ABSTRACT Building interior structure sensing using through‐the‐wall radar (TWR) is crucial for safety and security applications. However, existing methods struggle to decouple first‐order wall reflections from second‐order corner interactions, resulting in ambiguity between wall discontinuities and corner features. To address these challenges, this paper proposes a scattering‐order‐based decoupling model integrating geometric optics (GO) and physical optics (PO) principles to enable accurate building characterisation. Our proposed model decomposes total radar returns into distinct components including first‐order scattering from walls and second‐order double‐bounce scattering from corners. For wall characterisation, the scattering response is derived via PO integrals, considering reflections from parallel and perpendicular orientations relative to the radar. For corner detection, a coupled GO‐PO model is developed to explicitly capture double‐bounce interactions between adjacent walls. This analytical formulation isolates the corner representation by excluding first‐order contributions, ensuring the corner signature consists exclusively of coupled scattering energy. As a result, this approach effectively suppresses first‐order wall scattering artifacts and reduces wall‐induced ambiguities. Based on these analytical derivations, we designed a physics‐based dictionary method to enable building interior structure extraction. Extensive simulations and experiments validate that our model improves wall and corner extraction, supporting more reliable building interior reconstruction.

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.

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.

Through-the-wall radar (TWR) leverages the excellent penetration capabilities of low-frequency electromagnetic waves to achieve noninvasive and nondestructive detection of targets hidden behind the wall. However, the refraction and attenuation phenomena of complex walls have significantly affected the detection performance and imaging quality. Most of the traditional studies have made compensation based on ideal uniform wall structures. They only consider a single medium or a single thickness, which will lead to huge errors for actual nonuniform wall structures. To address the challenges, a multifrequency fusion imaging algorithm that can handle urban nonuniform walls is proposed in this article. First, a segmented wall compensation algorithm is proposed for nonuniform wall scenarios to address poor focusing effects. Second, this article proposes a multifrequency fusion algorithm that integrates high-frequency data with low-frequency data to improve resolution in nonuniform wall environments. Finally, simulation and experimental results demonstrate the superiority of the proposed algorithm under nonuniform wall scenarios.

ABSTRACT Distributed array radar (DAR) expands the aperture of the array radar by adding multiple synchronized auxiliary arrays with the main array, thereby enhancing the ability to counter mainlobe jamming. However, the long baseline of DAR causes signal sources to fall into the near‐field region. The coupling of range and angle parameters in the near‐field signal model poses challenges to anti‐jamming methods based on jamming parameter estimation and cancelation. To address this issue, this paper proposes a mainlobe jamming suppression method for DAR based on variational sparse Bayesian learning (SBL) jamming estimation and range‐angle two‐dimensional null broadening beamforming. To decouple the range and angle parameters in the near‐field steering vector model, a variational grid optimisation nonuniform sparse recovery dictionary is designed. Afterwards, iterative‐optimised variational SBL using prior information is performed to estimate the range‐angle parameters of jammers accurately. Given potential estimation errors, two‐dimensional null broadening beamforming based on steering vector perturbation is proposed to suppress jamming. Simulation and experimental results verify that the proposed method can effectively reduce the computational complexity of sparse recovery, obtain more accurate jamming parameters, and achieve higher output signal‐to‐interference‐plus‐noise ratio (SINR) for jamming suppression.

Noncontact 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 article 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 multimodal 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 noncontact vital sign monitoring and facilitating practical deployment.

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.

Building layout reconstruction (BLR) with through-the-wall radar (TWR) mounted on an unmanned aerial vehicle (UAV) is challenged by clutter and the mismatch between fixed blocks and wall structures. This letter proposes a size-adaptive block-sparse method guided by mean information entropy (MIE). Candidate wall blocks are grown under range-resolution and MIE constraints, and then incorporated into a reweighted alternating direction method of multipliers (ADMM) reconstruction model. To our knowledge, this is the first work to formulate block partitioning for TWR-based BLR as an information-theoretic optimization problem. Simulations achieve the best peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and F1-score among compared methods, reaching 12.511 dB, 0.782, and 0.854, respectively. Field experiments using a UAV-borne linear frequency modulated continuous wave (LFMCW) TWR operating at 2.95 GHz with 500 MHz bandwidth further validate improved structural continuity and clutter suppression.

