Compressive sensing (CS) technology has proven highly effective in rapid data acquisition and super-resolution target imaging for through-the-wall radar imaging (TWRI) applications. However, most existing CS-based TWRI techniques focus primarily on high-quality imaging of behind-wall targets, often neglecting the reconstruction of building layouts, which is essential for determining the relative positions of targets in unknown environments. To address this limitation, a structural-prior-guided CS framework is proposed for the joint reconstruction of building layouts and behind-wall targets. Specifically, first, distinct imaging models are developed for layouts and targets, accounting for their unique structural properties: layouts, referring to wall structures, typically manifest as extended, piecewise-continuous line-like structures, whereas targets manifest as compact, point-like structures. Building on these models, a unified constrained optimization problem is formulated by integrating (i) the strong inter-channel correlation of layout echoes, enforced via a low-rank regularization on the layout component, and (ii) structured sparsity priors tailored to both the layout and target images. Then, the resulting composite problem is efficiently solved using proximal gradient algorithm, yielding simultaneous reconstruction of the unknown building layouts and behind-wall targets. Finally, simulations and experimental results demonstrate the effectiveness of the proposed algorithm.

This paper considers the problem of line of sight (LOS) and non-line-of-sight (NLOS) targets localization in an L-shaped corner. Specifically, first, the propagation paths of the electromagnetic waves in an L-shaped corner are analyzed by the ray tracing technology. Then, a sparsity-based multipath model is formulated by regularizing the LOS target with sparsity norm and the NLOS targets with jointly sparse across different paths, respectively. After that, a proximal gradient-based iterative algorithm is proposed to tackle this optimization problem. Finally, the feasibility of simultaneously localizing NLOS and LOS targets is demonstrated through simulations.

This paper considers the building layout reconstruction (BLR) problem based on compressive sensing (CS) framework in the impulsive noise environment. Specifically, first, a CS-based imaging model considering the extended characteristics of walls is established. To realize effective BLR under impulsive noise, we formulate an optimization problem integrated maximum complex correntropy criterion (MCCC) and sparsity constraint. Then, a proximal-gradient-based iterative algorithm is introduced to solve the optimization problem. Finally, simulations under Gaussian noise and impulsive noise validate the effectiveness of the proposed method.

In this paper, we consider the problem of multipath suppression for through-the-wall radar imaging (TWRI). Exploiting the spatial diversity characteristic of multiple-input-multiple-output (MIMO) radar, we present a novel framework integrated compressive sensing and spatial filtering to formulate a ghost-free image with high-resolution. Specifically, an array signal processing model that accounts for the direction of departure (DoD), direction of arrival (DoA) and time of arrival (ToA) is established, where the signal with unequal DoD and DoA is regarded as the multipath. For the purpose of computational efficiency, we present a two-step method to solve the proposed large-scale problem, where the alternating direction method of multiplier (ADMM) and Capon beamforming are employed. Finally, the performance of the proposed method is verified via simulations.

In the field of through-the-wall radar imaging , existing compressive sensing (CS) methods mainly concentrate on deriving indoor targets image while overlooking the reconstruction of building layout image. In this paper, we focus on the problem of utilizing CS for building layout reconstruction (BLR) in wall reverberation environment. Specifically, first, by incorporating the characteristics of building layout, an extended target CS imaging model in wall reverberation environment is established. Then, an extended-target-based group block CS (ET-GBCS) algorithm based on the alternating direction multiplier method is proposed to accurately reconstruct the building layout. After obtaining the reconstructed result of each view, the total variation minimization method is used to process the multi-view fusion result for building layout edge preservation and image noise removal. Finally, the effectiveness of the proposed algorithm is verified by electromagnetic simulations.

