Through-the-wall Radar Research Articles

Iteration-Based Temporal Subgridding Method for the Finite-Difference Time-Domain Algorithm

A novel temporal subgridding technique is proposed for the finite-difference time-domain (FDTD) method to solve two-dimensional Maxwell’s equations of electrodynamics in the TEz mode. Based on the subgridding FDTD algorithm with a separated spatial and temporal interface, our method focuses on the temporal subgridding region, as it is the main source of late-time instability. Different from other subgridding algorithms that work on the interpolation between coarse and fine meshes, our method stabilizes the solution by using iterative updating equations on the temporal coarse–fine mesh interface. This new method presents an alternative approach aimed at improving the stability of the subgridding technique without modifying the interpolation formulas. We numerically study the stability of the proposed algorithm via eigenvalue tests and by performing long-term simulations. We employ a refinement ratio of 2:1 in our study. Our findings indicate the stability of the conventional temporal subgridding FDTD algorithm with a magnetic field (Hz) interpolation. However, when electric fields (Ex and Ey) are utilized in interpolation, late-time instability occurs. In contrast, the proposed iteration-based method with an electric field interpolation appears to be stable. We further employ our method as the forward problem solver in the Through-the-Wall Radar (TWR) imaging application.

Simultaneous Estimation of Wall and Object Parameters in TWR Using Deep Neural Network

The purpose of this paper is to present a deep learning model that simultaneously estimates targets and wall parameters in through-the-wall radar (TWR). As a result of the complexity of the environments in which through-the-wall radars operate, TWR faces many challenges. The propagation of radar signals through walls is further delayed and attenuated than in free space. Therefore, the targets are less able to be detected and the images of the targets are distorted and defocused as a consequence. To address the above challenges, two modes are considered in this work: single targets and two targets. In both cases, permittivity and wall thickness are considered, along with the target’s center in two dimensions and the permittivity of targets. Therefore, in the case of a single target, we estimate five values, whereas in the case of two targets, we estimate eight values simultaneously, each representing the mentioned parameters. As a result of using deep neural networks to solve the task of target locating problem in TWR, the model has a better chance of learning and increased accuracy if it involves more parameters (such as wall parameters and permittivity of the wall) in the target location problem. In this way, the accuracy of target locating improved when two wall parameters were considered in problem. A deep neural network model was used to estimate wall permittivity and thickness, as well as two-dimensional coordinates and permittivity of targets with 99% accuracy in single-target and two-target modes.

TWR-MCAE: A Data Augmentation Method for Through-the-Wall Radar Human Motion Recognition

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 multilink auto-encoding neural network (TWR-MCAE) data augmentation method. Specifically, the TWR-MCAE algorithm is jointly constructed by a singular value decomposition (SVD)-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 classifiers.

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.

Multi-View Real-Time Human Motion Recognition Based on Ensemble Learning

This paper deals with the real-time recognition from multiple spatial angles of concealed human motions with ultra wide band (UWB) through-the-wall radar (TWR). To conquer the performance loss incurred by diverse human motion in a single view, a multi-view real-time human motion recognition model based on ensemble learning is proposed. Specifically, we first proposes a multi-view human motion recognition model based on Stacking parallel ensemble learning algorithm, which is used to realize the real-time human motion recognition based on UWB TWR. Secondly, in order to address the irrationality of the existing accuracy evaluation criteria to evaluate the real-time motion recognition algorithm, a real-time model evaluation criterion based on normalized harmonic weighted intersection over union (NHW-IOU) is proposed. Finally, the collected multi-view human motion data are used to verify the effectiveness of the proposed algorithm. The actual measurement results show that the average recognition performance of the proposed Stacking model has improved by 14.76% compared with the single-view model, which is of great significance for using multi-view data to improve network performance. Moreover, compared with the bi-directional long short-term memory (Bi-LSTM) and Gated Recurrent Unit (GRU) models, the proposed model has better performance in accuracy and time delay.

Front-Wall Clutter Removal in Through-the-Wall Radar Based on Weighted Nuclear Norm Minimization

The front-wall clutter removal in the case of the through-the-wall radar (TWR) system is studied in this work. To remove the wall clutter, its low-rank property is utilized, and at the same time, the sparse property of the target returns is exploited to perform target reconstruction. To account for the unparalleled setting of the antenna and the wall, a weighted nuclear norm minimization (WNNM) is employed, and the resulting problem is solved in an alternating manner. In addition, different transmitted waveform signals, including monofrequency and stepped-frequency waveforms, are used to demonstrate their effects on the clutter suppression performances. The experimental results show that the proposed WNNM with stepped-frequency waveform outperforms other approaches.

