Terrorist attacks pose a severe threat to global public security, among which pre-embedding offensive weapons in walls is an important attack type. Therefore, obtaining information about buried objects is crucial for addressing potential threats. Ground-penetrating radar (GPR), as an established non-intrusive detection method, is capable of effectively acquiring the three-dimensional (3D) information of buried objects. However, traditional GPR imaging methods often fail to complete the 3D reconstruction task for targets under conditions of low signal-to-noise ratio (SNR). Moreover, the large computational load limits its application in real-time detection scenarios. To address these challenges, this paper proposes a robust and lightweight 3D reconstruction method for buried targets, specifically for pistols, eavesdropping devices, and explosives, etc. The method first utilizes the 3D Fourier transform of the wave equation to analyze the target wavefield information from 3D C-scan data, which can obtain a preliminary visualization of the target, then employs a projection mechanism to process the sparse energy-focused volume from multiple views. Finally, a multi-view reconstruction algorithm is used to reconstruct the 3D voxel model of the target. Compared to existing methods, our approach reduces parameters by 98.9% to 48.59M versus 3D U-Net, FLOPs by 79.7% versus Kirchhoff migration, and achieves 0.575-second inference with a 0.829 Dice coefficient under optimal conditions. It maintains robust performance down to -5 dB SNR, with monotonic improvements in MSE, MAE, and Dice as SNR increases. Experimental results indicate that the approach facilitates rapid and accurate retrieval of information on concealed dangerous objects in resource-limited settings.

Non-contact vital sign sensing has attracted increasing attention from both academia and industry. Millimeter-wave radar-based respiration sensing provides high accuracy but suffers from a limited field of view and strong dependence on target position and orientation, which restricts its practical deployment. In contrast, Wi-Fi-based sensing provides wide coverage and great resilience over device setup, yet its respiration estimation accuracy under ideal conditions is generally inferior to that of millimeter-wave radar. Consequently, the robustness of single-modal approaches in practical scenarios remains limited due to the inherent drawbacks of each system. This paper presents a fusion-based respiration monitoring system that integrates millimeter-wave radar and Wi-Fi signals. By jointly exploiting the high precision of millimeter-wave radar and the wide-area sensing capability of Wi-Fi, robust respiration monitoring is achieved in practical bedroom environments. To effectively utilize heterogeneous signals, a fusion decision scheme is designed to adaptively determine the necessity of signal fusion. Furthermore, a multi-modal signal fusion method based on multivariate signal processing is proposed to jointly extract the common respiration components shared across different modalities. Extensive experimental results demonstrate that the proposed system, FuseRes, can robustly and accurately estimate respiration in complex real-world scenarios, supporting stable non-contact vital sign monitoring and facilitating practical deployment.

Three-dimensional (3D) building layout sensing is a key capability for Internet of Things (IoT)–enabled smart city applications, including post-disaster assessment and urban security monitoring. However, acquiring reliable 3D building layouts from an external perspective remains challenging in complex urban environments due to severe signal attenuation and multipath effects. This paper proposes an IoT-enabled unmanned aerial vehicle (UAV)–based through-the-wall radar (TWR) sensing framework for large-scale 3D building layout reconstruction. In the proposed framework, UAV-mounted radar sensors act as mobile IoT sensing nodes to collect multi-view sensing data. A multi-layer wall echo propagation model and an angle-weighted three-dimensional back-projection (BP) imaging algorithm are employed to generate multi-view 3D synthetic aperture radar (SAR) representations. An enhanced U-Net architecture is then developed to fuse the multi-view SAR data and reconstruct clearer 3D building layout representations. The simulation and real-world experimental results show that the proposed framework achieves improved reconstruction performance over representative existing methods under the tested conditions, indicating its potential for IoT-oriented smart city sensing.

Identifying indoor individuals using micro-Doppler signature of multiple-input multiple-output (MIMO) through-the-wall radar (TWR), and determining whether they pose a threat holds significant research value in the field of urban security surveillance. However, large-scale TWR human motion data is difficult to collect, which reduces the recognition performance. To address this issue, a MIMO TWR micro-Doppler signature representation method under limited data based on heterogeneous transfer learning is proposed in this letter. The multi-channel TWR human motion Doppler-time maps (DTMs) are first generated, and the trace-ratio group sparse method is then proposed for multi-channel DTM feature augmentation. In addition, a micro-Doppler signature representation method based on optimal transport domain adaptation heterogeneous transfer learning is proposed. By leveraging large-scale millimeter-wave radar human gait data, the proposed method guides the micro-Doppler signature representation to maximize inter-class separation on the TWR DTM set. The effectiveness of the proposed method is validated through a few-shot measured dataset collected for TWR human threat identification.