The pursuit of high-quality building layout images is a key objective in radio tomographic imaging (RTI) as it provides essential information for precise indoor target localization. This study addresses the challenge of tomographic imaging for three-dimensional (3D) building layout, introducing a tensor-based enhancement imaging method. Specifically, first, the linear tomographic model is built by considering the relationship between the time delay of the transmissive signal and unknown region. By solving the tomographic model, the initial spatial map can be derived, and it is characterized as a three-order tensor, encapsulating the spatial attributes of the building. In the proposed enhanced imaging method, it leverages the spatial correlations, smoothness, and adaptive group sparsity properties inherent in 3D building layouts, and embeds those prior knowledge into the tensor-based optimization framework, which not only enhances reconstruction accuracy but also suppresses the striping artifacts. Numerical simulations and experiments are conducted to validate the proposed algorithm, with comparisons made against state-of-the-art methods. The results consistently demonstrate a substantial improvement in the quality of building layout image, which underscores its high potential and applicability within the field of radio tomography.

Building layout tomographic imaging (BLTI) is a nascent technology that effectively addresses issues, such as wall cavities and offsets, encountered in traditional synthetic aperture radar (SAR) imaging. Nevertheless, the current single-input single-output (SISO) imaging system is susceptible to multipath signals (MPSs) interference, leading to issues, such as artifacts and distortions in the imaging results. In this article, a tomographic scanning system based on multiple-input multiple-output (MIMO) ultrawideband (UWB) radar is designed, which can accurately acquire the delay of direct-path signals (DPSs) delay by the similarities of DPS across different channels. Next, a linear tomographic projection model is established according to the relationship between DPS delay and the scene to be imaged. Furthermore, a joint sparse imaging algorithm with adaptive projection matrix modification (JS-APMM) is proposed. It takes into consideration the prior information of building layout and material to improve imaging quality under limited data. Finally, simulation and experimental results demonstrate the superiority of the proposed method over the single-input single-output (SISO) imaging system and state-of-the-art compressed sensing algorithms.

Building layout reconstruction (BLR) is a prominent research topic in the field of through-the-wall radar (TWR) and wireless perception. Inspired by computed tomography (CT), the transmit–receive separated dual-bistatic radar system utilizes electromagnetic (EM) wave transmission signals to perform BLR. However, existing researches significantly rely on signal frequency bandwidth resources. Moreover, state-of-the-art researches only estimate the time delay (TD) information, thereby posing challenges in precisely discriminating between the direct path (DP) and multipaths. This article refines the sparse signal reconstruction-based angle of arrival (AOA) and TD super-resolution estimate algorithms under the condition of restricted broadband array signals. In accordance with this, the present study proposes a DP-identifying criterion with the assistance of AOA and obtains high-accuracy DPTD estimation. Furthermore, with the high-accuracy DPTD estimation, this article proposes a common material permittivities-based iterative multimaterial BLR algorithm. The final numerical simulations and EM simulations verify the effectiveness of the proposed super-resolution algorithm and the improvement of multimaterial BLR.

Localization of non-line-of-sight (NLOS) targets in the complex urban environment have attracted significant attention in recent years. However, the requirement for precise prior information about the environment is idealistic. It is challenging to know the environmental information in the blind area of vision in advance of practical applications. This paper proposes a joint estimation algorithm for building layout and target position in the L-shaped scene without any prior information. Specifically, a round-trip multipath propagation model is first developed for the cases of diffraction and multiple reflections. Then, the received echo signal is preprocessed with moving target identification (MTI), back-projection (BP) imaging, and image segmentation. In addition, the target points, which are screened by geometric association, are further matched and estimated by the multipath ghost’s hypothesis method, thus realizing the joint perceptual estimation of the building layout and the target position. Finally, electromagnetic (EM) simulations and experimental measurements are used to validate the effectiveness of the proposed algorithm.

This letter studies the problem of ultrawideband (UWB) tomographic imaging for unknown building layouts where the multipath-rich condition is considered. Specifically, first, the multiple propagation paths of the UWB signal are analyzed, and a delay estimation algorithm is proposed to estimate the direct path (DP) delay from the multipath signal. Then, a tomographic projection model is established by mapping the relationship between the delay of the DP and the relative permittivity of the unknown layout. Besides, a modified total variation method is developed to reconstruct the building layout, which shows significant performance in preserving the edges of the structure. Finally, the effectiveness of the proposed algorithm is verified using both simulated and real data.

