Non-contact 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 paper 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 multi-modal 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 non-contact vital sign monitoring and facilitating practical deployment.

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 building interior structure sensing has been greatly serving in various applications, including search-and-rescue operations. However, most existing methods exhibit limitations in imaging the walls and corners with good continuity and recognizable features. In this article, we consider imaging of the building interior structures by extracting the major building elements with structural continuity. Specifically, the signals from a complex building are first modeled as the superposition responses from discrete canonical scatterers, such as planar walls and wall corners. Then, a structural variational Bayesian method is designed to detect and extract these critical structures. This method improves the 1-D continuity of the walls and the 2-D continuity of the corners through a Bayesian hierarchical probabilistic model. Moreover, we incorporate the generalized approximate message-passing technique into the variational expectation maximization method to efficiently estimate the walls and corners simultaneously. Results from both simulated and real data validate the effectiveness of the proposed method in accurately extracting walls and corners with improved continuity, thereby enabling a comprehensive building structure.

Unmanned aerial vehicle (UAV) through-the-wall (TTW) synthetic aperture radar (SAR) extends traditional remote sensing into penetration perception of obstructed areas in high-rise buildings with significant advantages. However, image defocusing caused by motion errors severely hinders the application of UAV TTW SAR. The mismatched signal model in the TTW condition leads the ineffectiveness of conventional airbone autofocus, while wide-beam and wide-band characteristics of the UAV TTW SAR system further aggravate the defocusing issue. In the paper, an effective metric-based motion error estimation with ground Cartesian back-projection (GCBP) algorithm is proposed. Unlike conventional SAR scenarios, a parametric signal model of UAV TTW SAR is established by incorporating refraction approximation which is a critical distinction absent in traditional remote sensing. Phase errors are further analysed from the perspective of geometric, frequency-dependent, time-variant, and space-variant characteristics. Then, aperture division and subband division are integrated with GCBP algorithm for efficient imaging. With the constraint of the UAV motion continuity, a metric-based optimization method is designed. Gradient descent combined with the line search strategy constitutes an iterative optimization mechanism. Finally, through the verifications of simulations and experiments, the proposed algorithm realizes precise motion error estimation and achieves fabulous refocusing performance in UAV TTW SAR. This technology holds immense potential for large-scale through-wall sensing of high-rise buildings, bridging the gap between traditional remote sensing and concealed space sensing in urban environments.

Through-wall radar has been very pertinent to a variety of civilian and military services because of its ability to detect and sense through the wall obstacles. However, to maintain the penetrating ability, most of the existing TWR systems work at L/S band with limited bandwidth and thus can only generate a very crude blob of the target, whose resolution is not easy-to-use for many practical applications. To address this issue, this paper proposes a novel high resolution TWR imaging system by deep learning-based data fusion and reasoning techniques. First, we devise an image-reasoning module by fusing TWR and optical images with generative adversarial networks. Then, in the online phase, the low-resolution TWR image is fed into the image-reasoning module for resolution improvement. Extensive simulations and experiments demonstrate that the proposed method can successfully reconstruct the outline of an object rather than just a blob, which greatly eases the end user to interpret and thus facilitating more applications.

Through-the-wall radar can detect and locate targets behind obstacles, which has been widely applied in various civil and military applications. However, wall reflections are usually stronger than those of the targets, making it very challenging to extract the target information. To tackle the problem, this paper proposes an adaptive wall clutter mitigation method. First, the discrete prolate spheroidal sequence basis is used to model wall clutter due to its superiority in modeling the spatially extended property of the walls. Then, by incorporating the compressed sensing technique, the subspace pursuit algorithm is combined with block and adaptive iteration to estimate wall clutter. In an adaptive manner, the proposed algorithm greatly eases the requirement on priors and thus improves the applicability in practice. Practical experiments commendably validate the effectiveness and superiority of the proposed method.

