Through-wall Imaging Research Articles

Through-Wall Imaging Using Low-Cost Frequency-Modulated Continuous Wave Radar Sensors

Through-Wall Imaging Using Low-Cost Frequency-Modulated Continuous Wave Radar Sensors

Seeing Through Walls with Wi-Fi: Wi Vi Technology and its Security Implications

he technology known as WI- FI, or wireless networking, allows users to connect to the internet wirelessly “Wi-Fi" sis the acronym for “WIRELESS FIDELITY. “Data is transferred between transmitters and receivers using Wi-Fi waves. WI-VI (WIRELESS VISION) is a new technology introduced by WI-FI. WIVI is a device that uses WI-FI signals to see through any object on the wall. In a closed room, we can detect the number of moving humans. In the future, we will concentrate on detecting more than three moving objects using WI-FI signals. WI- VI can detect people's gestures and relative location via the WI-FI signal. WIVI technology can be utilized in the tax department to detect black money and weapons, as well as for security purpose. Wivi is used for defense at home. Wivi technology can help us with door-face detection. Within a limited radius of no more than 1000 square feet, Wi-Fi is linked to Wi-Fi. Wi-Vi technology can also be used to assist in paramedic circumstances, like a land slide. Wi-Fi, a popular technology, is nothing more than an information channel between transmitter and receiver. We demonstrate in this work that Wi-Fi can also extend our senses, allowing us to view moving objects across walls and behind closed doors. So, can Wi-Fi signals be used to identify persons in a closed room based on their Relative location? Yes, we Through the use of a technology known as Wi-Vi, things in Confined rooms can be identified. Simple movements can also be identified and combined behind the wall to express messages. This introduces two major changes. Initially, MIMO interference nulling is used to reduce static object reflections and focus on moving objects. It then demonstrates how to follow a human by treating. Keyword- Wi-Fi Sensing, Through-Wall Imaging, MIMO Interference Nulling, Object Detection, Moving Object, wivi, Detection, Human Gesture Recognition, Indoor Localization

A Compact Ultrawideband Antipodal Vivaldi Antenna and Its Efficacy in Through-Wall Imaging

A Compact Ultrawideband Antipodal Vivaldi Antenna and Its Efficacy in Through-Wall Imaging

A novel wall parameter estimation approach based on transmission line method from through-wall radar measurements

A novel technique for estimation of the wall’s relative permittivity, thickness and conductivity from signals measured from the through-wall radar is presented in this paper. The retrieval process is built around an electromagnetic circuit model that represents the wall structure as a transmission line segment. The propagation of the radar signal through the wall is equivalently described as a pair of voltage and current waves that propagate along the transmission line, using analogies between the electrical parameters of the transmission line segment and the constituent parameters of the materials of the wall structure. The synthetic time domain signal obtained analytically and the real through wall imaging time domain signal were correlated to estimate the wall parameters of the layers that comprise the wall structure. The effectiveness of the proposed estimation technique has been demonstrated through simulation and experimentation on two actual building walls. A comparison with conventional methods revealed consistent results between the estimated wall parameters using the proposed technique and those obtained through traditional approaches. These findings highlight the potential of the proposed method in enhancing the accuracy of real-time target localization and tracking in through-wall imaging applications.

Ultra-wide band antipodal Vivaldi antenna design using target detection algorithm for detection application

This work presents a technique for detecting targets between two walls using ultra-wide band (UWB) modified antipodal Vivaldi antenna (MAVA). The detection system works on principle of time domain reflectrometry (TDR) using through wall imaging (TWI). This technique utilized a vector network analyzer (VNA) to produced short and small pluses to irradiate through an antenna array system onto the wall under study. The purposed detection system operated in UWB frequency spectrum (3.1 GHz to 10.6 GHz). Furthermore, an algorithm for hidden target detection has been developed. The results of the simulation of the designed antenna revealed a significant level of penetration, demonstrating a smart advancement in detecting and imaging system, to locate hidden metallic targets with good accuracy. A signal processing technique have been employed to improve the resolution of the target image. Using computer simulation technology (CST) software, the development and optimization process of an antenna is carried out, and the parametric performance of return loss, directivity and radiation pattern is evaluated.

Three-Dimensional Motion Compensation Method Based on Sparse Array Designed for Time-Division Multiplexing Multiple-Input-Multiple-Output Through-Wall Radar

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.

A Multi-Target Detection Method Based on Improved U-Net for UWB MIMO Through-Wall Radar

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.

