Through-the-wall Radar Imaging Research Articles

Through the Wall Radar Imaging via Kronecker-structured Huber-type RPCA

The detection of multiple targets in an enclosed scene, from its outside, is a challenging topic of research addressed by Through-the-Wall Radar Imaging (TWRI). Traditionally, TWRI methods operate in two steps: first the removal of wall clutter then followed by the recovery of targets positions. Recent approaches manage in parallel the processing of the wall and targets via low rank plus sparse matrix decomposition and obtain better performances. In this paper, we reformulate this precisely via a RPCA-type problem, where the sparse vector appears in a Kronecker product. We extend this approach by adding a robust distance with flexible structure to handle heterogeneous noise and outliers, which may appear in TWRI measurements. The resolution is achieved via the Alternating Direction Method of Multipliers (ADMM) and variable splitting to decouple the constraints. The removal of the front wall is achieved via a closed-form proximal evaluation and the recovery of targets is possible via a tailored Majorization–Minimization (MM) step. The analysis and validation of our method is carried out using Finite-Difference Time-Domain (FDTD) simulated data, which show the advantage of our method in detection performance over complex scenarios.

Effective path-loss compensation model based on multipath exploitation for through-the-wall radar imaging

Through-the-wall radar imaging (TWRI) has attracted a great deal of attention in several sensitive applications, including rescue missions and military operations. Notwithstanding its broad range of applications, TWRI suffers from path-loss because distant targets experience more attenuation of signal power than those closer to the transceiver. This challenge may lead to missed targets with important information necessary for analysis and informed decision making. Responding to the challenge, we have developed a signal model with an effective path-loss compensator incorporating a free space exponent. Furthermore, multipath exploitation and compressive sensing techniques were employed to develop an effective algorithm for isolating residual clutter that may corrupt real targets. The proposed signal model integrates contributions from the front wall, multipath returns, and path-loss. Compared with the state-of-the-art model under the same experimental conditions, simulation results show that the proposed model achieves improved signal-to-clutter ratio, relative clutter peak, and probability of detection by 13.1%, 17.4% and 33.6%, respectively, suggesting that our model can represent the scene more accurately.

High-Resolution Through-the-Wall Radar Imaging with Exploitation of Target Structure

It is quite challenging for through-the-wall radar imaging (TWRI) to achieve high-resolution ghost-free imaging with limited measurements in an indoor multipath scenario. In this paper, a novel high-resolution TWRI algorithm with the exploitation of the target clustered structure in a hierarchical Bayesian framework is proposed. More specifically, an extended spike-and-slab clustered prior is imposed to statistically encourage the cluster formations in both downrange and crossrange domains of the target region, and a generative model of the proposed approach is provided. Then, a Markov Chain Monte Carol (MCMC) sampler is used to implement the posterior inference. Compared to other state-of-the-art algorithms, the proposed nonparametric Bayesian algorithm can preserve underlying target clustered properties and effectively suppress these isolated spurious scatterers without any prior information on targets themselves, such as sizes, shapes, and numbers.

Aspect dependent-based ghost suppression for extended targets in through-the-wall radar imaging under compressive sensing framework

Several approaches have been proposed to suppress multipath ghost in through-the-wall radar imaging (TWRI). One classical approach, called Aspect Dependent (AD), exploits locations of ghosts in the images without demanding prior knowledge of the reflecting geometry. This operation strategy makes the method superior over multipath exploitation-based approaches. However, the AD method assumes a point target that emulates unreal environment. Therefore, reconstructing extended targets with this method leads to incorrect scene interpretation. This work proposes a ghost suppression method for extended targets based on the AD feature that exploits duo sub-apertures. Firstly, we evaluate the best suppression method using a performance metric called relative clutter peak. Next, the evaluated method is extended to encompass a target extent during sub-images reconstruction. Following this strategy, an effective image fusion method suitable for extended targets is proposed. The method considers pixel neighborhood to effectively recover the given extended target. Simulation results show that, when signal-to-noise ratio is 20dB, the proposed method significantly improves signal-to-clutter ratio and relative clutter peak by 8.8% and 23.8%, respectively, relative to the existing AD based methods under point target assumption.