Indoor intelligent perception systems have gained significant attention in recent years. However, accurately detecting human presence can be challenging in the presence of non-human subjects such as pets, robots, and electrical appliances, limiting the practicality of these systems for widespread use. In this paper, we propose a novel system (“WI-MOID") that passively and unobtrusively distinguishes moving human and various non-human subjects using a single pair of commodity WiFi transceivers, without requiring any device on the subjects or restricting their movements. WI-MOID leverages a novel statistical electromagnetic wave theory-based multipath model to detect moving subjects, extracts physically and statistically explainable features of their motion, and accurately differentiates human and various non-human movements through walls, even in complex environments. In addition, WI-MOID is suitable for edge devices, requiring minimal computing resources and storage, and is environment-independent, making it easy to deploy in new environments with minimum effort. We evaluate the performance of WI-MOID in five distinct buildings with various moving subjects, including pets, vacuum robots, humans, and fans, and the results demonstrate that it achieves 97.34% accuracy and 1.75% false alarm rate for identification of human and non-human motion, and 95.98% accuracy in unseen environments without model tuning, demonstrating its robustness for ubiquitous use.

Through-the-wall radar can detect and localize hidden targets behind obstacles, which has been widely applied in varieties of civil and military applications. However, wall reflections are usually stronger than that of the targets, making it very challenge to extract the target information. In this paper, an adaptive wall clutter mitigation method is proposed. The discrete prolate spheroidal sequence basis is first used to model the wall clutter due to its superiority in modeling the spatially extended property of the walls. Then, by incorporating the compressed sensing technique, we propose a block adaptive subspace pursuit algorithm to estimate the support of the wall, which is further sifted by leveraging the correlation of the wall clutter over different antenna positions. In an adaptive manner, the proposed algorithm can realize the mitigation of wall clutter and separate the target echo data without wall parameters or other prior information, which greatly improves the robustness in practice. Extensive simulations and real-world experiments commendably validate the effectiveness and superiority of the proposed method.

In-car child presence detection (CPD) has gained worldwide attention due to increased child deaths reported yearly when they are left unattended in a car. Existing solutions usually require dedicated sensors and are being surpassed by WiFi-based CPD because the latter can provide broader coverage and can reuse the in-car WiFi devices. However, the existing WiFi-based CPD solutions are not robust and may suffer from miss detection due to the very weak breathing of a young child and high false alarms under unfavorable environmental conditions. In this paper, we propose a WiFi-based robust CPD system consisting of a motion and breathing detector. To improve breathing detection, we propose to treat the intermediate spectrogram for breathing estimation as images and apply image enhancement techniques followed by effective false alarm removal. Extensive experimental results have confirmed the robustness of the proposed system with a 99% detection accuracy and 3% false alarm rate.

Respiratory rate tracking has gained more and more interest in the past few years because of its great potential in exploring different pathological conditions of human beings. Conventional approaches usually require dedicated wearable devices, making them intrusive and unfriendly to users. To tackle the issue, many WiFi-based respiration tracking systems have been proposed because of WiFi’s ubiquity, low-cost, and most importantly, contactlessness. However, most existing works are of limited coverage and inflexible deployment, which greatly hinders their applications. In this paper, we propose WiResP, a practical and innovative WiFi-based respiration tracking system that utilizes a contrast enhancement technique to improve the detection of respiration. This approach combines both instantaneous and time-domain information, resulting in better recognition of breaths and identification of breath patterns. Extensive experiments under different settings show that WiResP can well capture respiratory rate during sleep under flexible deployments. Moreover, it remarkably increases the sensing coverage compared with the existing methods, making it a potential candidate toward real-world applications.