Through-the-wall radar (TWR) can monitor and analyze the motion characteristics and activity patterns of indoor human targets, with the advantages of non-contact, high flexibility and privacy protection. However, existing TWR human activity recognition (HAR) techniques developed based on single-channel radar contain limited Doppler information, making it difficult to achieve accurate recognition on data where the direction of human motion is not parallel to the radar observation. To solve this problem, in this letter, a multi-input- multi-output (MIMO) TWR micro- Doppler signature augmentation method based on multi-channel information fusion is proposed. First, a multi-channel Doppler profile feature fusion method based on multi-scale wavelets with low-rank decomposition is presented. Then, a motion parameter estimation method based on Broyden-Fletcher-Goldfarb-Shanno (BFGS) global optimization is proposed, and the fused Doppler profile transformation is implemented using the obtained orientation of human motion. Numerical simulated and measured experiments demonstrate the effectiveness of the proposed method. The open-source code for this work can be found at:https://github.com/JoeyBGOfficial/MIMO-Through-the-Wall-Radar-Micro-Doppler-Signature-Augmentation.

Synthetic aperture radar (SAR) enables all-weather, all-day, high-resolution, and large-area observations, which is of great significance for comprehensive ground observation. The interrupted sampling repeater jamming (ISRJ) which can create numerous deceptive targets within SAR images, has a substantial impact on the detection of actual targets. However, inaccurate prior jamming information and the difficulty of precise parameter estimation often limit the effectiveness of existing ISRJ suppression methods. To address this issue, this letter presents an ISRJ extraction and elimination method using phase code waveform. In the proposed method, the intermittent sampling frequency is obtained by constructing its range-frequency offset map using the raw echo. Subsequently, the jamming images are derived by performing frequency offset on the received echo with the estimated intermittent sampling frequency. Finally, the jamming is eliminated by subtracting the raw SAR image and the jamming image. A series of experiments are conducted to demonstrate the validation of the proposed method.

Building layout sensing of through-the-wall radar (TWR) plays a vital role in fields such as counter-terrorism operations and post-disaster rescue. Existing layout sensing methods based on TWR typically focus solely on either corner information or wall surface features, neglecting the complementarity between the two, which leads to low sensing accuracy in complex environments. To address this issue, we propose a Corner-Wall Sensing Network (CWSNet), a building layout sensing network that fuses corner and wall surface information. First, deep convolutional networks are used to extract wall and corner features from TWR images. Then, these complementary structural features are fused to form an integrated representation. Finally, a transformer-based dynamic graph reasoning module (DGRM) captures their spatial relationships, enabling high-precision layout sensing. Both simulated and real-world experimental datasets demonstrate that CWSNet significantly outperforms existing methods across multiple evaluation metrics, achieving superior wall localization accuracy and layout connectivity, while also exhibiting strong robustness and generalization capabilities.

The detection of indoor moving targets using autonomous-aerial-vehicle (AAV)-mounted through-the-wall radar (TWR) has been widely applied in both military and civilian fields. However, the imaging results of moving targets often suffer from defocusing and are prone to be submerged in strong stationary clutter, which reduces the detection rate of moving targets. To address this issue, this article proposes a detection method for indoor moving targets based on Doppler chirp rate profile and range gating filter using AAV-mounted TWR. In the proposed method, the characteristics of Doppler chirp rate for both clutter and moving targets are analyzed. The range migration of the target echo is corrected in the range-Doppler (RD) domain through sinc interpolation. Then, the fractional Fourier transform (FrFT) is applied to estimate the chirp rate at each range bin. Subsequently, the estimated chirp rate values are compared with the theoretical chirp rate values of stationary objects. Based on the differences of Doppler chirp rates, a gating filter in fast time domain is designed to suppress clutter originated from walls and stationary objects. Finally, an azimuth compression filter is constructed to achieve focused imaging of moving targets. Both simulation and field experiments demonstrate the feasibility and effectiveness of the proposed method. The proposed method shows strengths over other methods in terms of improvement factor, image entropy, and peak-sidelobe ratio.

Indoor human detection and location is a prominent research area within the internet of things, which can be effectively realized using radar technology. However, the electromagnetic waves will be reflected by the wall, leading to multipath ghosts in radar image, resulting in increased false alarm rates and degraded target location accuracy. In order to address this challenge, a multipath ghost recognition and suppression method based on template matching is proposed in this paper. It is proved that the shape can be modeled as ellipse and the major axis and rotation angle of ghost is different from that of target. Two distinct features are calculated and used to achieve ghost recognition, and ghost mask is generated further. The effectiveness and robustness of the proposed method are validated through simulations and experimental results. Particularly, the proposed method does not depend on the information of building layout and is geometry-layout-free.