This letter considers the problem of human activity recognition (HAR) behind the walls using ultra-wideband (UWB) radar. The graph convolutional network (GCN)-enhanced multi-domain fusion network (GMFN) is proposed to improve the recognition performance utilizing the complementarity of the multi-domain features. Specifically, firstly, a multi-branch convolutional neural network (CNN) is proposed to extract the multi-domain features from the range, time-frequency, and range-Doppler domain. Then the multi-domain features are constructed as a graph, and the GCN is employed to fuse the multi-domain features on the graph. Finally, HAR is implemented in the form of the graph classification. The experimental results on the real data show that the proposed GMFN achieves better performance than the state-of-the-art multi-domain fusion HAR methods.

This letter considers the problem of the target detection in the nonhomogeneous sea clutter environment, and proposes the complex-valued U-Net based clutter suppression method. Specifically, firstly, the complex signal features of radar echo sequences are extracted by developing the complex-valued convolutional blocks. Secondly, the complex multi-level features are fused, by employing the up-down sampling structure and skip connections, to suppress nonhomogeneous sea clutter. Further, the false alarm controllable detector is designed to detect the targets. Finally, the performance of the proposed method is evaluated via real data. The results show that it has a higher detection probability compared with the real-valued U-Net.

Building layout reconstruction (BLR) is an important topic in the field of through-the-wall imaging. Traditionally, reflective signals are commonly used to generate an accurate building map. However, due to the inherent features of electromagnetic waves, the reconstructed walls will inevitably suffer from problems such as deviation and cavities. Alternatively, as an extension of computed tomography, the transmissive signals can be exploited for BLR with high efficiency, but its performance degrades seriously when the sampling views are sparse. In this paper, to fully combine the superiority of the two types of implementations of BLR, we proposed a hybrid imaging framework by jointly exploiting the reflective and transmissive signals to retrieve the unknown layout. Specifically, first, the time delay of the transmissive signal will be estimated and used to reconstruct the spatial tomographic map. Then, a series of reflected echoes sampled by different routes will be compensated iteratively and used to generate the back-projection image. Finally, we fuse the two images generated by different types of signals using the feature-level detector. Both simulated and experimental results reveal that the proposed imaging framework can yield better performance compared with the image derived from single-type signals.

Non-line-of-sight (NLOS) detection is an enduring topic as it provides a powerful tool to monitor visually blocked areas. Currently, the NLOS detection requires precise prior knowledge of building layout, which limits its further applications in practice. In this paper, we consider the problem of joint estimation of building layout and target location in the NLOS scenario by exploiting multipath returns. Specifically, first, the building layout is simplified into combined linear equations with unknown parameters. In this way, we establish a parametrized multipath propagation model in the multiple targets NLOS scenario for the multiple-input-multiple-output (MIMO) radar, which is used in the image reconstruction and layout estimation problem. Then, a shape-remodeling group sparse constraint algorithm is proposed and combined with the particle swarm optimization method to simultaneously reconstruct the unknown layout and targets. Compared to the conventional compressed sensing-based methods, the proposed method integrates the basic structural characteristics and sparsity prior of the NLOS image to improve the stability of the solution. Finally, the performance of the proposed method is verified with numerical and experimental results.

Recently, wireless communication signals are not only used for information transmission but other possibilities are being developed, such as human body state estimation, wireless sensing, etc. In this article, the radio tomographic reconstruction of building layout and interior object is considered only utilizing the received signal strength indication (RSSI) of commercial WiFi signals. Specifically, first, an electromagnetic transmission energy attenuation model is introduced, which considers the case for scanning the entire unknown area using two wireless nodes. Then, we propose a novel algebraic iterative reconstruction method, which takes into account the constraint of prior knowledge and designs a more reasonable initial iteration factor. Finally, a variety of scenarios with different structures is built, and the reconstruction of the multimedia scenario is achieved for the first time with RSSI information. The simulation and experimental results show that the proposed method has better performance than current state-of-the-art algorithms in both the single-medium scenario and multimedia scenario.