mmWave radar-based human pose estimation is garnering increasing attention in Internet of Things (IoT) applications owing to its robustness under adverse lighting conditions and occlusion scenarios. However, accurately estimating human poses from mmWave signals remains challenging due to two key limitations: the lack of structural consistency when perceiving targets as a unified whole, and the absence of spatiotemporal consistency when tracking individual targets. To tackle these two challenges, this paper introduces RF-PoseR, a dedicated human pose rectifier for mmWave radar-based pose estimation. RF-PoseR serves as a corrective framework that refines the pose outputs generated by mmWave pose estimation systems. Based on the observation that perceptual inconsistencies in radar signals result in severely erroneous joint estimations, we introduce spatial and temporal constraints to enhance pose estimation accuracy. Specifically, RF-PoseR incorporates three core components: (1) unreliable joint removal guided by spatial structure, (2) pose completion leveraging temporal continuity, and (3) cross-modal pre-training utilizing large-scale visual pose datasets. Extensive experiments on mmWave radar pose datasets demonstrate that RF-PoseR significantly enhances the accuracy of poses generated by existing radar-based estimation networks. Furthermore, experiments on visual datasets confirm the method’s broader applicability beyond radar perception tasks.

In recent years, Channel State Information (CSI), recognized for its fine-grained spatial characteristics, has attracted increasing attention in WiFi-based indoor localization. However, despite its potential, CSI-based approaches have yet to achieve the same level of deployment scale and commercialization as those based on Received Signal Strength Indicator (RSSI). A key limitation lies in the fact that most existing CSI-based systems are developed and evaluated in controlled, small-scale environments, limiting their generalizability. To bridge this gap, we explore the deployment of a large-scale CSI-based localization system involving over 400 Access Points (APs) in a real-world building under the Integrated Sensing and Communication (ISAC) paradigm. We highlight two critical yet often overlooked factors: the underutilization of unlabeled data and the inherent heterogeneity of CSI measurements. To address these challenges, we propose a novel CSI-based learning framework for WiFi localization, tailored for large-scale ISAC deployments on the server side. Specifically, we employ a novel graph-based structure to model heterogeneous CSI data and reduce redundancy. We further design a pretext pretraining task that incorporates spatial and temporal priors to effectively leverage large-scale unlabeled CSI data. Complementarily, we introduce a confidence-aware fine-tuning strategy to enhance the robustness of localization results. In a leave-one-smartphone-out experiment spanning five floors and 25, 600m2, we achieve a median localization error of 2.17 meters and a floor accuracy of 99.49%. This performance corresponds to an 18.7% reduction in mean absolute error (MAE) compared to the best-performing baseline.

Physical adversarial attacks (PAAs) pose a serious security threat to millimeter-wave (mmWave) radar systems used in safety-critical applications such as autonomous driving and security checking. These attacks, manipulating radar signals via specially crafted materials, are proven feasible and can cause severe sensing failures; however, effective defenses remain unexplored due to the difficulty of distinguishing adversarial examples from normal environmental objects. This paper presents mmGuard, a physics-based defense framework that addresses this challenge by exploiting a fundamental insight: the engineering process that makes materials adversarial inevitably creates detectable physical signatures. We identify three key domains where adversarial examples show artificial nature: spatial phase discontinuities, anomalous radar cross-section patterns, and violations of natural physico-kinematic relationships. mmGuard systematically captures these signatures through multi-domain feature extraction, enhances their discriminability via neural refinement, and enables efficient per-object attack detection and mitigation compatible with automotive radar update rates. To enable evaluation, we introduce mmAD, comprising over 110,000 annotated radar frames with diverse adversarial examples across realistic deployment scenarios. Experimental results demonstrate that mmGuard achieves over 90% detection accuracy while exhibiting strong in-distribution performance, with few-shot adaptation enabling calibration to unseen settings Case studies further validate that mmGuard can reliably defend against PAAs in real-world settings.

