Search Results (459)

The rapid development of Internet of Things technologies has accelerated the deployment of smart home systems. However, perception solutions based on visual sensors remain constrained by illumination sensitivity, occlusion, and privacy concerns. Frequency-modulated continuous-wave (FMCW) millimeter-wave radar provides a promising alternative because it operates independently of lighting conditions, is robust to environmental changes, and preserves user privacy. To address multiple-extended-target tracking in cluttered indoor environments, this paper proposes a high-accuracy tracking algorithm that combines an improved Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, an optimized Nearest-Neighbor Data Association (NNDA) scheme, and an Extended Kalman Filter (EKF). The improved DBSCAN algorithm introduces spatial-extent constraints, velocity-consistency checks, and candidate-cluster validation to cluster raw radar point clouds and convert extended targets into representative point targets with little additional computational cost. The optimized NNDA scheme then integrates clustering information into the association process, improving the matching accuracy between existing tracks and current measurements. Finally, the EKF estimates the state of each target from the associated measurements. Real-world experiments show that the proposed algorithm achieves tracking errors below 0.4 m in typical motion scenarios, maintains continuous tracking in two-person crossing scenarios, and reaches 93.3% counting accuracy in five-person scenarios. These results outperform the tracking system based on the commercial Texas Instruments (TI) IWR6843ISK millimeter-wave radar evaluation board. The proposed method offers a reliable and privacy-preserving sensing solution for smart homes, elderly care, and intelligent building applications. Full article

Radar technology in the microwave and millimeter-wave frequency range is the subject of current research for structural health monitoring of composite materials, e.g., damage detection in wind turbine blades. Performance assessment, enabling widespread practical application of this promising and non-contact sensing approach, can be realized via probability of detection (POD) theory, which is a statistical method for determining the detectability of damage through response metrics as a function of flaw size. This paper deals with the experimental investigation of a delamination model represented by two parallel glass fiber reinforced polymer plates separated from each other from $0\mathrm{mm}$ to $1\mathrm{mm}$ in steps of $0.01\mathrm{mm}$. Experimental studies with a frequency modulated continuous wave radar are performed under laboratory conditions in the frequency range from $57GHz$ to $65GHz$. The signal response is represented by two damage indicators (DIs), according to the root mean square deviation and Mahalanobis distance. Since the reflection of electromagnetic waves exhibits a nonlinear behavior, this also implies a nonlinear response in the DI characteristic. The novelties in this work are the successful implementation of a nonlinear regression model, combined with an optimal threshold decision through receiver operating characteristic curves for a high-resolution POD representation. The POD with $95\%$ confidence bounds indicates the flaw size at which the delamination can be detected reliably. Depending on the radar distance in experimental studies, the binary structural condition (damaged or undamaged) was correctly assessed from $95\%$ to $100\%$. The minimum detectable size ranges from $0.01\mathrm{mm}$ to $0.08\mathrm{mm}$. Full article

The increasing use of Unmanned Aerial Systems (UAS) in civil applications has accelerated the development of new Air Traffic Management (ATM) frameworks to ensure the safe and efficient operation. Onboard technology, such as Detect and Avoid (DAA) systems, have been proposed as an alternative to reduce operational risk to acceptable levels. However, these technologies require preliminary validation to meet current regulatory standards, which define the Minimum Operational Performance (MOP). In this work, we propose the architecture of two DAA systems based on frequency-modulated continuous-wave (FMCW) radars operating in the radiolocalization bands at 9.5 GHz and 24 GHz. The performance of both onboard systems was validated through the probability of detection $P_d$ for different intruder categories, meeting the MOP in accordance with the RTCA DO-366A, DO-396, and ASTM F3442 standards. Full article

Spaceborne interrupted frequency-modulated continuous-wave synthetic aperture radar (iFMCW SAR) employs a single antenna on a single spacecraft operating in a time-division transmit/receive mode, effectively avoiding mutual interference between transmitted and received signals and thereby overturning the design paradigm of spaceborne FMCW SAR systems. However, the periodic switching of the antenna between transmit and receive states results in periodic data gaps along the azimuth direction in the echo signal, leading to spurious artifacts in the reconstructed images and severely degrading image quality. Sparse signal recovery techniques based on compressive sensing models have been shown to effectively suppress such spurious targets. Nevertheless, the generalized orthogonal matching pursuit (GOMP) algorithm requires prior knowledge of the signal sparsity, a condition that is often impractical in real-world scenarios. To address this limitation, this paper investigates the variation pattern of the residual norm with respect to sparsity in the GOMP algorithm and proposes a modified GOMP algorithm based on binary search. This approach enables rapid and accurate determination of the true sparsity level without prior knowledge, thereby achieving sparsity-adaptive reconstruction with GOMP and significantly enhancing the imaging quality of iFMCW SAR. Simulation experiments involving both point and scene targets are provided to demonstrate the effectiveness and potential of the proposed algorithms for practical applications. Full article