Through-the-wall building interior structure sensing has been greatly serving in various applications, including search-and-rescue operations. However, most existing methods exhibit limitations in imaging the walls and corners with good continuity and recognizable features. In this article, we consider imaging of the building interior structures by extracting the major building elements with structural continuity. Specifically, the signals from a complex building are first modeled as the superposition responses from discrete canonical scatterers, such as planar walls and wall corners. Then, a structural variational Bayesian method is designed to detect and extract these critical structures. This method improves the 1-D continuity of the walls and the 2-D continuity of the corners through a Bayesian hierarchical probabilistic model. Moreover, we incorporate the generalized approximate message-passing technique into the variational expectation maximization method to efficiently estimate the walls and corners simultaneously. Results from both simulated and real data validate the effectiveness of the proposed method in accurately extracting walls and corners with improved continuity, thereby enabling a comprehensive building structure.

The hyperbola detection in the ground-penetrating radar (GPR) data is of real significance for subsurface object localization. However, present detection methods in GPR cannot process B-scan data with arbitrary length. In this letter, a hyperbola detection method based on the state-space model (SSM) for arbitrarily long GPR B-scan is proposed. The proposed method consists of three parts: a time dimension encoder based on the ResNet block, an SSM module, and a time dimension decoder based on the transposed convolution. First, the time dimension encoder extracts high-dimensional time dimension signal features from each scan channel of B-scan data. Then, the SSM module extracts bidirectional correlation features along the survey line from the feature maps obtained in the first part, thereby obtaining feature maps containing hyperbolic target features. Finally, the time dimension decoder decodes the features obtained in the second part and outputs the probability map representing the target hyperbolic vertex region. Based on the probability map, the hyperbola detection and localization results can be obtained. The effectiveness of the proposed method is verified by both simulation and field experiments using GPR B-scan data. In addition, the experimental results show that the proposed method can achieve the AP with 96.9%. The code is available athttps://github.com/yimaxwell/state-space-model-for-GPR-detection.git.

Through-the-wall radar (TWR) can track moving human targets in obscured spaces, providing real-time information to operators. Changes in the position and angle of targets during movement will lead to diversity in the scale and orientation of the radar images, causing difficulty in target tracking. In order to solve this problem, this letter proposes a TWR target tracking method based on scale-adaptive rotation kernelized correlation filter (SA-RKCF). In the proposed method, the size and angle of the target region are both estimated, which are used for training samples generation and correlation filter updating. The candidate target region of current frame is delineated based on the geometric information of the previous frame, and then the correlation filter is used to localize the target position. The effectiveness of the proposed method is verified using both numerical simulation and experiment.

Moving target detection in through-the-wall scenario can be achieved by utilizing unmanned aerial vehicle (UAV) radar. However, considering indoor human target detection, the distance between the radar and the moving target is relatively short, and the ratio of target's velocity to the UAV's velocity varies greatly, which makes the range migration and Doppler phase much more complex and poses huge challenges for coherent integration. To address this issue, this letter proposes a moving target coherent integration method based on time reverse conjugate multiply and keystone transform (TRCM-KT) for UAV-mounted through-the-wall radar. In the proposed method, the reference signal is constructed by azimuthal time reverse and conjugation. Then, by multiplying the echo signal and the reference signal, the second-order range migration and doppler phase terms are eliminated. Next, keystone transform is employed to correct the remained first-order range migration. Finally, the fully coherent integration results can be obtained by performing Fourier transform along the azimuth direction. The effectiveness of the proposed method is verified by both simulation and experiment results.

Pipeline and void recognition are two key tasks in the underground monitoring of urban roads. Ground penetrating radar (GPR), as an effective geophysical method, plays an important role in this area. With the increasing amount of GPR data, automatic recognition has become a research hotspot. However, existing automated recognition methods still suffer from low accuracy or high dependence on datasets. In this paper, a non-learning method for both pipeline and void recognition is proposed. In this method, the B-scan is preprocessed by removing the direct coupled wave and multiple echoes.Then, the binarized image is converted to sparse image using non-zero interval sparse coding (NISE). Next, the slope distribution of sparse images is extracted using column offset coding (COE). And clustering is carried out according to the corresponding relationship between the image slope and its original position. Finally, the decision is made according to the slope distribution characteristics of each cluster. The proposed method was tested on both simulated and field data. Experimental results show that the method not only has the advantage of being training-free but also exhibits excellent recognition accuracy.

In this article, we present a layered cross correlation back-projection (LCBP) method based on ray-theory for electromagnetic (EM) imaging in stratified medium. To argue the challenge of resolving time delay in the stratified medium in traditional time-domain methods, we first construct a ray propagation model, then calculate the time delay at any point on the path and solve the sparse time delay at the deep position by linear interpolation, Finally, a clutter suppression method is used to reduce interference through the cross correlation of signals between different subapertures. LCBP achieves fast imaging in a stratified medium environment whereas retaining the advantages of the BP algorithm in the focusing ability. In order to verify the performance of the proposed method, the simulation and experimental data are carried out with comparisons of the phase shift migration (PSM) imaging method and layered range migration (LRM), and the proposed method exhibits excellent imaging quality.

