WiFi indoor localization plays an important role in many real-world applications and has gained widespread attentions from both academia and industry over the past decade. While existing works have already achieved remarkable performance under various practical scenarios, they solely utilize multiple WiFi Access Points (APs) to achieve a higher accuracy and do not deeply explore the intrinsic relationships among them. To further unleash the potential of multiple APs and reach the limits of WiFi indoor localization, in this paper, we propose Qidi, a novel multi-APs collaboration based localization system. To the best of our knowledge, we are the first to explicitly reveal three underlying relationships among multiple spatially distributed APs, i.e., consistency, continuity, and non-uniformity. By combining these three basic principles with the unique characteristics of specific tasks, a series of long-lasting practical challenges, such as automatic phase offset calibration, bilateral angle ambiguity, elevation angle estimation with Uniform Linear Array (ULA) and Non-Line-of-Sight (NLoS), can be efficiently resolved. Extensive experiments in various complex environments are provided to demonstrate that Qidi can achieve 2.4°, 3.2° median errors of joint azimuth and elevation angle estimation, and 0.4mlocalization median error even in 20m×20mexhibition hall. Moreover, a one-month longitudinal evaluation conducted on a real-world deployed WiFi ISAC system further validates the effectiveness and robustness of the proposed localization system.

As RF sensing increasingly moves toward real-world deployments, critical concerns emerge around unauthorized model usage and access control. Existing protection approaches-such as watermarking-are typically reactive, task-specific, and ineffective at runtime. This paper presents AuthRF (Authentication with passports for RF sensing models), a novel signal-level passport mechanism that proactively enforces access control by mapping a user-specific passport to phase-compensation weights in the signal processing pipeline. Valid passports yield coherent phase alignment and high-fidelity representations, while invalid or forged ones induce phase distortion that significantly degrades model performance. This design effectively deters unauthorized access, supports scalable multi-user authentication, and enables personalized service provisioning through controlled passport variation. We evaluate AuthRF on six representative RF sensing tasks using both WiFi and radar signals. Experimental results demonstrate its robust protection capabilities and seamless integration with existing sensing pipelines, positioning AuthRF as a practical foundation for secure and commercial-grade RF sensing deployment.

The learning-based RF sensing methods typically involve signal processing to transform the RF signals into spectrograms, which are then input into neural networks. However, this approach is suboptimal due to the potential degradation caused by the inherent multi-path interference, resulting in blurred RF spectrograms. To tackle this challenge, we introduce a novelRefocusingTransformerbackbone customized forRFsensing (RF$^{2}$Transformer). Rather than attempting to precisely model interference using the traditional signal processing techniques, the RF$^{2}$Transformer utilizes a self-compensation mechanism to treat the interference-induced phase shifts as learnable parameters. This mechanism learns and compensates for the phase shift caused by interference in the complex feature space to obtain refocused and high-quality feature maps of RF spectrograms, thereby improving the performance of downstream tasks. We show that the RF$^{2}$Transformer is general for various RF sensing tasks by evaluating it on six typical RF sensing tasks using two general RF signals (WiFi and radar). Experimental results indicate that the RF$^{2}$Transformer takes an important step toward learning-based solutions for RF sensing.

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.

The major challenge in learning-based RF sensing is acquiring high-quality large-scale annotated datasets. Unlike visual datasets, RF signals are inherently non-intuitive and non-interpretable, making their annotation both time-consuming and labor-intensive. To address this challenge, we propose RF-URL 2.0, a novel unsupervised representation learning (URL) framework for RF sensing, which enables pre-training on easily collected, large-scale unannotated RF datasets to make downstream tasks solve easier. Existing URL techniques, such as contrastive learning, are primarily designed for natural images and are prone to learn shortcuts rather than meaningful information when applied to RF signals. RF-URL 2.0 is the first framework to overcome these limitations by constructing positive and negative pairs through well-established RF signal processing algorithms. Besides, it introduces a novel signal-model-driven augmentation technique, which augments signal representations by identifying and perturbing physically meaningful parameters of signal processing models. Moreover, the RF-URL 2.0 is carefully designed to take into account the heterogeneity characteristics of different RF signal processing representations. We show the universality of RF-URL 2.0 in three typical RF sensing tasks using two general RF devices (WiFi and radar), including human gesture recognition, 3D pose estimation, and silhouette generation. Extensive experiments on the HIBER and WiDAR 3.0 datasets demonstrate that RF-URL 2.0 takes a significant step toward learning-based solutions for RF sensing.

Self-supervised learning (SSL) is a powerful approach that learns general semantic representations from large-scale unlabeled data to make downstream tasks solve easier, offering significant potential in enhancing downstream performance and alleviating the appetite for large-scale annotated data. However, existing SSL techniques, predominantly designed for natural images, may be prone to shortcuts when applied to RF signals. This study presents surprising empirical findings showing that SSL can indeed learn meaningful RF representations by employing simple group shuffle (GS) and asymmetry augmentation techniques. The GS augmentation is inspired by blind calibration tasks in Time-Interleaved Analog-to-Digital Converters (TIADC). By treating the original RF signal as a composite output from sub-ADCs, GS augmentation enriches RF signals while preserving their global semantics. We also provide a theoretical validation of the GS augmentation’s singular value consistency. Notably, we observe that the shortcut is essentially a domain gap between the pre-trained and the downstream task models. This issue can be mitigated by an asymmetry augmentation technique, which maximizes the similarity between an original RF signal and its augmented version, rather than between two augmentations of the same RF signal. By integratinggroupshuffle andasymmetryaugmentation (GSAA) into an existing contrastive learning framework, we develop an effective contrastive learning approach for RF signals. Our evaluations, spanning seven downstream RF sensing tasks across two general RF devices (WiFi and radar), strongly demonstrate that GSAA plays a significant role in advancing SSL-based solutions in RF sensing.

