Through-wall Radar Research Articles

Clutter Removal Method for Through-Wall Radar Based on Weighted Bilinear Factorization Regularization

Clutter Removal Method for Through-Wall Radar Based on Weighted Bilinear Factorization Regularization

Overview of Vivaldi Antenna Selection for Through-Wall Radar Applications

This paper analyzes broadband antennas, with a special focus on Vivaldi antennas, for their suitability for through-wall radar applications. It assesses various antenna designs, emphasizing high gain, wide impedance bandwidth, and effective wall penetration capabilities. Vivaldi antennas are superior due to their broad bandwidth, high gain, and directional radiation patterns. This study further explores structural optimizations, feeding techniques, and performance enhancement strategies to refine Vivaldi antenna designs for through-wall radar systems. Through a comparative analysis and technical evaluation, this paper highlights Vivaldi antennas’ potential for improving through-wall radar systems’ imaging and sensing capabilities. This presents a pathway for future ultra-wideband advancements.

Innovative W-Band Through-Wall Radar With Sector Scanning: Utilizing Traveling Wave Tubes for Enhanced Penetration

Innovative <i>W</i>-Band Through-Wall Radar With Sector Scanning: Utilizing Traveling Wave Tubes for Enhanced Penetration

Indoor Target Localization Using Through-Wall Radar Based on the Time-Frequency Enhancement Algorithm

Indoor Target Localization Using Through-Wall Radar Based on the Time-Frequency Enhancement Algorithm

Data Fusion Network-Based Time–Frequency Enhancement Algorithm for Doppler Through-Wall Radar Tracking

Data Fusion Network-Based Time–Frequency Enhancement Algorithm for Doppler Through-Wall Radar Tracking

Target localization algorithm based on improved short-time Fourier transform

The properties of Doppler through-wall radar (TWR) in target localization applications mainly depend on the accuracy of instantaneous frequency (IF) estimations obtained from the time-frequency distribution (TFD) of target echo. However, when the time-frequency aliasing occurs in TFD, it is difficult to accurately estimate the target IF curves. To solve this problem, an improved short-Time Fourier transform is proposed in this paper. Firstly, a series of rough estimation time-frequency information (IF and chirp rate) of target echoes are obtained by fractional Fourier transform. Secondly, we update the time-frequency window according to the rough time-frequency information of the target signal. Meanwhile, we define a new distribution estimator based on the local maximum. Finally, the target IF curve is obtained by the local maximum operator as well as the updated window function. The experimental results show that the method significantly improves the target IF estimation and localization accuracy by 76.5 % and 74.6 %, respectively, compared with STFT.

Trajectory Deviation Estimation Method for UAV-Borne Through-Wall Radar

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

The survey of through-wall radar system

In contemporary times, optical cameras have garnered extensive usage, particularly in the realm of surveillance applications. Nevertheless, optical cameras possess certain limitations that hinder their ability to capture images efficiently in certain scenarios. For instance, they struggle to produce clear photographs in environments characterized by excessive brightness, darkness, or the use of flash. Additionally, the acquisition of high-definition portraits using optical cameras may raise ethical questions regarding potential violations of personal privacy. The detection of occluded objects by optical cameras poses a significant difficulty in the context of security applications. The topic of through-wall radar has gained significant interest due to its notable camouflage, as well as its advantageous features such as through-wall detection and resilience to optical causes. Researchers have successfully devised and developed several efficient through-wall radar systems that have shown to be helpful in several domains such as search and rescue operations, counter-terrorism efforts, medical applications, and urban warfare scenarios. This study provides an overview of prevalent through-wall radar technologies, conducts comparative evaluations, and explores the potential advancements and issues associated with this technology.

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.

RadarFormer: End-to-End Human Perception With Through-Wall Radar and Transformers.

For fine-grained human perception tasks such as pose estimation and activity recognition, radar-based sensors show advantages over optical cameras in low-visibility, privacy-aware, and wall-occlusive environments. Radar transmits radio frequency signals to irradiate the target of interest and store the target information in the echo signals. One common approach is to transform the echoes into radar images and extract the features with convolutional neural networks. This article introduces RadarFormer, the first method that introduces the self-attention (SA) mechanism to perform human perception tasks directly from radar echoes. It bypasses the imaging algorithm and realizes end-to-end signal processing. Specifically, we give constructive proof that processing radar echoes using the SA mechanism is at least as expressive as processing radar images using the convolutional layer. On this foundation, we design RadarFormer, which is a Transformer-like model to process radar signals. It benefits from the fast-/slow-time SA mechanism considering the physical characteristics of radar signals. RadarFormer extracts human representations from radar echoes and handles various downstream human perception tasks. The experimental results demonstrate that our method outperforms the state-of-the-art radar-based methods both in performance and computational cost and obtains accurate human perception results even in dark and occlusive environments.

Multicomponent WVD Spectrogram Enhancement Algorithm for Indoor Through-Wall Radar Target Tracking

Multicomponent WVD Spectrogram Enhancement Algorithm for Indoor Through-Wall Radar Target Tracking

A Multitask Network for People Counting, Motion Recognition, and Localization Using Through-Wall Radar.