Recovering Human Pose and Shape From Through-the-Wall Radar Images

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

Sensor Arrangement in Through-the Wall Radar Imaging

In this paper, the problem of how to spatially sample the scattered field in microwave through-the wall imaging is addressed. To this end, a two-dimensional scalar configuration for a three-layered back-ground medium is considered under a linearized scattering model. The aim is to collect as low as possible measurements by maintaining the same performance in the reconstructions. Accordingly, the number and the positions of the spatial measurement points are determined so that the point-spread function of the resulting semi-discrete problem approximates well the one of the ideal continuous case (i.e., when data space is not sampled at all). It is shown that the resulting measurement spatial positions must be non-uniformly arranged across the measurement domain and their number is generally much lower than the one returned by some literature sampling criteria. Also, the measurement points can be analytically determined by taking into account the geometrical parameters as well as the wall features. Numerical examples are included to check the theoretical arguments.

Millimeter-level resolution through-the-wall radar imaging enabled by an optically injected semiconductor laser.

Millimeter-level resolution through-the-wall radar (TWR) imaging is demonstrated using a broadband nonlinear frequency-modulated (NLFM) signal that is generated by an optically injected semiconductor laser. The proposed system uses period-one dynamics of a semiconductor laser, together with an optical frequency downconversion technique to generate NLFM signals, which addresses the problem of traditional period-one oscillation not being able to generate broadband signals in the low-frequency region. In the experiment, an NLFM signal having a broad bandwidth of 18.5 GHz (1.5-20 GHz) is generated with a corresponding radar range resolution of 8.1 mm. Using this signal, TWR imaging is demonstrated, in which the use of the NLFM signal achieves good side-lobe suppression during pulse compression, and a modified back projection imaging algorithm with sub-aperture weighting is proposed to improve the imaging quality.

Numerical and Experimental Comparison Among a New Hybrid FT-Music Technique and Existing Algorithms for Through-the-Wall Radar Imaging

A fast low-cost through-the-wall radar imaging (TWRI) system, based on a vector network analyzer (VNA), a couple of switches and an array of Vivaldi antennas, has been designed, realized, and tested. To solve the TWRI inversion problem, an original theoretical modeling for a class of TWRI techniques whose basic functions are the cross-range Fourier transform (FT) of the scattered field and its covariance operator has been proposed. Using these functions, four conventional algorithms, namely the delay and sum (DAS), the FT, the multiple signal classification (MUSIC), the hybrid DAS-MUSIC and a new algorithm, the hybrid FT-MUSIC, have been derived. All these techniques have been implemented and their accuracy and field of view have been tested on canonical scatterers. Then, the algorithms have been applied to measured data collected in different scenarios constituted by a metallic bar or a human subject in the absence and in the presence of a wall between the antenna and the considered targets. Using the proposed TWRI system, it has been possible to detect a subject located up to 5-m away from the radar antenna array through a tuff wall. The proposed FT-MUSIC algorithm has evidenced performances similar to those of the DAS-MUSIC but with significantly lower execution times. Finally, FT-MUSIC performances in terms of field of view and immunity to disturbances are better compared to those of the MUSIC algorithm.

Fast sparse image reconstruction method in through-the-wall radars using limited memory Broyden–Fletcher–Goldfarb–Shanno algorithm

AbstractCompressed sensing allows recovery of image signals using a portion of data – a technique that has drastically revolutionized the field of through-the-wall radar imaging (TWRI). This technique can be accomplished through nonlinear methods, including convex programming and greedy iterative algorithms. However, such (nonlinear) methods increase the computational cost at the sensing and reconstruction stages, thus limiting the application of TWRI in delicate practical tasks (e.g. military operations and rescue missions) that demand fast response times. Motivated by this limitation, the current work introduces the use of a numerical optimization algorithm, called Limited Memory Broyden–Fletcher–Goldfarb–Shanno (LBFGS), to the TWRI framework to lower image reconstruction time. LBFGS, a well-known Quasi-Newton algorithm, has traditionally been applied to solve large scale optimization problems. Despite its potential applications, this algorithm has not been extensively applied in TWRI. Therefore, guided by LBFGS and using the Euclidean norm, we employed the regularized least square method to solve the cost function of the TWRI problem. Simulation results show that our method reduces the computational time by 87% relative to the classical method, even under situations of increased number of targets or large data volume. Moreover, the results show that the proposed method remains robust when applied to noisy environment.

