm rate constraint, the probability of detection of our proposed method is above 80% when the signal-to-noise ratio (SNR) equals -6 dB, while the orthogonal basis function (OBF) method fails when the SNR is below 0 dB; (2) for geomagnetic noise suppression, an improvement of 10 to 15 dB is achieved for data with input SNRs between -5 and 15 dB. Our results paved the way for data-driven magnetic anomaly detection and denoising.

An arc-shaped distributed multilayer capacitive loading structure combined with resistive loading is presented in this paper to enhance the radiation performance of folded bowtie antenna (FBA). The radiation efficiency of the loaded bowtie antenna (BA) is improved, and the low-frequency bandwidth is broadened by the bent loop circuits. Additionally, a novel technique of cavity groove is proposed to design the reflector. The optimized cavity tightly coupling with the radiation part overcomes the shortcomings of conventional cavity-backed BAs. The improved ultra-wideband (UWB) FBA with low profile is then designed and fabricated. The basic geometry of the proposed antenna is combined by a novel acorn-shaped bent bowtie patch and a hollowed stepped back cavity. The simulated -10 dB impedance bandwidth of the proposed antenna is over 100 MHz-1100 MHz while the measured operating bandwidth is 250 MHz-850 MHz. This BA of folded type obtains a high realized gain of 4 dBi at 850 MHz and a narrow pattern over the whole band. Time-domain measurements and field tests for practical ground-penetrating radar (GPR) applications have been carried out and the results demonstrate the advanced performance.

Dielectric lenses are widely used in terahertz imaging and communication systems to focus or collimate the Gaussian beam by adjusting the phase distribution of wave front. However, due to the high frequency and short wavelength of the terahertz band, the focusing lenses used in the application systems are usually electrically large, which bring the extremely difficulty to carry out efficient electromagnetic (EM) simulation and optimization design. In this paper, a dimensionality reduction concept was introduced to achieve efficient design and optimization of electrically-large terahertz lenses, which have symmetric structures and are commonly used in quasi-optical systems, such as circular lenses and cylindrical lenses. To precisely solve the EM problem with reduced dimension, a two-dimensional moment method (2D-MOM) for homogeneous dielectric targets was studied and successfully developed by solving the surface coupled integral equation discretized with appropriate basis and test functions. Then, the dimensionality reduction approach with the combination of the ray-tracing method (RTM) and the 2D-MOM was developed for the shaped design of terahertz lens with high efficiency. A 0.3THz lens with diameter 10cm was designed with the proposed approach as an example, with its pattern measured by a terahertz field scanning platform. It's found that, with the dimensional reduction, the unknowns can be reduced more than 1500 times and the memory required can be reduced more than 2.5 million times, as compared to the traditional 3D-MOM simulation. And the simulation results agree well with the experiments, which both demonstrate the greatly improved performance of the lens designed by the proposed approach, as compared to the standard lens.

In this paper, the enhanced phase shift migration (PSM) algorithm for multi-input-multi-output (MIMO) side-looking (SL) scheme was proposed for the 2-dimension (2D) fast image reconstruction in the terahertz (THz) band. By decomposing the MIMO imaging problem into a superposition of several single-input-multiple-output (SIMO) sub-arrays, the proposed algorithm can be applied to systems with randomly distributed transmitting array. By deriving and applying the modified phase shift factor and necessary phase compensation, zero-padding in the transmitting array and data reorganization in the spatial-frequency domain of the conventional wavenumber domain MIMO algorithm can be totally avoided, which will hopefully further reduce the amount of calculation. A comprehensive MIMO-SL-EPSM algorithm was established in the wavenumber domain. Proof-of-principle experiments in the 0.1-THz band were performed based on a MIMO-SL prototype system. The reconstructed images for different targets with high efficiency and quality demonstrate the theoretical results and the effectiveness of the proposed algorithm.

On 1 December 2020, China’s Chang’E-5 (CE-5) probe successfully landed in the northeastern Oceanus Procellarum. This work mainly presents the results of Lunar Regolith Penetrating Radar (LRPR) equipped on the CE-5 Lander. The lunar regolith structure of the landing site from the surface to 3-m depth is unveiled by LRPR, which found that abundant rock fragments are distributed in uniform lunar regolith. The imaging result proved that the drilling and sampling process was prevented by big rocks at about 100 cm depth. On the basis of the response of lunar soil to electromagnetic (EM) wave, the EM properties of the landing site estimate that the relative dielectric constant and the loss tangent are 2.520 ± 0.186 and 0.0133 ± 0.0020, respectively.