Millimeter-wave (mmWave) radar systems with large array have pushed radar sensing into a new era, thanks to their high angular resolution. However, our long-term experiments indicate that array elements exhibit phase drift over time and require periodic phase calibration to maintain high-resolution, creating an obstacle for practical high-resolution mmWave sensing with large array. Unfortunately, existing calibration methods are inadequate for periodic recalibration, either because they rely on artificial references or fail to provide sufficient precision. To address this challenge, we introduce AutoCalib, the first framework designed to automatically and accurately calibrate high-resolution mmWave radars by identifying Ambient Radio Anchors (ARAs)—naturally existing objects in ambient environments that offer stable phase references. AutoCalib achieves calibration by first generating spatial spectrum templates based on theoretical electromagnetic characteristics. It then employs a pattern-matching and scoring mechanism to accurately detect these anchors and select the optimal one for calibration. Extensive experiments across 11 environments demonstrate that AutoCalib is capable of identifying ARAs that existing methods miss due to their focus on strong reflectors. AutoCalib's calibration performance approaches corner reflectors (74% phase error reduction) while outperforming existing methods by 83%. Beyond radar calibration, AutoCalib effectively supports other phase-dependent applications like handheld imaging, delivering 96% of corner reflector calibration performance without artificial references.

Although radio frequency (RF) sensing offers a promising approach for monitoring human cardiac activity, it traditionally requires subjects to remain in a steady-state throughout the monitoring process to avoid motion artifacts—an inherently impractical constraint that hampers real-world adoption. However, existing methods either yield incorrect estimates from motion-affected segments or discard them entirely, leading to fragmented data and incomplete observations. To address the challenge, we introduce MaskSense, a novel framework designed to address motion interference in long-term monitoring. The key insight is that latent patterns within dynamic inter-beat interval (IBI) sequences allow for accurate heartbeat reconstruction from incomplete observations. Leveraging this insight, MaskSense treats motion-affected periods as ”masked” and steady-state periods as ”unmasked”, and employs a contrastive-learning-assisted masked modeling architecture to reconstruct the masked information. Our 400-hour evaluation with 18 participants confirms that MaskSense effectively recovers IBIs in the presence of motion artifacts, paving the way for more natural and unobtrusive RF-based cardiac activity monitoring.

Passive non-line-of-sight (NLOS) imaging has developed rapidly in recent years. However, existing models generally suffer from low-quality reconstruction due to the severe loss of information during the projection process. In this paper, we introduce ParaEncodeNet, an NLOS imaging method for reconstructing high-quality, complex hidden scenes. Our approach utilizes a reconstruction network with parallel encoder to bridge the distribution gap between projection images and hidden images. The parallel encoder employs a codebook pretrained on a natural image dataset to construct a discrete prior, enabling the efficient encoding of projection images into hidden images. Moreover, we apply pixel-level constraints to the projection images to further reduce noise and distortion during reconstruction. Extensive experiments on a large-scale passive NLOS dataset have effectively demonstrated the superiority of our method over existing approaches, achieving a 1.2 dB increase in the Peak Signal-to-Noise Ratio (PSNR) metric. This validates the effectiveness and robustness of our proposed model in improving reconstruction quality and handling complex scenes.

Contactless stress monitoring, with its non-intrusive nature, is invaluable for maintaining mental and physical health. Recent studies have demonstrated encouraging results in contactless stress monitoring during daytime using radio frequency (RF) signals. However, the weak correlation between stress levels and behaviors during the night poses a significant challenge in stress monitoring, which remains unsolved. In this paper, we propose mmWave Nighttime Stress monitoring (mmNS), a learning-based end-to-end framework for contactless nighttime stress monitoring. Specifically, this framework incorporates radar signal processing and a self-supervised physiological feature separation strategy, combined with a signal complexity-oriented network design, to effectively extract and encode periodic physiological features for accurate stress level classification. To evaluate the stress monitoring performance of mmNS, we collect a RF-based nighttime stress monitoring dataset, which contains stress data from 10 volunteers. The experimental results demonstrate that our method achieves state-of-the-art stress monitoring performance, about 76% accuracy and 72% F1-score in classifying low, medium and high stress. To our knowledge, this is the first attempt dealing with contactless nighttime stress monitoring.

Human-centric perception with radio frequency (RF) signals has recently entered a new era of end-to-end processing with Transformers. Considering the long-sequence nature of RF signals, the State Space Model (SSM) has emerged as a superior alternative due to its effective long-sequence modeling and linear complexity. However, integrating SSM into RF-based sensing presents unique challenges including the fundamentally different signal representation, distinct frequency responses in different scenarios, and incomplete capture caused by specular reflection. To address this, we carefully devise a dual-branch SSM block that is characterized by adaptively grasping the most informative frequency cues and the assistant spatial information to fully explore the human representations from radar echoes. Based on these two branchs, we further introduce an SSM-based network for handling various downstream human perception tasks, named RFMamba. Extensive experimental results demonstrate the superior performance of our proposed RFMamba across all three downstream tasks. To the best of our knowledge, RFMamba is the first attempt to introduce SSM into RF-based human-centric perception.