Human Posture Reconstruction for Through-the-Wall Radar Imaging Using Convolutional Neural Networks

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

3-D Through-the-Wall Radar Imaging Using Compressed Sensing

The compressed sensing (CS) technique is applied to the diffraction tomography (DT) method for through-the-wall radar imaging (TWRI), which leads to significantly decreasing the number of measurements and, consequently, reducing the data acquisition time. A large amount of required measurements is a real difficulty in TWRI, which is addressed in our work, due to the CS theory. In addition, the nonuniform fast Fourier transform (NUFFT) is also employed in the DT algorithm to accelerate the image reconstruction processing time. By exploiting the advantages of both the CS and NUFFT, the imaging speed is noticeably increased. As a proof of concept, in a sample scenario, our experiments show that, by using only 10% of the measurements used in the conventional DT algorithm, the imaging time is reduced by around 1/4 (with respect to the conventional DT method) while maintaining the quality of the reconstructed images.

Toward Moving Target Detection in Through-the-Wall Radar Imaging

With the advances in radar technology, through-the-wall radar imaging (TWRI) has become a viable sensing modality that can allow fire-and-rescue personnel, police, and military forces to detect, localize, and identify targets behind opaque obstacles. Many of the existing TWRI approaches detect either stationary or moving targets but not both of them simultaneously. In this article, a method is proposed to detect both stationary and moving targets from a sequence of radar signals. The proposed method decomposes the 3-D radar data, i.e., frequency, space, and time data into a low-rank tensor and two sets of sparse images. One set of images comprises the stationary targets, and the other set of images contains the moving targets. Wall clutter removal and target detection are formulated into an optimization problem regularized by tensor low-rank, joint sparsity, and total variation constraints. Then, an alternating direction technique is developed to reconstruct the sets of stationary and moving target images. Experiments using simulated and real radar signals are conducted. The experimental results illustrate the effectiveness of the proposed method to detect and separate the stationary and moving targets into a pair of sparse images.

Performance Evaluation of Aspect Dependent-Based Ghost Suppression Methods for Through-the-Wall Radar Imaging

There are many approaches which address multipath ghost challenges in Through-the-Wall Radar Imaging (TWRI) under Compressive Sensing (CS) framework. One of the approaches, which exploits ghosts’ locations in the images, termed as Aspect Dependent (AD), does not require prior knowledge of the reflecting geometry making it superior over multipath exploitation based approaches. However, which method is superior within the AD based category is still unknown. Therefore, their performance comparison becomes inevitable, and hence this paper presents their performance evaluation in view of target reconstruction. At first, the methods were grouped based on how the subarrays were applied: multiple subarray, hybrid subarray and sparse array. The methods were fairly evaluated on varying noise level, data volume and the number of targets in the scene. Simulation results show that, when applied in a noisy environment, the hybrid subarray-based approaches were robust than the multiple subarray and sparse array. At 15 dB signal-to-noise ratio, the hybrid subarray exhibited signal to clutter ratio of 3.9 dB and 4.5 dB above the multiple subarray and sparse array, respectively. When high data volumes or in the case of multiple targets, multiple subarrays with duo subarrays became the best candidates.
 Keywords: Aspect dependent; compressive sensing; point target; through-wall-radar imaging;

Sparse Blind Deconvolution Method for Wall Parameters Estimation

The information of wall parameters is very important for the imaging performance of through-the-wall radar imaging (TWRI). The existing estimation methods usually require the calibration procedure to ensure the accuracy of the estimated wall parameters. To avoid the time-consuming calibration procedure, a sparse blind deconvolution method for wall parameters estimation is proposed in this letter. The proposed method uses the time-delay-only estimation (TDOE) strategy with the hybrid bistatic–monostatic measurement configuration to retrieve the unknown thickness, relative permittivity, and conductivity of the wall. In the estimation process, the sparse blind deconvolution algorithm is developed to retrieve the time delays of echoes reflected from the wall. The proposed sparse blind deconvolution method can precisely extract the time delays of echoes in the time domain without the calibration procedure to obtain the shape of the transmitted pulse and the frequency response of the antennas. The estimation results from the full-wave synthetic data have shown that the proposed method can also improve the estimation accuracy of the wall parameters.

Multiple Moving-Target Indication for Urban Sensing Using Change Detection-Based Compressive Sensing

Moving-target indication (MTI) has been widely researched in urban sensing. The existing research work mainly concentrates on a single moving target, and there exist difficulties to research multiple moving targets, such as estimating the number of targets and distinguishing the targets that are close to each other. In this letter, based on compressive sensing (CS), we consider the indication of multiple moving targets in through-the-wall radar imaging (TWRI). Using the method of change detection, stationary targets and clutter are removed. Then, according to the reasonable state of motion, we delete the ghost points and combine the tracing points of each moving target. Comparison with the existing CS-based MTI method shows that with the proposed method, ghosts can be suppressed for about 60% at most, which can better estimate the number of moving targets and show the indication of each moving target.