Drift detection is one of the important detection modes in a digital ionosonde system. In this paper, a new data processing method is presented for boosting the automatic and high-quality drift measurement, which is helpful for long-term ionospheric observation, and has been successfully applied to the Chinese Academy of Sciences, Digital Ionosonde (CAS-DIS). Based on Doppler interferometry principle, this method can be successively divided into four constraint steps: extracting the stable echo data; restricting the ionospheric detection region; extracting the reliable reflection cluster, including Doppler filtering and coarse clustering analysis; and calculating the drift velocity. Ordinary wave (O-wave) data extraction, complementary code pulse compression and other data preprocessing techniques are used to improve the signal-to-noise ratio (SNR) of echo data. For the purpose of eliminating multiple echoes, the ionospheric region is determined by combining the optimal height range and detection frequencies obtained from the ionogram. Successively, Doppler filtering and coarse clustering analysis extract reliable reflection clusters. Finally, the weighting factor is brought in, and then weighted least-squares (WLS) is used to fit the drift velocity. The entire data processing process can be implemented automatically without constantly changing parameter settings due to changes in external conditions. This is the first time coarse clustering analysis has been used to extract the paracentral reflection cluster to eliminate scattered reflection points and outer reflection clusters, which further reduces the impacts of external conditions on parameter settings and improves the ability of automatic drift measurement. Compared with the previous method possessed by Digisonde Protable Sounder 4D (DPS4D), the new method can achieve comparable drift detection precision and results even with fewer reflection points. In 2021–2022, several experiments on F region drift detection were carried out in Hainan, China. Results indicate that drift velocities fitted by the new method have diurnal variation and change more gently; the trends of drift velocities fitted by the new method and the previous method are semblable; and this new method can be widely applied to digital ionosondes.

Ultra-wideband (UWB) impulse radar is widely used for through-wall human detection due to its high-range resolution and high penetration capability. UWB impulse radar can detect human targets in non-line-of-sight (NLOS) conditions, mainly based on the chest motion caused by human respiration. The automatic detection and extraction of multiple stationary human targets is still a challenge. Missed alarms often exist if the detection method is based on the energy of the human target. This is mainly because factors such as the range of the target, the intensity of the respiratory movement, and the shadow effect will make a difference between the energy scattered by targets. Weak targets are easily masked by strong targets and thus cannot be detected. Therefore, in this paper, a multiple stationary human targets detection method based on convolutional neural network (CNN) in through-wall UWB impulse radar is proposed. After performing the signal-to-clutter-and-noise ratio (SCNR) enhancement method on the raw radar data, the range-slow-time matrix is fed into a six-layer CNN. Benefiting from the powerful feature extraction capability of CNN, the target point of interest (TPOI) can be extracted from the data matrix. The clustering algorithm is used to simplify the TPOIs to achieve accurate detection of multiple targets behind the wall. The effectiveness of the proposed method is verified by the experimental data.

Semi-airborne electromagnetic method (SAEM) is a geophysical electromagnetic detection method which places the transmitting subsystem on the ground and the receiver subsystem on the flight platform. This transceiver configuration has been found to be more efficient than that of the traditional ground electromagnetic methods while it can get much deeper penetration than the traditional airborne electromagnetic methods. Therefore, the SAEM has been widely used in mineral, groundwater, geothermal, and other resources exploration fields. In this paper, we would like to take the most key problems encountered in the SAEM system design and application as the starting point, and then we focus mainly on reviewing the progress of the transmitter, sensor, and receiver technology, as well as data processing, modeling, and imaging methods of the SAEM in China. In particular, the research progress of multi-source SAEMs will be introduced. Beyond that, two prospecting examples with different SAEM systems will be introduced. Finally, we will discuss the future of the SAEM in terms of equipment developing, data processing, modeling and inversion, to promote the research and application in this field to a higher level.