Layered parameter estimation represents a crucial application of ground penetrating radar (GPR), playing a pivotal role in reconstructing the internal structures of media. In situations where adjacent media are similar and multiple reflected waves are present, conventional methods face substantial challenges in detecting weak echoes and accurately extracting time delays. To achieve precise estimation of layered media parameters in the presence of similar materials and multiple reflected waves, this paper presents a modified Orthogonal Matching Pursuit (OMP) parameter estimation method. This method extracts the correct time delays by eliminating multiple reflected waves and integrates the constructed generalized reflection coefficients to recover the signal. Subsequently, a genetic algorithm is utilized to optimize the constructed objective function, enabling the precise estimation of the thicknesses and permittivities of adjacent layers with similar media. This method is particularly applicable to scenarios involving two or more subsurface layers with nearly identical permittivities. Finally, numerical and real experiments have been conducted to validate the accuracy and effectiveness of the proposed method.

Unmanned aerial vehicle (UAV) through-the-wall (TTW) synthetic aperture radar (SAR) extends traditional remote sensing into penetration perception of obstructed areas in high-rise buildings with significant advantages. However, image defocusing caused by motion errors severely hinders the application of UAV TTW SAR. The mismatched signal model in the TTW condition leads the ineffectiveness of conventional airbone autofocus, while wide-beam and wide-band characteristics of the UAV TTW SAR system further aggravate the defocusing issue. In the paper, an effective metric-based motion error estimation with ground Cartesian back-projection (GCBP) algorithm is proposed. Unlike conventional SAR scenarios, a parametric signal model of UAV TTW SAR is established by incorporating refraction approximation which is a critical distinction absent in traditional remote sensing. Phase errors are further analysed from the perspective of geometric, frequency-dependent, time-variant, and space-variant characteristics. Then, aperture division and subband division are integrated with GCBP algorithm for efficient imaging. With the constraint of the UAV motion continuity, a metric-based optimization method is designed. Gradient descent combined with the line search strategy constitutes an iterative optimization mechanism. Finally, through the verifications of simulations and experiments, the proposed algorithm realizes precise motion error estimation and achieves fabulous refocusing performance in UAV TTW SAR. This technology holds immense potential for large-scale through-wall sensing of high-rise buildings, bridging the gap between traditional remote sensing and concealed space sensing in urban environments.

Through-wall radar has been very pertinent to a variety of civilian and military services because of its ability to detect and sense through the wall obstacles. However, to maintain the penetrating ability, most of the existing TWR systems work at L/S band with limited bandwidth and thus can only generate a very crude blob of the target, whose resolution is not easy-to-use for many practical applications. To address this issue, this paper proposes a novel high resolution TWR imaging system by deep learning-based data fusion and reasoning techniques. First, we devise an image-reasoning module by fusing TWR and optical images with generative adversarial networks. Then, in the online phase, the low-resolution TWR image is fed into the image-reasoning module for resolution improvement. Extensive simulations and experiments demonstrate that the proposed method can successfully reconstruct the outline of an object rather than just a blob, which greatly eases the end user to interpret and thus facilitating more applications.

Through-the-wall radar can detect and locate targets behind obstacles, which has been widely applied in various civil and military applications. However, wall reflections are usually stronger than those of the targets, making it very challenging to extract the target information. To tackle the problem, this paper proposes an adaptive wall clutter mitigation method. First, the discrete prolate spheroidal sequence basis is used to model wall clutter due to its superiority in modeling the spatially extended property of the walls. Then, by incorporating the compressed sensing technique, the subspace pursuit algorithm is combined with block and adaptive iteration to estimate wall clutter. In an adaptive manner, the proposed algorithm greatly eases the requirement on priors and thus improves the applicability in practice. Practical experiments commendably validate the effectiveness and superiority of the proposed method.

Due to the electromagnetic propagation loss and environmental interference, the deep ground penetrating radar (GPR) image is poor for further interpretation. Many image enhancement methods including resolution improvement and clutter removal have been widely studied to improve the GPR image quality. In this letter, a deep GPR image enhancement method is proposed to generate clear high-resolution images by the diffusion model (DM). In order to ensure the model is equipped with abilities of resolution enhancement and declutter simultaneously, the low-frequency image inserted with clutter performances as prior knowledge input network to fit the Gaussian distribution clutter added in the forward process of the DM. The method has already been tested by simulation and field experiments. Compared with the classical methods of declutter only and improving the resolution only, our method achieves the best results by synthesizing entropy (EN), peak signal-to-clutter ratio (PSCR), and structural similarity (SSIM), which proves the effectiveness of resolution enhancement and clutter removal in the deep GPR image.