Terrorist attacks pose a severe threat to global public security, among which preembedding 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 nonintrusive detection method, is capable of effectively acquiring the 3-D information of buried objects. However, traditional GPR imaging methods often fail to complete the 3-D 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 article proposes a robust and lightweight 3-D reconstruction method for buried targets, specifically for pistols, eavesdropping devices, and explosives. The method first utilizes the 3-D Fourier transform of the wave equation to analyze the target wavefield information from 3-D C-scan data, which can obtain a preliminary visualization of the target, and then employs a projection mechanism to process the sparse energy-focused volume from multiple views. Finally, a multiview reconstruction algorithm is used to reconstruct the 3-D voxel model of the target. Compared to existing methods, our approach reduces parameters by 98.9% to 48.59M versus 3-D U-Net, floating-point operations (FLOPs) by 79.7% versus Kirchhoff migration, and achieves 0.575-s inference with a 0.829 Dice coefficient under optimal conditions. It maintains robust performance down to -5-dB SNR, with monotonic improvements in mean squared error (MSE), mean absolute error (MAE), and Dice as SNR increases. The experimental results indicate that the approach facilitates rapid and accurate retrieval of information on concealed dangerous objects in resource-limited settings.

ABSTRACT With the acceleration of urbanisation, the ability to detect target information in urban blind areas is urgently needed in many fields such as smart cities and autonomous driving. However, existing nonline‐of‐sight (NLOS) target localisation methods mainly assume that the urban building walls are flat with ideal reflections. This simplification results in significant localisation errors in real‐world scenarios, where wall surfaces include concave–convex features such as balconies and windows. To address this issue, we propose a joint estimation algorithm for target position and wall parameters based on the single‐input‐single‐output (SISO) radar system. First, we parameterise the concave–convex features of the walls at reflection points and incorporate them into the multipath echo model based on the sparsity of the scene. Then, an objective function is constructed by minimising the error between the echo signal and the signal reconstructed using compressed sensing. Furthermore, the alternating direction method of multipliers (ADMM) and genetic algorithms are designed for hierarchical optimisation. Finally, extensive simulations and experiments demonstrate that the proposed algorithm can achieve high‐precision NLOS target localisation in irregular wall surfaces.

Material identification based on intelligent sensing is a fundamental capability that enables next-generation IoT applications such as industrial automation, smart-home perception, and safety detection. Existing technologies often suffer from limited deployment flexibility, poor robustness when dealing with small objects, or insufficient exploitation of material physical properties. To address these issues, this paper proposes a non-contact material identification method based on millimeter-wave radar and micro-vibration sensing. By acoustically exciting micro-vibrations on the target and extracting micro-vibration features from the radar echoes, the method effectively distinguishes different material types. First, we design a same-side excitation detection architecture, in which the acoustic source and radar are placed on the same side of the target. This configuration enhances practical deployment flexibility and mitigates the issue that, under opposite-side setups, the weak echoes from small targets may be obscured by strong reflections from the speaker. Second, we extract three types of micro-vibration features from the radar echoes—frequency, power, and damping—each capturing distinct physical aspects of material properties. Furthermore, to effectively fuse these heterogeneous features, we develop a feature-aware neural network that employs branched modeling and attention mechanisms based on the structural characteristics of different features. The experiments are conducted on eight common material categories, each with multiple object instances and measured under various target-radar distances and observation angles. The results demonstrate that the proposed method achieves an average identification accuracy of 99.0%, providing a practical solution for non-contact material identification in IoT scenarios.