Study on the application of multi-angle imaging related technology in the construction process

Abstract The mufti-angle imaging technique is proposed to address the wall position offset and the side flaps and gate flaps caused by the sparse array in the ultra-wide band through-wall radar building layout imaging, using the characteristic that the azimuth upward distance profile is similar around the main flap but different around the side flap/gate flap. Firstly, after the backward projection algorithm obtains the wall image of each viewpoint, the mufti-directional two-dimensional matching filter is used to enhance the image of each viewpoint and eliminate the effect of side flaps and raster flaps. Then the images of each viewpoint are decomposed by combining the multistage transform, i.e., the non-down sampling contour let transform, and the obtained low-frequency components and high-frequency components are fused using the corresponding fusion rules, respectively. Finally, the final fused building layout image is obtained by non-down sampling contour let inverse transform. The simulation experimental data show that the running time of the method in this paper is 10.99s. It shows that the proposed method enhances the outline and detail information of the building wall, and the final fused image obtained has prominent wall image, clearer edge contours, and good retention of overall detail information. The quality of image fusion is greatly improved than other methods, which paves the way for subsequent fusion methods including chatterers such as construction building corners and windows and doors.

Ripple phenomenon and artifact elimination in millimeter-wave imaging of a hidden target.

In millimeter-wave imaging of a hidden target, the effect of the dielectric cover before the target is typically ignored. This results in ripple-corrupted images that pose challenges for target recognition. In this paper, we provide a perspective for understanding the image of the hidden target, which clearly reveals the origin of the ripples, and propose a separation method that not only gets rid of ripples, but also obtains the target's depth map. Reflections and transmissions during imaging are considered and decoupled to separately form images corresponding to each real or virtual object. An algorithm based on the range-direction spread function is developed to iteratively estimate the depth and reflectivity of the target. Imaging experiments with and without a cover are conducted to demonstrate the formation and influence of ripples and to verify the proposed algorithm. Our work deepens the comprehension of covered target imaging. Benefited fields might include non-destructive testing, through-wall imaging, subsurface imaging, and security screening.

A compact quasi-Yagi antenna for FMCW radar-on-chip-based through-wall imaging

AbstractA compact quasi-Yagi antenna with a modified ground plane is designed for a through-wall radar on-chip. A slot-based ground plane modification in the proposed antenna results in significant miniaturization with an increase in the impedance bandwidth by 44.62%. The antenna has a high directivity of 9.02 dBi and a front-to-back ratio of 25.76 dB at 2.4 GHz. Based on experiments in real-world deployment scenarios, the performance of the proposed quasi-Yagi antenna is found to be comparable to that of a Vivaldi antenna and a commercial-off-the-shelf horn antenna. Spectrogram-based signatures of a moving person behind a wooden partition and a 40 cm thick masonry wall are successfully obtained using the designed antenna, demonstrating the suitability of the quasi-Yagi antenna for portable applications using a radar-on-chip.

Cluster Adaptive Matching Pursuit for Multipolarization Through-Wall Radar Imaging

Among various types of sensors, through-wall radar is a promising sensor that can penetrate obstacles and sense the targets behind them. In this article, by exploiting the common sparsity property of the multipolarization images, we propose a cluster adaptive matching pursuit (CAMP) algorithm for multipolarization through-wall radar imaging (TWRI). Our proposed CAMP algorithm is developed from compressive sensing (CS) theory. In the proposed CAMP algorithm, the common sparsity among the multipolarization images is utilized through a multiple-measurement vector (MMV) model. The target area is obtained through a support set. We model the support set as a combination of several clusters, thus the number of clusters and the pattern of each cluster are two important factors that can describe the support set. To obtain the pattern of a cluster that corresponds to a certain target in the imaging scene, we propose an adaptive clustering strategy based on the backtracking scheme. On this basis, the number of clusters is adaptively selected through a two-layer matching pursuit method. Different from conventional matching pursuit algorithms that often use atom methods, the support set is acquired cluster by cluster in the two-layer matching pursuit method, which accelerates the speed of acquiring the support set. Experimental results show that our proposed algorithm can locate targets accurately in TWRI with a high target-to-clutter ratio (TCR) performance.

An adaptive approach for the detection of contrast targets for the through-wall imaging

AbstractThrough-wall imaging is capable of detecting various living and non-living things behind the wall. The characteristics of the wall under the investigation, amount of clutter and noise govern the quality and reliability of the image as well as the detection ability of the targets using through the wall imaging system. The characteristics of the wall are not known prior, in the literature only the intensity profile is investigated for the unknown wall characteristics using a single dielectric target and the effect of the wall characteristics on the contrast imaging and impact on time or frequency domain features are not investigated. The target with less dielectric is having less reflectivity; hence its detection in the presence of a high reflective target and a noisy environment becomes difficult. In this paper, to enhance the detection ability of the imaging system attenuation constant (α) of the wall is estimated with the proposed wall parameter estimation methods and used as a normalizing factor. To achieve effective beamforming different weighting strategies are developed and the obtained images are compared with the traditional beamforming. Furthermore, a novel approach to finding the effective rank in the low-rank estimation using a statistical model and multi-objective genetic algorithm is proposed for de-noising.