Due to the outstanding penetrating detection performance of low-frequency electromagnetic waves, through-wall radar (TWR) has gained widespread applications in various fields, including public safety, counterterrorism operations, and disaster rescue. TWR is required to accomplish various tasks, such as people detection, people counting, and positioning in practical applications. However, most current research primarily focuses on one or two tasks. In this paper, we propose a multitask network that can simultaneously realize people counting, action recognition, and localization. We take the range-time-Doppler (RTD) spectra obtained from one-dimensional (1D) radar signals as datasets and convert the information related to the number, motion, and location of people into confidence matrices as labels. The convolutional layers and novel attention modules automatically extract deep features from the data and output the number, motion category, and localization results of people. We define the total loss function as the sum of individual task loss functions. Through the loss function, we transform the positioning problem into a multilabel classification problem, where a certain position in the distance confidence matrix represents a certain label. On the test set consisting of 10,032 samples from through-wall scenarios with a 24 cm thick brick wall, the accuracy of people counting can reach 96.94%, and the accuracy of motion recognition is 96.03%, with an average distance error of 0.12 m.

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.

Miniaturized Time-Domain Antenna With Improved Low-Frequency Radiation Performance

A miniaturized end-fire antenna with improved low-frequency radiation performance (RP) is proposed for time- domain applications. Through a novel resistive loading (RL) method, the proposed time-domain antenna (TDA) has a compact size of 0.176 λ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</sub> ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</i> ) × 0.156 λ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</sub> ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">W</i> ), and improved low-frequency RP. Note that, this improvement does not mean the improvement of radiation efficiency, but means the enhancement of the radiation in forward and the reduction of the radiation in backward. Furthermore, the size of the proposed TDA is reduced by 78% as compared with the traditional antipodal Vivaldi antenna (AVA). In order to provide a certain reference for the design of end-fire TDAs, the mechanism of the novel RL technique is explored according to the design. Measured results show that the bandwidth of the proposed TDA is 0.168 ∼ 3 GHz (179%). Due to the compact size and good time-domain performance, the proposed TDA can be a good candidate for time-domain applications such as ground penetrating radar (GPR), Through-Wall Radar (TWR) and high-power pulsed source (HPS). Meanwhile, the novel RL technique can be a prior solution for the miniaturization and low-frequency RP improvement for end-fire TDAs.

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.

Unsupervised Human Contour Extraction From Through-Wall Radar Images Using Dual UNet

Through-wall radar (TWR) can image the target of interest and capture the human sensing information. However, the poor human interpretability of TWR images and the lack of effective supervision make the extraction of complete body contour intractable. This letter proposes dual UNet, an unsupervised human contour extraction method for TWR images. Specifically, the method adopts two UNets with the same structure. One serves as the encoder to convert the TWR images into the latent representation. Another serves as the decoder to reconstruct the latent representation into the original images. Reconstruction loss and smooth normalized cut loss are optimized together to offset the dependence on labels and supplement global segment constraints. After training and post-processing, the latent representation can be used as the result of contour extraction. Experimental results show that dual UNet stands out among unsupervised human contour extraction methods in both free space and wall-occlusive scenarios, opening the possibility of learning useful human sensing information from raw TWR images without manual annotations.

Target Trajectory Estimation Algorithm Based on Time–Frequency Enhancement

Doppler through-wall radar (TWR) is one of the mainstream target positioning equipment, acquiring the target instantaneous frequency (IF) to achieve trajectory tracking. However, severe time–frequency interference leads to a significant deviation of the estimated trajectory from the actual position. Hence, in this work, we propose a target trajectory estimation algorithm based on time–frequency enhancement. First, the time–frequency analysis (TFA) of the echo signal is carried out by the short-time Fourier transform (STFT). By analyzing the time–frequency distribution (TFD) of the echo signal, an overcomplete time–frequency dictionary is obtained that can reflect the characteristics of the target. Then, the Wigner–Ville distribution (WVD) is employed to process the radar echo signal, and the predefined dictionary is used to suppress the time–frequency interference in the WVD to achieve time–frequency enhancement. Finally, the target IF curve is obtained from the enhanced TFD to synthesize the target motion trajectory. The experiment results show that the proposed method can suppress time–frequency interference and improve the estimation accuracy of IF values. Compared with the existing methods, the proposed method can effectively improve the accuracy of TWR localization.

Multi-Target Tracking Based on Ferguson-Hough Algorithm Against Frequency Ambiguity Issue

The Short-time Fourier transform (STFT) is widely used in the analysis of non-stationary signals because of its mature principle and simple implementation, as well as in the multi-target tracking based on Doppler Through-Wall Radar (TWR). However, due to the limitation of the uncertainty principle, when the frequencies of several echo signals are close to each other, there will be aliasing in the spectrum obtained by STFT, which will seriously degrade the accuracy of multi-target tracking. To overcome this limitation, we proposed an improved Ferguson-Hough algorithm, which combines the asymmetric Ferguson parameter model with the Hough transform and can fit the instantaneous frequency curve of the echo signal in the frequency ambiguous area, thus compensating for the loss of tracking accuracy. Experiments show that the method can effectively fit the frequencies in the frequency-ambiguous region, thus making the localization results approximate to the real data, and achieving better localization accuracy than other similar algorithms.

CNN-Based Time–Frequency Image Enhancement Algorithm for Target Tracking Using Doppler Through-Wall Radar

CNN-Based Time–Frequency Image Enhancement Algorithm for Target Tracking Using Doppler Through-Wall Radar

Backward Projection Imaging of Through-Wall Radar Based on Airspace Nonuniform Sampling

Backward Projection Imaging of Through-Wall Radar Based on Airspace Nonuniform Sampling