Through-the-wall radar (TWR) utilizes range and Doppler information to achieve indoor human activity recognition. However, traditional recognition methods are developed based on range-time maps (RTM) and Doppler-time maps (DTM), resulting in low accuracy and poor robustness. In order to solve these problems, this letter proposes to use micro-Doppler corner feature to achieve activity recognition and gives an adaptive corner feature extraction method based on difference of Gaussian (DoG) filter and deformable convolution. Micro-Doppler corner feature is defined as the points on the radar squared-range and squared-Doppler images where the gray scale changes sharply in different directions, reflecting the inflection, stationing, intersection, and boundaries of the motion trajectory curves of the human limb nodes. The proposed corner feature extraction method utilizes the DoG filter to extract the micro-Doppler corner supervisory labels on simulated data. The labels are then used to train the$\boldsymbol{\mu }$D-CornerDet, which is constructed based on deformable convolution network (DCN), task-adaptive deformable convolution network (TDCN), feature pyramid network (FPN) and learnable regression global attention module (LRGA). For predictions, only$\mu$D-CornerDet is used on measured data to obatin the corner feature maps. Both numerical simulations and experiments are conducted to verify the effectiveness and robustness of the proposed method.

In order to address the contradiction between azimuth resolution and system complexity, sparse array is employed in through-the-wall radar for the purpose of detecting moving targets in shadowed spaces. However, the radar images can be distorted due to azimuth grating-lobes and high sidelobes. To mitigate this issue, a through-the-wall radar imaging grating-lobe and sidelobe suppression method based on imaginary sign coherence factor is proposed in this letter. Theoretically, it is demonstrated that the phase across different channels is symmetrical at the azimuth grating-lobes and sidelobes in centrosymmetric radar. Then, the weight is calculated based on the positive and negative consistency of the imaginary part of the imaging results in each channel. The azimuth grating-lobes and sidelobes are suppressed by applying weight to the original image. Through simulations and experiments, the effectiveness and robustness of the proposed method are validated. The azimuth grating-lobes and sidelobes are suppressed efficiently without increasing the computation amount, demonstrating its practical feasibility.

Multiple-input multiple-output (MIMO) through-the-wall radar (TWR) is utilized for target detection under indoor environments. However, target signals are often overshadowed by wall clutter. Existed clutter suppression methods predominantly work effectively for TWR under synthetic aperture mode, with limited approaches for MIMO TWR. Therefore, this letter introduces amplitude coherence factor (ACF) to suppress the wall clutter. In the proposed method, the ACF is denoted by using the variance across all transmit-receive channels, which directly reflects the correlation of echo amplitudes in each pixel. Then, weights based on the ACF are assigned to the image of raw TWR image. The performance of ACF is validated by simulation and experimental results.

The recognition of underground pipelines is an important in urban areas. As an efficient and non-destructive recognition method, ground penetrating radar (GPR) has been increasingly applied in the recognition of underground pipelines. With the growing volume of GPR data, there is an urgent need for automatic recognition. However, due to the complexity of the subsurface environment, existing automatic recognition methods still have drawbacks such as low accuracy, poor robustness, and the requirement for large training datasets. An underground pipeline recognition model for GPR that combines scale-invariant feature transform (SIFT) and support vector machine (SVM) is proposed in this article. The model is based on the fact that there are scale-invariant keypoints at the tops of hyperbolas. First, SIFT is used to identify scale-invariant keypoints in the image. These keypoints undergo symmetry assessment and feature enhancement. Subsequently, SVM is employed to filter out the keypoints located at the tops of the hyperbolas. Finally, keypoints located on the same hyperbola are clustered to obtain the recognition results. The model improves the original SIFT method by modifying the calculation of the blur coefficients in the Gaussian pyramid layers. It also employs manually designed feature enhancement methods when constructing the feature descriptors. Additionally, we have introduced methods such as symmetry judgment to further enhance the model’s accuracy. The results indicate that the proposed method exhibits superior recognition performance for the field data even with very limited training samples.

The common middle point (CMP) ground penetrating radar (GPR) utilizes the time-delay information under different antenna separations to realize layered inversion. However, parameter inversion of multilayered media is mostly subject to heavy computational complexity and worst estimation error in the determination of refraction positions. To address the problems, an effective parameter inversion method is proposed based on off-grid sparse CMP model with refined orthogonal matching pursuit (OMP) for multilayered parameter inversion. In the proposed method, a sparse CMP signal model with accurate reflected wave propagation modeling is developed based on a refraction approximation method. An off-grid sparse CMP model is further constructed based on second-order Taylor expansion to overcome the deviation from the grid node. Then, a refined OMP algorithm based on compressed sensing (CS) is proposed with an off-grid optimization process to achieve accurate off-grid parameter inversion. Finally, the effectiveness of the proposed method is verified by simulations and experiments.