Frequency Selective Computational Through Wall Imaging Using a Dynamically Reconfigurable Metasurface Aperture

A two-dimensional (2D) dynamically reconfigurable metasurface aperture is presented to perform frequency selective through wall imaging (TWI) with an unknown structure of the wall. Generally, in TWI, the medium properties and thickness of the wall need to be known in advance, which is not always possible. Moreover, compensating for these effects can significantly increase the computational complexity. We propose a two-stage method that leverages the concept of a dynamically reconfigurable metasurface antenna (DMA) in a narrow frequency band in which the effects of the wall are minimum to perform TWI. First, two simple probe antennas are used to evaluate the reflection response of the wall by means of a simple backscatter measurement. Based on these characteristics, a narrow band frequency selective window is identified. Second, a DMA consisting of an array of tunable metamaterial elements is used for TWI in the identified frequency selective window. The DMA aperture enables the scene information to be sampled through a set of spatio-temporally varying quasi-random modes using a single-channel transmit and receive architecture. This physical-layer compression scheme can significantly simplify the data acquisition while the quasi-random sampling of the scene information eliminates the need for conventional raster-scan based modalities.

Motion Compensation Method Based on MFDF of Moving Target for UWB MIMO Through-Wall Radar System

Ultrawideband (UWB) multiple-input-multiple-output (MIMO) radar is widely used for through-wall imaging (TWI) due to its excellent penetrability and large aperture. Multichannels in the MIMO radar system are usually time-division multiplexing based on microwave switches to reduce the complexity of the system in engineering. The switching process of the channel will bring time delay, which cannot be ignored in the TWI of the moving target. The switching time delay will cause the defocus and position shift of the TWI of the moving target. This letter proposes a motion compensation method based on multiframe data fusion (MFDF) used for correcting the echo of the through-wall moving target. A geometric model is established in the proposed method through the echo of the current frame and the next frame, and the compensated signal is obtained through the geometric solution. The proposed method is compared with before compensation and the traditional single-channel motion compensation algorithm (SCMCA) through simulation and experimental data verification. The visual images and quantitative results show that the proposed motion compensation method can obtain a good focus image of the through-wall moving target and reduce the positioning error.

Vivaldi Antenna Arrays Feed by Frequency-Independent Phase Shifter for High Directivity and Gain Used in Microwave Sensing and Communication Applications.

This paper describes a novel feed system for compact, wideband, high gain six-slot Vivaldi antenna arrays on a single substrate layer using a unique combination of power splitters based on binary T-junction power splitter topology, frequency-independent phase shifter, and a T-branch. The proposed antenna system consists of six Vivaldi antennas, three on the left, and three on the right arm. Each arm connects with T-junction power divider splitter topology, given that the right arm is linked through a frequency-independent phase shifter. Phase shifters ensure that the beam is symmetrical without splitting in a radiating plane so that highly directive radiation patterns occur. The optimal return losses (S-parameters) are well enriched by reforming Vivaldi’s feeding arms and optimizing Vivaldi slots and feeds. A novel feature of our design is that the antenna exhibits the arrangements of a T-junction power splitter with an out-of-phase feeding mechanism in one of the arms, followed by a T-branching feeding to even arrays of proper Vivaldi antenna arrangement contributing high realized gain and front-to-back ratio up to 14.12 dBi and 23.23 dB respectively applicable for not only ultra-wideband (UWB) application, also for sensing and position detecting. The high directivity over the entire UWB frequency band in both higher and lower frequency ranges ensures that the antenna can be used in microwave through-wall imaging along with resolution imaging for ground penetration radar (GPR) applications. The fabricated antenna parameters are in close agreement with the simulated and measured results and are deployed for the detection of targets inside the voids of the concrete brick.

SFCW Radar with an Integrated Static Target Echo Cancellation System.

Continuous Wave (CW) radars systems, especially air-coupled Ground-Penetrating Radar (GPR) or Through-Wall Imaging Radar (TWIR) systems, echo signals reflected from a stationary target with high energy, which may cause receiver saturation. Another effect caused by reflection of stationary targets is noticeable as background within a radargram. Nowadays, radar systems use automatic gain control to prevent receiver saturation. This paper proposes a method to remove stationary targets automatically from the received signal. The method was designed for a radar system with a moving platform, with an assumption that the distance between the surface and target is constant. The design is proposed of an SFCW radar with an integrated system for real-time multiple static target Echo Cancellation (EC). The proposed EC system removes the static target using active Integrated Circuit (IC) components, which generate the corresponding EC signal for each frequency step of the SFCW radar and sum it with the received echo signal. This has the main advantage of removing even multiple echoes at any distance, and excludes the need for a high-dynamic-range receiver. Additionally, the proposed system has minimal impact on the radar size and power consumption. Besides static target removal, the antenna coupling can be removed if the signal appears to be constant. The operating frequency was selected between 500 MHz and 2.5 GHz, due to the limitation of the used electronic components. The experimental results show that the simulated target’s echo using a cable with a known length could be suppressed to up to 38 dB. Experimental results using a moving radar platform and the real environment scenario with static and dynamic targets, show that the proposed EC system could achieve up to 20 dB attenuation of the static target. The system does not affect any other target of interest, which can even move at any distance during the measurement. Therefore, this could be a promising method for further compact implementation into SFCW radars, or any other radar type that generates CW single frequencies.