Heat-stroke and death of children being left alone in a parked car has attracted more and more attentions. As a result, car manufactures start to reward solutions for in-car Child Presence Detection (CPD) system to save lives recently. However, most of the existing works rely on dedicated sensors and only achieve limited accuracy and coverage. This paper presents the first-of-its-kind intelligent CPD system using commodity Wi-Fi. Based on a statistical electromagnetic wave model to fully leverage the information in all the multi-path components, the proposed CPD system mainly consists of a motion target detector to detect a child in awake/motion status, a stationary target detector to detect a sleeping child by extracting breathing rate information, and a transition target detector based on a Naive Bayes Classifier using multipath profiles as features. We build a real-time testbed and show through extensive experiments that the proposed system can achieve ≥ 99.34% detection rate and ≤ 4.38% false alarm rate, regardless of the location and motion status of a child. Built upon 2.4/5GHz Wi-Fi, the proposed system can integrate with the existing in-car Wi-Fi system with no additional hardware and calls for low CPU and memory consumptions, thus promising a practical candidate for CPD applications.

As automobiles have become an essential part to facilitate our daily life, advanced driver assistance systems (ADASs) have been gaining more and more interest in assisting drivers to enhance both safety and convenience. To respond timely in case of an emergency, ADAS needs to keep track of the driver’s health/consciousness, which is generally achieved by monitoring the driver’s vital signs, including respiration rate (RR), heart rate (HR), and heart rate variability (HRV). However, most of the state-of-art solutions need to assume that the human is stationary, which does not hold in practical driving scenarios. To tackle the problem, we propose a novel system, which can estimate driver’s RR, HR, and interbeat intervals (IBIs) in the presence of driver’s motion artifacts using commercial millimeter-wave (mmWave) radio. The system consists of two key components. First, to extract the reflection signals containing vital signals, the motion artifacts are first removed by a novel motion compensation module, followed by the periodicity check to identify the components with vital signals. Second, the respiration and heartbeat signals are reconstructed by jointly optimizing the decomposition of all the extracted compound vital signals over different range-azimuth bins. We evaluate the system performance in a real driving environment and investigate the impact of different parameters, including the device locations, pavement conditions, and motion types. The experimental results show that the proposed system can achieve a median error of 0.16 respiration per minute (RPM), 0.82 beat per minute (BPM), and 46 ms for RR, HR, and IBI estimations, corresponding to the relative accuracy of 99.17%, 98.94%, and 94.11%, respectively.

Child presence detection (CPD) is becoming a regulatory requirement for car manufacturers to save children’s lives when they are left alone in unattended vehicles. However, most of the existing solutions require dedicated devices and suffer from limited accuracy and coverage. In this article, we build WiCPD, the first-of-its-kind in-car CPD system using commodity Wi-Fi, which can cover the entire interior of a car with no blind spot. First, we introduce a statistical electromagnetic model which accounts for the impact of motion on all the multipaths inside a car, followed by a motion statistics metric indicating the ambient motion intensity and a signal-to-noise-ratio (SNR) boosting scheme to extract the minute chest movement. Then, we design a unified CPD framework consisting of three target detector modules, including a motion target detector to detect a child in motion/awake, a stationary target detector to detect a stationary/sleeping child, and a transition target detector to detect a sleeping child with sporadic motion who is missed by both the motion and stationary target detectors. We implement a real-time WiCPD system by using commercial Wi-Fi chipsets, deploy it over 20 different cars, and collect data for multiple children aging from 4 to 50 months. The results show that WiCPD can achieve 100% detection rate within 8 s when the child is awake/in-motion and 96.56% detection rate within 20 s for a static/sleeping child. Extensive experiments also demonstrate that WiCPD can be easily deployed in minutes without calibration and enjoys very low CPU and memory consumption, thus promising a practical candidate for CPD applications.

Heart Rate Variability (HRV), which measures the fluctuation of heartbeat intervals, has been considered as an important indicator for general health evaluation. In this paper, we present mmHRV, a contact-free HRV monitoring system using commercial millimeter-wave (mmWave) radio. We devise a heartbeat signal extractor, which can optimize the decomposition of the phase of the channel information modulated by the chest movement, and thus estimate the heartbeat signal. The exact time of heartbeats is estimated by finding the peak location of the heartbeat signal while the Inter-Beat Intervals (IBIs) can be further derived for evaluating the HRV metrics. Experimental results show that mmHRV can measure the HRV accurately with 3.68ms average error of mean IBI (w.r.t. 99.49% accuracy) based on the experiments over 10 participants.