Through-the-wall radar can detect and localize hidden targets behind obstacles, which has been widely applied in varieties of civil and military applications. However, wall reflections are usually stronger than that of the targets, making it very challenge to extract the target information. In this paper, an adaptive wall clutter mitigation method is proposed. The discrete prolate spheroidal sequence basis is first used to model the wall clutter due to its superiority in modeling the spatially extended property of the walls. Then, by incorporating the compressed sensing technique, we propose a block adaptive subspace pursuit algorithm to estimate the support of the wall, which is further sifted by leveraging the correlation of the wall clutter over different antenna positions. In an adaptive manner, the proposed algorithm can realize the mitigation of wall clutter and separate the target echo data without wall parameters or other prior information, which greatly improves the robustness in practice. Extensive simulations and real-world experiments commendably validate the effectiveness and superiority of the proposed method.

Layered medium parameter inversion is a crucial technique in ground-penetrating radar (GPR) data processing and has wide application in civil engineering and geological exploration. In response to the issues of high computational complexity and low accuracy associated with existing methods, a novel layered medium parameter inversion approach is proposed, comprising the deconvolution autoencoder and the parameter inversion network. First, the deconvolution autoencoder is introduced to solve the pulse response of layered medium systems in an unsupervised manner, which enhances the computational efficiency of deconvolution and decouples the data acquisition system from the supervised model. Subsequently, a parameter inversion network, including a self-attention module and a residual multilayer perceptron (MLP), is proposed to address the challenge posed by the excessively sparse pulse responses. The self-attention module calculates the autocorrelation of the pulse sequence, providing temporal delay information between pulses and reducing the sparsity of the pulse response to facilitate feature extraction. Meanwhile, the residual MLP, known for its low information loss and adaptability to different output dimensions, is employed for model-based and pixel-based inversions in situations with and without prior knowledge of the layer number, respectively. Finally, simulated and measured datasets are constructed to comprehensively train and evaluate the proposed method. The results demonstrate that the proposed method exhibits better performance of inversion accuracy, computational efficiency, robustness, generalization capability, and noise resistance. In addition, it remains applicable even when prior knowledge of the layer number is unknown.

Through-the-wall radar (TWR) achieves indoor human activity recognition (HAR) by extracting Doppler and micro-Doppler features. However, the conventional deep learning-based HAR methods have the shortcomings of low accuracy and long inference time. To solve these problems, a lightweight multiscale neural network for indoor HAR based on macro and micro-Doppler features (TWR-FMSN) is proposed in this article. In the proposed method, the trajectories of macroscopic Doppler and microscopic Doppler features are defined first and the integrated models are applied to label the trajectories at both scales for recognition. An efficient attention-mechanism-based lightweight target detection neural network with the Lagrangian trajectory estimation is proposed to obtain macro-Doppler features of human motion. In addition, a kernel-distance-based micro-Doppler labeling method is utilized to obtain the micro-Doppler features of human motion. Finally, all the extracted macro-Doppler and micro-Doppler features are concatenated together for the decision of indoor HAR. The effectiveness of the proposed method is verified by experiments, and the results show that the proposed method can significantly reduce the inference time while retaining high recognition accuracy, which shows great potential in real-time deployment for the practical application.

Media parameter inversion is an important issue in the field of electromagnetic propagation widely applied to multilayered scenarios in ground penetrating radar (GPR). However, due to unsolvable refraction points in multilayered conditions, computing burden in iterative optimization, and unreachable global optima in multi-objective optimization, the existing inversion methods cannot effectively realize the multilayered inversion. For the problem, demanding for high precision and high efficiency in multilayered applications, a multilayered media parameter inversion based on reflected trajectory fitting is proposed. The method adopts a layer-by-layer inversion mechanism with a novel refraction approximation. At each layer inversion, it utilizes the reflected travel equation of the first A-scan and the reflected trajectory fitting by least square linear regression to draw two relation curves whose intersection position determines layer thickness and permittivity. Finally, through simulations and experiments, the method shows its accuracy and effectiveness in the multilayered structure.