Millimeter-wave (mmWave) radar sensing powered by deep learning is now emerging in numerous applications, which are predominantly trained in a supervised manner. However, due to the non-interpretable nature of mmWave signals, labeling the radar data has always been a difficult task. While there have been investigations on unsupervised pre-training for mmWave radar sensing, these methods are tailored to specific signal representations. In this paper, we propose UMIMO, an unsupervised learning framework combining the hardware nature of MIMO radar and deep learning techniques to resolve the challenge raised by the insufficient labeled data. UMIMO leverages the antenna arrays synthesized from multiple transmitting and receiving antennas in mmWave radar to construct positive samples for contrastive learning. To achieve this, we propose the constraints on angular resolution and grating lobes to generate effective signal representations with different synthetic arrays. We conduct experiments using UMIMO on three tasks: contactless ECG monitoring, 3D human pose estimation, and human silhouette generation. All experimental results demonstrate that UMIMO can effectively improve the performance of learning-based mmWave radar sensing in an unsupervised manner.

This paper introduces a novel paradigm for learning-based RF sensing, termed Pre-training RF signals In Sparsity-aware Masked autoencoders (PRISM), which shifts the RF sensing paradigm from supervised training on limited annotated datasets to unsupervised pre-training on large-scale unannotated datasets, followed by fine-tuning with a small annotated dataset. PRISM leverages a carefully designed sparsity-aware masking strategy to predict missing contents by masking a portion of RF signals, resulting in an efficient pre-training framework that significantly reduces computation and memory resources. This addresses the major challenges posed by large-scale and high-dimensional RF datasets, where memory consumption and computation speed are critical factors. We demonstrate PRISM’s excellent generalization performance across diverse RF sensing tasks by evaluating it on three typical scenarios: human silhouette segmentation, 3D pose estimation, and gesture recognition, involving two general RF devices, radar and WiFi. The experimental results provide strong evidence for the effectiveness of PRISM as a robust learning-based solution for large-scale RF sensing applications.

Indoor localization is a fundamental task to many real-world applications, which however remains unresolved, especially with commodity WiFi Access Points (APs). In this paper, we tackle this problem and propose an accurate, robust, and real-time indoor localization system that can be directly deployed on commodity WiFi infrastructure. Specifically, the proposed system makes three key contributions: 1) we introduce a non-parametric metric to measure the accuracy of Angle of Arrival (AoA) estimation; 2) we are the first to explicitly consider the relationship among the AoAs of different APs and propose a multiple APs co-localization algorithm to exploit such a relationship to improve the localization performance; 3) we propose several strategies to reduce the computational complexity of our system to achieve real-time localization. Extensive experiments are conducted to evaluate the performance of the proposed system under various situations, which demonstrate that the proposed system can achieve a 4 degrees median error of AoA estimation and 30 cm localization median error, outperforming the state-of-the-art systems.

Toxic gases inhalation is the most common cause of death in fire scenes, which can make people unconscious and unable to save themselves. Hence, discovering the unconscious victims is crucial to improve their survival rate. In this paper, we propose RF-Search, a victim searching system with RF device mounted on the drone. The challenge mainly comes from the fact that drone motion would overwhelm the subtle vital signs utilized for victim identification. To resolve this problem, we have noted that the physical signature of drone motion has been encoded in stationary object reflections. Leveraging this unique physical signature, we propose to identify the unconscious victim through the spatio-temporal correlation between signals reflected from the victim and the surrounding stationary objects. To extract respiration information of the victim, we propose a motion segmentation module and a motion compensation module to suppress the signal variation caused by drone movement. Extensive experiments have demonstrated that our system could achieve an accuracy of 92.5% for victim identification.

The major obstacle for learning-based RF sensing is to obtain a high-quality large-scale annotated dataset. However, unlike visual datasets that can be easily annotated by human workers, RF signal is non-intuitive and non-interpretable, which causes the annotation of RF signals time-consuming and laborious. To resolve the rapacious appetite of annotated data, we propose a novel unsupervised representation learning (URL) framework for RF sensing, RF-URL, to learn a pre-training model on large-scale unannotated RF datasets that can be easily collected. RF-URL utilizes a contrastive framework to mind the gap between signal-processing-based RF sensing and learning-based RF sensing. By constructing positive and negative pairs through different signal processing representations, RF-URL seamlessly integrates the existing RF signal processing algorithms into the learning-based networks. Moreover, the RF-URL is carefully designed to take into account the asymmetric characteristics of different RF signal processing representations. We show that RF-URL is universal to a variety of RF sensing tasks by evaluating RF-URL in three typical RF sensing tasks (human gesture recognition, 3D pose estimation and silhouette generation) based on two general RF devices (WiFi and radar). All experimental results strongly demonstrate that RF-URL takes an important step towards learning-based solutions for large-scale RF sensing applications.

This letter mainly deals with the problem of counting moving human targets in an enclosed building space for through-the-wall radar. Specifically, a typical deep convolutional neural network, namely, residual neural network (ResNet), is designed to identify the line-like texture information associated with the target number from the blurred range-time images of a single-channel stepped-frequency continuous-wave (SFCW) radar. Experiments demonstrate that the ResNet-based counting algorithm achieves an accuracy of 91.54% for one to six human targets, and the accuracy rises to 97.12% when only counting one to three humans, even under conditions of wall penetration degradation, limited spatial resolution, heavy multipath clutters, and target-to-target occlusion. The achieved number of information of moving human targets not only contributes directly to the situation assessment behind the wall but also can act as the prior information to promote further target detection.