Unmanned aerial vehicle (UAV) mounted through-the-wall synthetic aperture radar (TTW-SAR) is critical for urban building detection, holding significant value in civilian and military applications. However, wall clutter is typically much stronger, severely degrading imaging quality. To address this issue, this paper proposes a joint dual-channel low-rank and sparse decomposition (JD-LRSD) method for wall clutter suppression based on UAV mounted TTW-SAR. In the proposed method, a joint dual-channel low-rank and sparse model is developed, with constraints of the dual-channel similarity and wall clutter distribution. Then, the alternating direction method of multipliers (ADMM) is employed to solve the developed optimization problem. Closed-form solutions of each subproblem are derived as explicit expressions. Finally, numerical simulations and field experiments are carried on a dual-channel UAV TTW-SAR system. The proposed method improves the signal to interference plus noise ratio (SINR), outperforming comparison methods by more than 9 dB.

Ground-penetrating radar (GPR) frequently encounters substantial clutter interference, which hinders the precise identification of concealed defects in tunnel linings, particularly in scenarios involving double-layered rebar and heterogeneous concrete. In this article, an efficient clutter suppression method is proposed to address this challenge. Initially, the method employs 2-D variational mode decomposition (2D-VMD) for data augmentation, thereby enhancing the representation of rebar-related clutter signals. Subsequently, the VIBWNet network is proposed to suppress clutter, enabling the restoration and enhancement of defect signals. Additionally, this study incorporates heterogeneous concrete models during dataset construction to ensure a closer match with real-world heterogeneous concrete conditions, thereby substantially enhancing the network's generalization capability when processing real-world measured data. Furthermore, evaluations on sandbox and concrete block experiments confirm that the method possesses strong generalization capability for measured data, demonstrating its effectiveness in suppressing complex clutter while accurately restoring defect signals. Finally, quantitative ablation studies and visual comparisons of network variants are conducted to elucidate the specific contributions of each component, while comparative analyses against recognized unsupervised baselines demonstrate the superiority of the proposed method.

Real-time bed-exit detection for the elderly or bedridden patients serves as a pivotal strategy in healthcare to mitigate accident risks and improve nursing standards. To address the challenges posed by covered stationary human targets and dynamic environmental interference, this paper proposes a novel non-contact detection method based on millimeter-wave (mmWave) radar. First, we introduce an adaptive clutter suppression mechanism. We fuse global energy with R´enyi entropy to filter out interference from fluttering light fabric. Second, to detect weak targets and filter out isolated noise clusters, we design a cascaded framework comprising ordered-statistic constant false alarm rate (OS-CFAR), adaptive morphological filtering and area thresholding. Finally, to suppress interference from fluttering heavy fabric, we propose a spatiotemporal morphological consistency assessment. Experimental validation based on 14.5 h of data collected from real-world scenarios demonstrates that the system achieves a sensitivity of 99.0%, a specificity of 98.2%, and an average detection delay of 6.9 s. These results confirm the reliability and robustness of the proposed method in handling complex scenarios, including blanket occlusion and interference from fluttering bedding and curtains.

Through-wall radar (TWR) plays a pivotal role in nonintrusive detection of buildings because of its excellent penetrability. Many methods have been developed to estimate the parameters of walls and perform wall compensation for accurate through-wall imaging results. However, most existing studies are based on uniform wall models that cannot be generalized to actual building walls with complex structures such as doors, pillars, and windows, nor can they quickly generate a parameter distribution map of the entire wall. To solve this problem, this article proposes an innovative segment-then-estimate framework that first fuses LiDAR point clouds and optical images with simultaneous localization and mapping (SLAM) to reconstruct a 3-D exterior wall model, thereby guiding the precise division of radar brightness scan (B-scan) data along the slow-time dimension. Then, we introduce a Transformer-based parameter estimation model for the echo data of each segment, leveraging its self-attention mechanism to capture long-range dependencies and deep features to accurately estimate the wall thickness and relative permittivity. Finally, segment-based estimates are stitched using geometric information obtained from multimodal reconstruction to generate the wall parameter distribution map. Experimental results demonstrate that the wall parameter estimation maps generated by the proposed framework in this study not only achieve highly accurate parameter estimation values but also exhibit distinct door and window boundaries, providing reliable prior information for highly refined compensated imaging.