In the helicopter transient electromagnetic system (HTEM), weak-coupling coils reduce the mutual inductance of the transmitting and receiving coils, which can protect the data acquisition circuit and improve the signal-to-noise of the system. The PRBS HTEM system is a newly proposed multi-receiver EM measurement system, which can effectively identify the system impulse response of the unknown geological model based on the high-precision synchronous recording signal of the PRBS emitted current and induced voltage. However, the standard PRBS current signal is turned on/off very quickly, easily resulting in signal saturation. Concerning this problem, this paper proposes a new weak-coupling structure named eccentric dual bucking coils for the multi-receiver EM system by analyzing the on/off characteristics of PRBS current and the magnetic field distribution of the transmitter–receiver system. It also verifies the feasibility of the proposed structure by Maxwell software simulation. Furthermore, considering the influence of the residual primary field and other factors, the data preprocessing results of the PRBS method and the traditional square wave method are compared by theoretical analysis and data simulation, and the results show that the earlier-time response data can be obtained by PRBS method under the same simulation conditions. Finally, the reliability of the proposed method is verified by ground experiment.

A terahertz (THz) passive imager with automatic target detection is an effective solution in the field of security inspection. The high-quality training datasets always play a key role in the high-precision target detection applications. However, due to the difficulty of passive image data acquisition and the lack of public dataset resources, the high-quality training datasets are often insufficient. The generative adversarial network (GAN) is an effective method for data augmentation. To enrich the dataset with the generated images, it is necessary to ensure that the generated images have high quality, good diversity, and correct category information. In this paper, a GAN-based generation model is proposed to generate terahertz passive images. By applying different residual connection structures in the generator and discriminator, the models have strong feature extracting ability. Additionally, the Wasserstein loss function with gradient penalty is used to maintain training stability. The self-developed 0.2 THz band passive imager is used to carry out imaging experiments, and the imaging results are collected as a dataset to verify the proposed method. Finally, a quality evaluation method suitable for THz passive image generation task is proposed, and classification tests are performed on the generated images. The results show that the proposed method can provide high-quality images as supplementary.

The low-frequency electromagnetic fields and magnetic anomalies generated by ships and other underwater platforms are widely recognized as important features for ocean target detection. Low-frequency magnetic fields and anomalies are typically measured by optically pumped magnetometers installed on aircraft. However, the interference that is generated by the aircraft platform may significantly affect the detection performance. The traditional aeromagnetic compensation model has a good effect on eliminating the interference magnetic field that is caused by the carrier attitude variation. Usually, the magnetometer is fixed at the top of a long probe on the aircraft to avoid the influence from the main body in the aircraft. However, the probe is sensitive to external vibrations, and vibration-induced magnetic interference can occur in the measurements. The magnetometer is especially easily affected by the interference magnetic field, including the vibration frequency and harmonic frequency of the probe, in a moving platform, such as a helicopter. These interference fields usually have independent frequency characteristics that can be eliminated by compensation methods. In this paper, we propose a method based on the improved coherent noise suppression method with a pair of magnetometers to eliminate the effects from these magnetic field disturbances and improve the detection performance of the measurement system. The results of the flight experiment show that the method can effectively eliminate low-frequency vibration interference and improve the detection ability of weak signals from targets.

Aeromagnetic surveys play an important role in many fields, for example, archaeology, anti-submarine warfare, and geophysical exploration. Being in the geomagnetic field, the aircraft generates a great deal of magnetic interference, resulting in bad performance during detection surveys. Thus, it is necessary and important to perform aeromagnetic compensation in advance. Conventional aeromagnetic compensation methods consider that the geomagnetic gradient is approximately zero after bandpass filtering, bringing about the inaccuracy of compensation coefficients. To address this issue, an improved aeromagnetic compensation method robust to geomagnetic gradient is proposed. In this study, the International Geomagnetic Reference Field (IGRF) model was employed to model the geomagnetic gradient. Then, the estimated geomagnetic gradient was subtracted from the measured data, which improved the accuracy of the compensation equations. Field experiments were conducted to verify the effectiveness of the proposed method. The experimental results show that compared to the traditional method, the compensation performance of the proposed method was improved by 152% to 329%. For the level flight, the standard deviation of residual noise after compensation can be as low as 3.3pT. The results indicate that the proposed method can significantly improve the compensation effect, showing great benefits for weak magnetic anomaly detection.