Continuous-capture microwave imaging

Light-in-flight sensing has emerged as a promising technique in image reconstruction applications at various wavelengths. We report a microwave imaging system that uses an array of transmitters and a single receiver operating in continuous transmit-receive mode. Captures take a few microseconds and the corresponding images cover a spatial range of tens of square meters with spatial resolution of 0.1 meter. The images are the result of a dot product between a reconstruction matrix and the captured signal with no prior knowledge of the scene. The reconstruction matrix uses an engineered electromagnetic field mask to create unique random time patterns at every point in the scene and correlates it with the captured signal to determine the corresponding voxel value. We report the operation of the system through simulations and experiment in a laboratory scene. We demonstrate through-wall real-time imaging, tracking, and observe second-order images from specular reflections.

Real time Adaptive Approach for Hidden Targets Shape Identification using through Wall Imaging System

In Through-wall Imaging (TWI) system, shape-based identification of the hidden target behind the wall made of any dielectric material like brick, cement, concrete, dry plywood, plastic and Teflon, etc. is one of the most challenging tasks. However, it is very important to understand that the performance of TWI systems is limited by the presence of clutter due to the wall and also transmitted frequency range. Therefore, the quality of obtained image is blurred and very difficult to identify the shape of targets. In the present paper, a shape-based image identification technique with the help of a neural network and curve-fitting approach is proposed to overcome the limitation of existing techniques. A real time experimental analysis of TWI has been carried out using the TWI radar system to collect and process the data, with and without targets. The collected data is trained by a neural network for shape identification of targets behind the wall in any orientation and then threshold by a curve-fitting method for smoothing the background. The neural network has been used to train the noisy data i.e. raw data and noise free data i.e. pre-processed data. The shape of hidden targets is identified by using the curve fitting method with the help of trained neural network data and real time data. The results obtained by the developed technique are promising for target identification at any orientation.

Variational Bayesian Compressive Multipolarization Indoor Radar Imaging

This article introduces a probabilistic Bayesian model for addressing the problem of compressive multipolarization through-wall radar imaging (TWRI). The proposed approach formulates the task of wall-clutter mitigation and multipolarization image reconstruction as a Bayesian inference problem for a joint distribution between observed radar measurements and latent wall-clutter matrix and indoor target images. The joint probability distribution incorporates three prior beliefs: low-dimensional structure of the wall reflections, group sparsity structure of the target images, and joint sparsity among the polarization images. These signal attributes are modeled through hierarchical priors, whose parameters and hyperparameters are treated with a full Bayesian formulation. Furthermore, this article presents a variational Bayesian inference algorithm that estimates wall-clutter and multipolarization images as posterior distributions and optimizes the model parameters and hyperparameters simultaneously. Experimental results on simulated and real radar data show that the proposed model is very effective at removing wall clutter and enhancing target localization even when the radar measurements are significantly reduced.

Mitigation of Through-Wall Distortions of Frontal Radar Images Using Denoising Autoencoders

Radar images of humans and other concealed objects are considerably distorted by attenuation, refraction, and multipath clutter in indoor through-wall environments. Although several methods have been proposed for removing target-independent static and dynamic clutter, there still remain considerable challenges in mitigating target-dependent clutter especially when the knowledge of the exact propagation characteristics or analytical framework is unavailable. In this article, we focus on mitigating wall effects using a machine learning-based solution—denoising autoencoders—that does not require prior information of the wall parameters or room geometry. Instead, the method relies on the availability of a large volume of training radar images gathered in through-wall conditions and the corresponding clean images captured in line-of-sight conditions. During the training phase, the autoencoder learns how to denoise the corrupted through-wall images in order to resemble the free space images. We have validated the performance of the proposed solution for both static and dynamic human subjects. The frontal radar images of static targets are obtained by processing wideband planar array measurement data with 2-D array and range processing. The frontal radar images of dynamic targets are simulated using narrowband planar array data processed with 2-D array and Doppler processing. In both simulation and measurement processes, we incorporate considerable diversity in the target and propagation conditions. Our experimental results, from both simulation and measurement data, show that the denoised images are considerably more similar to the free-space images when compared to the original through-wall images.