Through-the-Wall Target Separation Using Low-Rank and Variational Mode Decomposition

Through-the-wall radar imaging (TWRI) is a sensing technology that can be used for detecting, locating, and identifying targets inside enclosed building structures. Many of the existing target classification approaches focus on single-target scene. For multi-target classification, the radar signal has to be segregated into different target components. However, target separation in TWRI is a challenging problem since the radar signals consist of both strong wall reflections and weak target echoes. Furthermore, the target signals are attenuated and distorted when propagating through the wall. In this paper, a variational model with low-rank constraint is proposed for decomposing the radar signal into target components and removing the wall returns. Experimental results show that the proposed method can effectively separate the radar signal into different target components.

Cross Validation Based Distributed Greedy Sparse Recovery for Multiview Through-the-Wall Radar Imaging

Multiview through-the-wall radar imaging (TWRI) can improve the imaging quality and target detection by exploiting the measurement data acquired from various views. Based on the established joint sparsity signal model for multiview TWRI, a cross validation (CV) based distributed greedy sparse recovery algorithm which combines the strengths of the CV technique and censored simultaneous orthogonal matching pursuit algorithm (CSOMP) is proposed in this paper. The developed imaging algorithm named by CV-CSOMP which separates the total measurements into reconstruction measurements and CV measurements is able to achieve the accurate imaging reconstruction and estimation of recovery error tolerance by the iterative CSOMP calculation. The proposed CV-CSOMP imaging algorithm not only can reduce the communication costs among radar units, but also can provide the desirable imaging performance without the prior information such as the sparsity or noise level. The experimental results have verified the validity and effectiveness of the proposed imaging algorithm.

Dynamic Joint Reconstruction of Walls and Targets in Through-the-Wall Radar Imaging

Multipath exploitation with compressive sensing (CS) has been successfully applied in through-the-wall radar imaging (TWRI) given prior knowledge of the room geometry. However, in most practical applications and critical missions, the geometry of the room is not known. In this paper dynamic wall pursuit algorithm is proposed to simultaneously recover the room geometry and image the scene behind the wall. We demonstrate that moving targets increase the wall reconstruction accuracy. The algorithm exploits the fact that wrong wall positions populate the reconstructed image with false targets. It makes use of the moving targets in the scene to increase the certainty of the detected wall positions. Simulated results show the effectiveness of this method even at low SNR values.

RPM and Doppler‐based multi‐path exploitation method for UWB TWR

Through-the-wall radar (TWR) with ultra-wideband (UWB) signals is a promising technology for three-dimensional monitoring systems used for rescue and security applications. To develop a suitable imaging algorithm to deal with UWB radar data, the range points migration (RPM) method was enhanced for the TWR observation model. However, there are some problems in the actual application because multiple reflection signals from the walls or targets produce a ghost image. This research extends the RPM-based TWR imaging method to convert a ghost image to an accurate one by exploiting the double-bounced signals discriminated by the Doppler velocity. Such discrimination can be achieved by prior estimations for the motion and location of the target provided by the Doppler-associated RPM algorithm. The finite-difference time-domain-based numerical simulation, with the assumption of multiple surrounding walls, demonstrated that the authors proposed method produced an accurate object by converting a ghost image to an actual image.

Comparison Between Delay and Sum and Range Migration Algorithms for Image Reconstruction in Through-the-Wall Radar Imaging Systems

Through-the-wall radar imaging (TWRI) systems allow police, fire personnel, and defense forces to detect, identify, and track subjects inside buildings or under rubble. In this paper, the delay and sum (DAS) and range migration (RM) algorithms are compared as imaging techniques for a multiple input multiple output (MIMO) stepped-frequency (SF) radar system. These algorithms have been applied to analytical, simulated, and measured data both in the absence and in the presence of a wall between the antenna and the target. Both techniques were able to accurately reconstruct the position of targets behind a wall. The DAS presents a wider angle of nonambiguity while the RM is faster. An improvement of the DAS, in terms of accuracy in the target positioning, is achieved applying the Fermat's principle.