In multiple-input-multiple-output synthetic aperture radar (MIMO-SAR) systems, sparse arrays are usually applied, resulting in increased sidelobes of the point spread function. In this paper, a phase shift migration (PSM) imaging algorithm based on the explosion reflection model with modified coherent factor was proposed for sidelobe suppression in MIMO-SAR three-dimensional (3D) imaging application. By defining the virtual difference wavenumber, reconstructing the raw echo by data rearrangement in wavenumber domain, the original coherent factor algorithm operating in spatial domain can be achieved by the PSM algorithm frame in the wavenumber domain, which means two orders of magnitude increase in computational efficiency. The correctness of the theory is verified by simulation. Finally, a bistatic prototype imaging system in the 0.3 THz band was designed for the proof-of-principle experiments. The experimental results show that the proposed algorithm has a 0.948 structural similarity value to the original coherent factor back-projection algorithm (CF-BPA) which means comparable image quality with much superior efficiency.

Ultra-wideband (UWB) impulse radar is widely used for through-wall human respiration detection due to its high range resolution and high penetration capability. UWB impulse radar emits very narrow time pulses, which can directly obtain the impulse response of the target. However, the time interval between successive pulses emitted is not ideally fixed because of timing jitter. This results in the impulse response position of the same target not being fixed, but it is related to slow-time. The clutter scattered by the stationary target becomes non-stationary clutter, which affects the accurate extraction of the human respiration signal. In this paper, we propose a method for reducing timing jitter’s impact in through-wall human detection by UWB impulse radar. After the received signal is processed by the Fast Fourier transform (FFT) in slow-time, we model the range-frequency matrix in the frequency domain as a superposition of the low-rank representation of jitter-induced clutter data and the sparse representation of human respiratory data. By only extracting the sparse component, the impact of timing jitter in human respiration detection can be reduced. Both numerical simulated data and experimental data demonstrate that our proposed method can effectively remove non-stationary clutter induced by timing jitter and improve the accuracy of the human target signal extraction.

In practical situations such as hostage rescue, earthquake and other similar events, the ultra-wideband (UWB) life-detection radar echo response from the respiration motion of the trapped person is always quasi-/non-periodic in respiration frequency or very weak in respiration amplitude, which can be called quasi-static vital sign. Although it is an extremely difficult task, considering the economic cost, the detection ability of the traditional UWB life-detection radars with only a pair of transceiver antennas is desired to be enhanced for locating the quasi-static trapped human being. This article proposes two different detection methods for quasi-static trapped human beings through the single/multiple observation points, which corresponds to the single-/multi-station radar operating mode, respectively. Proof-of-principle experiments were carried out by our designed radar prototypes, validating the effectiveness of the proposed methods.

In this paper, we propose a terahertz (THz) spiral spatial filtering (SSF) imaging method that can enable image contrast enhancement. The related theory includes three main steps: (1) the THz image of the target is Fourier transformed to the spatial spectrum distribution; (2) the spatial spectrum is modulated by a spiral phase at the Fourier plane; (3) the filtered spatial spectrum is inverse Fourier transformed to the desired THz image. Meanwhile, analytic expression of the final THz image is derived. Due to the unique nature of the spiral phase, THz image contrast enhancement can be achieved and verified by various simulated target images with different contrasts. In our designed THz SSF imaging system, Fourier transform is carried out by the lens, and the spiral phase is acquired by the spiral phase plate (SPP). Proof-of-principle experiments with three different types of targets (carved metal letters, a high-density polyethylene (HDPE) piece with a scratch, and a leaf) were carried out, and the effectiveness of contrast enhancement and edge extraction on the THz reconstruction images was validated.

Terahertz (THz) imaging technology has received increased attention in recent years and has been widely applied, whereas the three-dimensional (3D) imaging for moving targets remains to be solved. In this paper, an adaptive 3D imaging scheme is proposed based on a single input and multi-output (SIMO) interferometric inverse synthetic aperture radar (InISAR) imaging system to achieve 3D images of moving targets in THz band. With a specially designed SIMO antenna array, the angular information of the targets can be determined using the phase response difference in different receiving channels, which then enables accurate tracking by adaptively adjusting the antenna beam direction. On the basis of stable tracking, the high-resolution imaging can be achieved. A combined motion compensation method is proposed to produce well-focused and coherent inverse synthetic aperture radar (ISAR) images from different channels, based on which the interferometric imaging is performed, thus forming the 3D imaging results. Lastly, proof-of-principle experiments were performed with a 0.2 THz SIMO imaging system, verifying the effectiveness of the proposed scheme. Non-cooperative moving targets were accurately tracked and the 3D images obtained clearly identify the targets. Moreover, the dynamic imaging results of the moving targets were achieved. The promising results demonstrate the superiority of the proposed scheme over the existing THz imaging systems in realizing 3D imaging for moving targets. The proposed scheme shows great potential in detecting and monitoring moving targets with non-cooperative movement, including unmanned military vehicles and space debris.