While high accuracy tracking of targets has been extensively explored because of its wide applications, many exiting methods degenerate in the presence of multipath distortions. This paper proposes an accurate and novel multipath-resilient system to track the targets by leveraging the large number of antennas in massive MIMO systems. We first prove that statistical autocorrelation of the received energy physically shows a sinc-like distribution around the receiver in far-field scenario. Based on such an observation, a novel method is developed to estimate the moving speed of the target with respect to a single base station. The absolute moving speed and direction are further estimated by using the geometrical relationships among multiple base stations and thus we can track the target by dead-reckoning using the consecutive moving speed and moving direction estimations. Numerical simulations show that the proposed system can achieve decimeter-lever accuracy for tracking in various environments, which outperforms the existing methods.

Heart rate variability (HRV), which measures the fluctuation of heartbeat intervals, has been considered as an important indicator for general health evaluation. To alleviate the user burden and explore the usability for long-term health monitoring, noncontact methods for HRV monitoring have drawn tremendous attention. In this article, we present mmHRV, the first contact-free multiuser HRV monitoring system using commercial millimeter-wave (mmWave) radio. The design of mmHRV consists of two key components. First, we develop a calibration-free target detector to identify each user’s location. Second, a heartbeat signal extractor is devised, which can optimize the decomposition of the phase of the channel information modulated by the chest movement and, thus, estimate the heartbeat signal. The exact time of heartbeats is estimated by finding the peak location of the heartbeat signal while the interbeat intervals (IBIs) can be further derived for evaluating the HRV metrics of each target. We evaluate the system performance and the impact of different settings, including the distance between human and the device, user orientation, incidental angle, and blockage. Experimental results show that mmHRV can measure the HRV accurately with a median IBI estimation error of 28 ms (with respect to 96.16% accuracy). In addition, the root-mean-square error (RMSE) measured in the nonline-of-sight (NLOS) scenarios is 31.71 ms based on the experiments with 11 participants. The performance of the multiuser scenario is slightly degraded compared with the single-user case; however, the median error of the 3-user case is within 52 ms for all three tested locations.

High-accuracy target localization and tracking have been widely used in the modern navigation system. However, most of the methods such as global positioning system (GPS) are highly dependent on time measurement accuracy, which prevents them from achieving high accuracy in practice. Time reversal (TR)-based technique has been shown to be able to achieve centimeter accuracy localization by fully utilizing the focusing effect brought by the massive multipaths naturally existing in a rich scattering environment such as indoor scenarios. By investigating a similar statistical property, this article develops a novel high-accuracy target localization method by using massive MIMO to provide massive signal components. We first observe that the statistical autocorrelation of the received energy physically focuses into a beam around the receiver exhibiting a sinc-like distribution in the far-field scenario. By leveraging such a distribution of the focusing beam, an effective way to estimate the relative moving speed of the target with respect to a single base station is proposed. We also obtain the absolute moving speed and subsequently track the target accurately by associating the speed estimation results and geometrical relationship of multiple stations. The theoretical analysis on the error in the speed and localization estimation validated by numerical simulation results show that the proposed system can achieve decimeter accuracy for target localization and tracking.

In a low angle target parameter estimation scenario, the backscattered signals from targets are distorted by clutter and multipath, which degrades the performance of direction-of-arrival (DOA) estimator significantly. This paper presents a novel method using time reversal (TR) technique and coherent signal-subspace method (CSM) for DOA estimation in a low angle scenario. The TR method exploits target information contained in the return echoes due to multipath and adaptively adjusts TR probing waveforms to increase the signal-to-noise ratio (SNR). Furthermore, CSM is adopted to focus the energy of the multipath signal in a predefined subspace so as to exploit the full time-bandwidth product of the target source. We analyze the performance of the proposed new DOA algorithm. Numerical simulations demonstrate its superior performance compared with the conventional DOA estimators.