The performance of ground penetrating radar (GPR) target detection is seriously affected by the clutter. In this letter, an effective GPR clutter removal method is proposed based on low-rank and sparse decomposition with total variation regularization (LRSD-TVR). In the proposed method, a total variation (TV) regularization of sparse matrix is introduced to further remove the remaining clutter and to obtain a clearer target image. An iterative approach based on alternating direction method of multipliers (ADMM) is developed to solve the optimization problem of LRSD-TVR. In each iteration, the low rank component which corresponds to the clutter is computed by singular value decomposition (SVD) thresholding. Besides, the sparse component corresponding to the target is obtained by solving the sub-optimization problem reformulated in terms of TV component. The effectiveness of proposed method is verified by both numerical simulations and field experiments.

Through-the-wall radar (TWR) is commonly employed for indoor targets imaging and detection. However, the targets’ echoes are often whelmed by the reflected waves of the wall and the rebars inside. Since the shape of clutter caused by rebars is similar with the target echo in B-scan, the performance of many clutter suppression methods in the literature degrades severely. Aiming at the problem of rebar clutter removal, this paper proposes a rebar clutter suppression method based on range migration compensation and low-rank and sparse decomposition with total variation regularization. In the proposed method, Hough transform is used to locate the rebars after removing the reflected waves of the wall. Then, the rebars’ echoes are transformed into a horizontal line by range migration compensation in order to make it in a low-rank subspace. Based on the model of low-rank sparse decomposition and total variation regularization, the target matrix is extracted. Finally, inverse range migration is performed to obtain the target signal. Numerical simulation and experimental results demonstrate the effectiveness and superiority of proposed method in terms of target-to-clutter ratio (TCR).

Ground-penetrating radar (GPR) is an effective detection tool for multilayered structures such as road detection and underground exploration. However, traditional inversion algorithm in GPR cannot achieve layered media parameter inversion accurately due to neglect of multiple reflections and complicated refraction effect. To address this issue, an inversion algorithm for layered media parameters based on common middle point (CMP) model and pattern search (PS) method is proposed in this letter. The proposed method combines the wave propagation equation and Snell’s law to build the cost functions in CMP model, and takes the layered media parameters as the optimization variables. Both numerical and experiment results indicate that inversion efficiency and accuracy can be improved. The novel inversion algorithm saves at least a quarter of time to control relative errors within 1% and 6% in simulation and laboratory experiment, respectively. The field test in asphalt highway shows its potential benefits in the field of multilayered structure.

In order to solve the problems of reduced accuracy and prolonging convergence time of through-the-wall radar (TWR) human motion due to wall attenuation, multipath effect, and system interference, we propose a multi-link auto-encoding neural network (TWR-MCAE) data augmentation method. Specifically, the TWR-MCAE algorithm is jointly constructed by a singular value decomposition based data preprocessing module, an improved coordinate attention module, a compressed sensing learnable iterative shrinkage threshold reconstruction algorithm (LISTA) module, and an adaptive weight module. The data preprocessing module achieves wall clutter, human motion features, and noise subspaces separation. The improved coordinate attention module achieves clutter and noise suppression. The LISTA module achieves human motion feature enhancement. The adaptive weight module learns the weights and fuses the three subspaces. The TWR-MCAE can suppress the low rank characteristics of wall clutter, and enhance the sparsity characteristics in human motion at the same time. It can be linked before the classification step to improve the feature extraction capability without adding other prior knowledge or recollecting more data. Experiments show that the proposed algorithm gets a better peak signal-to-noise ratio (PSNR), which increases the recognition accuracy and speeds up the training process of the back-end classifers.

To better reconstruct underground targets based on ground-penetrating radar (GPR) data, this paper proposes a joint physics and data driven full-waveform inversion (PDD-FWI) scheme. This scheme combines a physics-based non-iterative approach and a data-driven deep neural network (DNN) to reconstruct target location, shape and permittivity accurately. Firstly, the normalized range migration algorithm (RMA) is introduced to extract the target contour and location information, which not only improves the robustness of the proposed scheme, but also ensures adaptability to different GPR equipment. Then, the GPR dielectric target reconstruction network (GPRDtrNet) is developed based on the improved U-net structure, including reducing network layers and adding multi-scale additive spatial attention gates and skip-connection structures. Compared with previous DNN-based reconstruction methods, GPRDtrNet has the advantages of small data requirement, high accuracy, strong generalization and noise tolerance. Finally, the simulated and real dataset containing kinds of targets is constructed to train and test GPRDtrNet. The results show that the proposed method can reconstruct underground dielectric targets accurately with high robustness and noise tolerance.