This paper considers the detection and localization of a human subject in complex environments using an ultra-wideband impulse radar. The subject is remotely sensed by extracting micro-motion information, such as the respiration and heartbeat frequencies. It is challenging to extract this information due to the low signal to noise and clutter ratio in typical disaster environments. To improve the localization accuracy, a new method is proposed using the characteristics of vital sign signals. The range is determined using a short-time Fourier transform of the kurtosis and standard deviation of the received signals. Furthermore, an improved arctangent demodulation technique is used to determine the frequency of human micro-motion based on a multiple frequency accumulation method. Performance results are presented, which show that the proposed method is superior to several well-known techniques.

In actual life-detection radar applications, a quasi-static person with weak respiration is difficult to find when relying on the echoes from a single fixed observation point. To effectively sense the weak respiration of a quasi-static person in complex through-wall and through-floor conditions, this paper proposes a novel multi-observation point detection system composed of multiple Golay complementary coded radars in which communication and synchronization are carried out wirelessly. The collaboration structure and Golay complementary coded transmitter improve the signal to noise ratio (SNR). Proof-of-principle experiments are carried out with our designed radar prototype and prove that the radar system can detect a respiring target 21 m behind a brick wall or a respiring target behind two levels of reinforced concrete floors, validating the effectiveness of a multi-observation point working mode for the efficient detection of weak human respiration.

This paper analyzes the noise sources in photoelectric detection circuits with several low-noise operational amplifiers cores. The fabricated circuits are low-noise pre-amplifiers that are used for optically pumped magnetometers. In the proposed circuits, the noise levels of equivalent output voltage are calculated, and the results are in accordance with measurements. With a cooperation of several operational amplifiers, we select LT1028 from linear technologies as the core for our detection circuit, which has an output signal-to-noise ratio of more than 2 × 105 up to the frequency of 100 kHz. By analyzing the individual noise sources in the detection circuit, the dominant noise source is confirmed as the photocurrent shot noise below 200 kHz. Beyond this frequency, the voltage noise source in the operational amplifier dominates. Besides, the lamp power, the radio frequency (RF) power, the temperature variations, and their influences on the sensitivity are studied and optimized. Finally, an optically pumped magnetometer with cesium head is established, showing an intrinsic sensitivity of 85 fT/√Hz. This sensitivity is realized under a geomagnetic magnetic field strength of 53 μT.

Precise characterization of reinforcing bars (rebars) in a concrete structure is of significant importance for construction quality control and post-disaster safety evaluation. This paper integrates ground-penetrating radar (GPR) and electromagnetic induction (EMI) methods for simultaneous estimation of rebar diameter and cover thickness. A prototype of GPR-EMI dual sensor is developed, and a calibration experiment is conducted to collect a standard EMI dataset corresponding to various rebar diameters and cover thicknesses. The handheld testing cart can synchronously collect both GPR and EMI data when moving on the concrete surface, from which a data processing algorithm is proposed to simultaneously estimate the rebar diameter and cover thickness. Firstly, by extracting the apex of the hyperbolic reflection from the rebar in the preprocessed GPR profile, the rebar position is determined and further used to extract the effective EMI curve. Then, the rebar diameter and cover thickness are simultaneously estimated from the minimum mean square error between the measured and calibrated EMI data under the constraint of the GPR-estimated cover thickness. A laboratory experiment is performed using four casted concrete specimens with 11 embedded steel rebars. The results show that the diameters of 10 rebars are correctly estimated out of the 11 rebars, and the maximum estimation error for the cover thickness is 6.7%. A field trial is carried out in a newly-constructed building, and the diameters of four tested rebars are all accurately estimated while the estimation errors of the cover thickness are less than 5%. It is concluded that the developed GPR-EMI dual sensor and the proposed algorithm can estimate the rebar diameter and cover thickness accurately by a single scan.