Human Motion Serialization Recognition With Through-the-Wall Radar

Motivated by the intrinsic dynamics of physical motion as well as establishment of target motion model, this article addresses the problem of human motion recognition with ultra wide band (UWB) through-the-wall radar (TWR) in a novel view of range profile serialization. Specifically, we first convert the original radar echoes into range profiles. Then, an auto-encoder network (AEN) with three dense layers is adopted to reduce the dimension and extract the features of each range profile. After that, a gated recurrent unit (GRU) network with two hidden layers is employed to deal with the features of each time-range slice and output the recognition results at each slice in real time. Finally, experimental data with respect to four different behind-wall human motions is collected by self-developed UWB TWR to validate the effectiveness of the proposed model. The results show that the proposed model can validly recognize the human motion serialization and achieve 93% recognition accuracy within the initial 20% duration of the activities (the average durations are 4s, 5.5s, 3s and 4.5s), which is of great significance for real-time human motion recognition.

Development of Optimal Thresholding Technique for Shape and Size Detection for Through the wall Radar Imaging System

Through-the-wall radar (TWR) images of stationary target behind a wall are subject to strong stationary and non-stationary clutter which obscures the target position and size in the image. Stationary clutters are present due to strong reflection from the wall and non-stationary clutter occurs due to multipath, noise, etc. A lot of work has been reported for mitigating stationary clutter successfully for various real scenarios. However, for mitigating nonstationary clutter various authors have reported their work in this field but any concrete result has not been reported so far. Hence, there is a need for an optimal methodology to mitigate non-stationary clutter in TWR images for achieving a high-quality image representing the target position, shape and its size. Till now it is difficult to achieve shape detection of the target from the TWR system. Therefore, in this paper, a novel optimal thresholding technique is proposed to mitigating non-stationary clutter for further enhancement on shape detection of a target using curve fitting and genetic algorithm. The proposed methodology gives a satisfactory result and can prove to be a powerful technique for minimising clutter.

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.

A Sparse Bayesian Learning Approach for Through-Wall Radar Imaging of Stationary Targets

Through-the-wall radar (TWR) imaging is an emerging technology that enables detection and localization of targets behind walls. In practical operations, TWR sensing faces several technical difficulties including strong wall clutter and missing data measurements. This paper proposes a sparse Bayesian learning (SBL) approach for wall-clutter mitigation and scene reconstruction from compressed data measurements. In the proposed approach, SBL is used to model both the intraantenna signal sparsity and interantenna signal correlation for estimating the antenna signals jointly. Here, the Bayesian framework provides a learning paradigm for sharing measurements among spatial positions, leading to accurate and stable antenna signal estimation. Furthermore, the task of wall-clutter mitigation is formulated as a probabilistic inference problem, where the wall-clutter subspace and its dimension are learned automatically using the mechanism of automatic relevant determination. Automatic discrimination between targets and clutter allows an effective target image formation, which is performed using Bayesian approximation. Experimental results with both real and simulated TWR data demonstrate the effectiveness of the SBL approach in indoor target detection and localization.

DESIGN AND PROTOTYPE OF A COMPACT, ULTRA WIDE BAND DOUBLE RIDGED HORN ANTENNA FOR BEHIND OBSTACLE RADAR APPLICATIONS

In this paper, we propose an ultrawide band design for the double ridged horn (DRH) antenna to be used behind the obstacle radar (BOR) applications such as ground penetrating radar (GPR) and through the wall radar (TWR) imaging. The design is developed and optimized by the help of full electromagnetic simulator code; CST. The design parameters such as frequencies of operation and the half power beam width (HPBW) are taken into account by considering the BOR application requirements. The design double ridged horn antenna provides a frequency bandwidth between 1.5 GHz and 7 GHz and HPBW around 30° around the center frequency that are very suitable for GPR and TWR applications. The final optimized design that is formed by CST is physically manufactured and measured. The prototyped DRH antenna’s measurement antenna parameter results are in good agreement with the simulated ones.

Design and prototype of radar sensor with Vivaldi linear array for through-wall radar imaging: an experimental study

We present a radar sensor that was designed to detect and image moving objects/targets on the other side of a wall. The radar sensor was composed of a linear array of Vivaldi antenna elements, an radio frequency (RF) switch, a microcontroller unit, and an RF transceiver. For the linear array, a total of eight antenna elements were used as sensors in synthetic aperture radar (SAR) configuration in the cross-range axis to improve the resolution in this dimension. Design steps of Vivaldi antenna elements and the entire linear array were presented. After the design, the prototyping procedure and the details of the radar sensor were given. Through-the-wall radar (TWR) imaging experiments were performed for stationary and moving targets using the assembled sensor. The resultant TWR images after these experiments were presented. During the image formation, a back-projection type image focusing algorithm was implemented and applied to increase the signal-to-noise ratio of the raw images. The constructed radar images demonstrated that our radar sensor could successfully detect and image both stationary and moving targets on the other side of the wall.