We develop a new tensor model for slow-time multiple-input multiple-output (MIMO) radar, and apply it for joint direction-of-departure (DOD), and direction-of-arrival (DOA) estimation. This tensor model aims to exploit the independence of phase modulation matrix, and receive array in the received signal for slow-time MIMO radar. Such tensor can be decomposed into two tensors of different ranks, one of which has identical structure to that of the conventional tensor model for MIMO radar, and the other contains all phase modulation values used in the transmit array. We then develop a modification of the alternating least squares algorithm to enable parallel factor decomposition of tensors with extra constants. The Vandermonde structure of the transmit, and receive steering matrices (if both arrays are uniform, and linear) is then utilized to obtain angle estimates from factor matrices. The multi-linear structure of the received signal is maintained to take advantage of tensor-based angle estimation algorithms, while the shortage of samples in Doppler domain for slow-time MIMO radar is mitigated. As a result, the joint DOD, and DOA estimation performance is improved as compared to existing angle estimation techniques for slow-time MIMO radar. Simulation results verify the effectiveness of the proposed method.

Zhangming Chan, Juntao Li, Xiaopeng Yang, Xiuying Chen, Wenpeng Hu, Dongyan Zhao, Rui Yan. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.

A novel high-accuracy phase-derived velocity measurement (PDVM) method for fast-moving space targets is presented in this letter. First, a wideband linear frequency-modulated signal model that considers the effect of radial acceleration was developed. To obtain the unambiguous phase difference between two adjacent echo pulses, coarse velocity and acceleration measurements derived from range profile cross correlation were used to resolve the phase ambiguity. Then, the derived accurate and unambiguous phase difference and the phase error induced by the discrete Fourier transform were analyzed. The PDVM technique was applied after the phase error was compensated for, and all required parameters were calculated. Under low signal-to-noise ratio (SNR) conditions, a correction for the phase unwrapping error was developed. The simulation results showed that the proposed PDVM technique was highly accurate. The root-mean-square error of the PDVM results was less than 0.025 m/s when the SNR was greater than 15 dB.

Penetrating radar systems are widely employed to scan the objects that are placed behind or buried inside mediums (such as walls, ground, and so on). As the clutter is much stronger than the target echo, clutter removal must be performed before imaging. The moving average subtraction, spatial notch filtering, and singular value decomposition methods are commonly used to remove clutter. However, the drawback is that these methods eliminate some of the target spectrum information, which causes target energy losses and generates side lobes. To solve this problem, two spectrum recovery methods are proposed in this paper. The first method recovers the spectrum magnitude and phase via sinc interpolation and linear fitting, respectively, which is fast and suitable for real-time processing. Although the second method recovers the spectrum based on matrix completion with prior information, which is more accurate and more computational expensive. Extensive simulations and experiments are presented to validate the proposed methods. The results show that the proposed methods can improve various traditional clutter removal methods, the side lobes are clearly suppressed, and the signal-to-clutter ratio is significantly improved.

Geosynchronous Synthetic Aperture Radar (GEO SAR) will be affected by ionosphere scintillation inevitably because it usually works at L band. In this paper, GEO SAR signal model in presence of ionospheric scintillation is proposed. The scintillation sampling model is employed to simulate ionospheric scintillation data. Several GEO SAR imaging simulations in different scintillation cases and a real ionospheric scintillation measurement in Zhuhai district of China are conducted to study the impacts of ionospheric scintillation on GEO SAR in azimuth. The results suggest that ionospheric scintillation will worsen the azimuth resolution, and rise azimuth peak sidelobe ratio (PSLR), and severely deteriorate azimuth integrated sidelobe ratio (ISLR).

Unknown Noise-MUSIC (UN-MUSIC) is a promising method of direction of arrival (DOA) estimation in unknown spatially correlated noise using sparse arrays composed of two widely separated sub-arrays. The conventional UN-MUSIC estimator only utilizes information from one calibrated sub-array. If two sub-arrays are calibrated, a joint estimator that equally weights two sub-arrays’ conventional estimators has been found in literature. But no theoretical study has been reported. To compare and improve performance of different UN-MUSIC estimators, this paper proposes a unified framework of sub-array weighting to investigate the UN-MUSIC estimators, including the conventional one and the joint ones. The closed-form expression of the sub-array weighting estimator’s variance is derived which has not been done before. With the asymptotic results, different weighting strategies are compared and optimal weighting strategy to minimize estimation variance is proposed. Numerical simulations demonstrate the theoretical analysis and the validity of optimal weighting estimator.

The clutter suppression of space-time adaptive processing (STAP) will degrade severely in forward-looking airborne radar if the range-dependence of clutter spectrum is not compensated correctly. In this paper, a new method for mitigating the range-dependence of the clutter spectrum based on the geometry-aided subspace projection is proposed. In the proposed method, the clutter covariance matrix of the range under test is firstly constructed based on the prior knowledge of antenna array configuration, and then the clutter subspace projection matrix is obtained by decomposing the corresponding covariance matrix. In the following, the mitigation of the range-dependence is accomplished by applying this clutter subspace projection matrix to transform the secondary range samples to obtain an approximate clutter subspace with the range under test. The simulation results show that the proposed method can mitigate the range-dependence significantly.