Passive non-line-of-sight (NLOS) imaging aims to recover hidden scenes from indirect reflections. While reconstruction has been extensively studied, the high-level tasks for understanding hidden scenes, such as recognition, remain insufficiently explored despite their importance for practical applications. Direct classifying using either the projection or reconstructed images yields limited performance due to the severe image degradation. In this paper, we propose NLOS-R2, an alternate reconstruction-recognition framework that leverages the complementary nature of both tasks to enhance NLOS scene understanding. By iteratively optimizing reconstruction and recognition networks, our framework effectively improves recognition accuracy while maintaining reconstruction quality. To enable systematic evaluation, we introduce the first large-scale multi-class passive NLOS dataset, containing 42 classes and 50,400 projection and hidden image pairs. Extensive experiments demonstrate that our approach achieves 52.88% recognition accuracy, significantly outperforming existing methods. The code and dataset are available at https://github.com/ustceewy/NLOS-R2.

Millimeter-wave (mmWave) radar imaging has shown remarkable potential in critical applications. While previous researches have explored attacks on high-level radar perception, the vulnerability of low-level radar imaging to adversarial attacks remains largely unexplored. In this work, we introduce mmHide, the first general attack framework on mmWave radar imaging that utilizes neural rendering of meta-materials to hide imaging targets (e.g., handguns). mmHide’s novelty lies in its three-fold approach: (1) an implicit neural rendering network that efficiently represents and optimizes complex 3D meta-material structures, (2) an explicit differentiable forward imaging model that provides physical constraints, and (3) a self-supervised learning strategy that iteratively refines the meta-material design. This unique combination enables mmHide to create an “invisible cloak” for target objects while maintaining plausible imaging results. Extensive real-world experiments demonstrate mmHide’s effectiveness in significantly reducing target visibility while preserving background similarity. A user study confirms its high success rate in deceiving human observers, outperforming existing methods. These findings not only showcase the potential of our approach but also underscore the urgent need for robust defense mechanisms in mmWave imaging systems.

Passive non-line-of-sight (NLOS) imaging has witnessed rapid development in recent years, due to its ability to image objects that are out of sight. The light transport condition plays an important role in this task since changing the conditions will lead to different imaging models. Existing learning-based NLOS methods usually train independent models for different light transport conditions, which is computationally inefficient and impairs the practicality of the models. In this work, we propose NLOS-LTM, a novel passive NLOS imaging method that effectively handles multiple light transport conditions with a single network. We achieve this by inferring a latent light transport representation from the projection image and using this representation to modulate the network that reconstructs the hidden image from the projection image. We train a light transport encoder together with a vector quantizer to obtain the light transport representation. To further regulate this representation, we jointly learn both the reconstruction network and the reprojection network during training. A set of light transport modulation blocks is used to modulate the two jointly trained networks in a multi-scale way. Extensive experiments on a large-scale passive NLOS dataset demonstrate the superiority of the proposed method. The code is available at https://github.com/JerryOctopus/NLOS-LTM.

Recent advancements have showcased the potential of handheld millimeter-wave (mmWave) imaging, which applies synthetic aperture radar (SAR) principles in portable settings. However, existing studies addressing handheld motion errors either rely on costly tracking devices or employ simplified imaging models, leading to impractical deployment or limited performance. In this paper, we present IFNet, a novel deep unfolding network that combines the strengths of signal processing models and deep neural networks to achieve robust imaging and focusing for handheld mmWave systems. We first formulate the handheld imaging model by integrating multiple priors about mmWave images and handheld phase errors. Furthermore, we transform the optimization processes into an iterative network structure for improved and efficient imaging performance. Extensive experiments demonstrate that IFNet effectively compensates for handheld phase errors and recovers high-fidelity images from severely distorted signals. In comparison with existing methods, IFNet can achieve at least 11.89 dB improvement in average peak signal-to-noise ratio (PSNR) and 64.91% improvement in average structural similarity index measure (SSIM) on a real-world dataset.

Recent advancements have showcased the potential of hand-held millimeter-wave (mmWave) imaging, which applies synthetic aperture radar (SAR) principles in portable settings. However, existing studies addressing handheld motion errors either rely on costly tracking devices or employ simplified imaging models, leading to impractical deployment or limited performance. In this paper, we present IFNet, a novel deep unfolding network that combines the strengths of signal processing models and deep neural networks to achieve imaging and focusing for handheld mmWave systems. By integrating multiple priors and mapping the optimization processes into an iterative network structure, IFNet effectively compensates for phase errors and recovers high-fidelity images from severely distorted signals. Extensive experiments demonstrate that IFNet outperforms state-of-the-art methods, both qualitatively and quantitatively.

Millimeter-wave (mmWave) radar has gained increasing attention in environmental perception due to its robustness under low-light conditions. However, existing methods fail to address the challenges of multipath interference and low angle resolution. In this paper, we introduce DiffRadar which leverages the diffusion probabilistic model (DPM) for high-quality mmWave environmental sensing. To adapt DPM for radar signals that lack pix-level structural information, we design a contour encoder to capture intrinsic scene features that enable the DPM to learn a robust representation from radar data. Then the DPM decoder utilizes this high-level semantic information to effectively reconstruct real-world scene distribution. Extensive experiments have demonstrated that our approach surpasses state-of-the-art methods in various complex scenarios.

Millimeter-wave (mmWave) radar pointcloud offers attractive potential for 3D sensing, thanks to its robustness in challenging conditions such as smoke and low illumination. However, existing methods failed to simultaneously address the three main challenges in mmWave radar pointcloud reconstruction: specular information lost, low angular resolution, and severe interference. In this paper, we propose DREAM-PCD, a novel framework specifically designed for real-time 3D environment sensing that combines signal processing and deep learning methods into three well-designed components to tackle all three challenges: Non-Coherent Accumulation for dense points, Synthetic Aperture Accumulation for improved angular resolution, and Real-Denoise Multiframe network for interference removal. By leveraging causal multiple viewpoints accumulation and the "real-denoise" mechanism, DREAM-PCD significantly enhances the generalization performance and real-time capability. We also introduce RadarEyes, the largest mmWave indoor dataset with over 1,000,000 frames, featuring a unique design incorporating two orthogonal single-chip radars, Lidar, and camera, enriching dataset diversity and applications. Experimental results demonstrate that DREAM-PCD surpasses existing methods in reconstruction quality, and exhibits superior generalization and real-time capabilities, enabling high-quality real-time reconstruction of radar pointcloud under various parameters and scenarios. We believe that DREAM-PCD, along with the RadarEyes dataset, will significantly advance mmWave radar perception in future real-world applications.

Passive non-line-of-sight (NLOS) imaging has drawn great attention in recent years. However, all existing methods are in common limited to simple hidden scenes, low-quality reconstruction, and small-scale datasets. In this paper, we propose NLOS-OT, a novel passive NLOS imaging framework based on manifold embedding and optimal transport, to reconstruct high-quality complicated hidden scenes. NLOS-OT converts the high-dimensional reconstruction task to a low-dimensional manifold mapping through optimal transport, alleviating the ill-posedness in passive NLOS imaging. Besides, we create the first large-scale passive NLOS imaging dataset, NLOS-Passive, which includes 50 groups and more than 3,200,000 images. NLOS-Passive collects target images with different distributions and their corresponding observed projections under various conditions, which can be used to evaluate the performance of passive NLOS imaging algorithms. It is shown that the proposed NLOS-OT framework achieves much better performance than the state-of-the-art methods on NLOS-Passive. We believe that the NLOS-OT framework together with the NLOS-Passive dataset is a big step and can inspire many ideas towards the development of learning-based passive NLOS imaging. Codes and dataset are publicly available (https://github.com/ruixv/NLOS-OT).

Low visibility and high-level noise are two challenges for low-light image enhancement. In this paper, by introducing fractional order differential, we propose an end-to-end conditional generative adversarial network(GAN) to solve those two problems. For the problem of low visibility, we set up a global discriminator to improve the overall reconstruction quality and restore brightness information. For the high-level noise problem, we introduce fractional order differentiation into both the generator and the discriminator. Compared with conventional end-to-end methods, fractional order can better distinguish noise and high-frequency details, thereby achieving superior noise reduction effects while maintaining details. Finally, experimental results show that the proposed model obtains superior visual effects in low-light image enhancement. By introducing fractional order differential, we anticipate that our framework will enable high quality and detailed image recovery not only in the field of low-light enhancement but also in other fields that require details.