Sit-to-stand (STS) transitions are clinically informative indicators of functional independence and are sensitive to compensatory strategies associated with physical decline and pain. This study presents a non-contact, non-visual framework for quantitative STS assessment using a 60 GHz frequency-modulated continuous-wave (FMCW) radar in a residential setting. We developed a signal-processing pipeline that converts intermediate-frequency radar data into range–time intensity (RTI) maps, tracks dominant torso motion, and extracts temporal, kinematic, and spectral features. Experiments were conducted across two sensing orientations (subject-facing and side-facing), five mounting heights (45–153 cm), and three execution speeds, with approximately 30 repeated cycles per condition. For normal non-compensated STS transitions, radar-derived metrics reflected expected biomechanical scaling: mean full-cycle duration decreased from $23.90$ s (slow) to $13.95$ s (medium) and $7.98$ s (fast), while peak ascent velocity increased from $0.311$ m/s to $0.358$ m/s and dominant cadence increased from 0.0416 Hz to 0.125 Hz. Simulated abnormal transitions produced distinct and quantifiable deviations. Preparatory rocking introduced an additional oscillatory phase (mean rocking duration 2.36 s), prolonging the standing transition to 4.80 s and altering trajectory regularity. Across configurations, subject-facing mid-torso mounting provided the most continuous and separable STS signatures, whereas side-facing placement and extreme heights reduced effective radial motion or introduced clutter artifacts. These findings establish practical deployment guidelines and demonstrate that radar-derived STS metrics can serve as candidate digital biomarkers for unobtrusive, privacy-preserving detection of mobility decline, compensatory pain behaviors, and functional impairment in real-world home environments. Full article

Small drone-borne W-band synthetic aperture radar (SAR) systems are highly susceptible to motion errors that conventional navigation sensors and phase-based autofocus algorithms cannot effectively resolve due to phase wrapping. This paper presents a sensor-independent imaging framework to robustly suppress these errors. First, joint time-frequency analysis is employed to identify and discard motion-corrupted pulses. Subsequently, a compressed sensing-based backprojection algorithm reconstructs high-resolution images from the remaining sparse dataset. To alleviate the substantial memory burden of matrix-based compressed sensing, the reconstruction is reformulated iteratively. Experimental results confirm that the proposed method maintains structural integrity even when up to 60% of the received pulses are corrupted and demonstrates robust focusing down to an SNR of −25 dB. This approach provides a practical, memory-efficient, and cost-effective solution for SAR platforms. Full article

This paper presents a multimodal sensing approach for fine-grained soccer action recognition using synchronized mm-wave FMCW radar and multiview RGB cameras. A TI IWR1443BOOST FMCW radar and three Sony IMX296 global-shutter cameras were used to record seven soccer-related actions in different movement directions in an outdoor environment. Range–Doppler radar processing is applied to extract global mel features and CFAR-localized block representations of mel and radar spectrogram features to capture both coarse and fine micro-Doppler characteristics. Camera features are derived from bounding box, HOG, optical flow, and pose estimations. Classification is performed using logistic regression as the classical model and various deep models. Performance is evaluated using cross-validation. Radar alone achieved moderate performance (0.897 F1_macro using TCN), successfully identifying coarse motion but showing limited separability for dribbling-based actions. Camera-only models achieve near-perfect accuracy (≥0.997 F1_macro using 1D-CNN), with the confusion matrices being nearly perfectly diagonal already. The best performance is obtained from a cross-modal transformer with multiple cameras (0.998 F1_macro). These results demonstrate that a camera by itself performs strongly for the action recognition task but also that radar–camera fusion can improve robustness and enhance the discrimination of finer soccer player movements for outdoor analytics and player monitoring applications. Full article

In this paper, an improved state-space method (SSM) is proposed for gait feature extraction. By introducing zero-phase component analysis Whitening (ZCA Whitening) and an algorithm to search estimated echo as the preprocessing method, pedestrian echoes are divided into three groups according to the frequency probability density: torso, feet, and other segments. Two channels of echoes are selected as inputs to the SSM, which is employed to identify the corresponding micro-Doppler trajectory. On this basis, five gait features of torso amplitude, stride length, walking cycle, torso maximum speed, and feet maximum speed are extracted. Simulation based on the Boulic model, compared with the traditional SSM, demonstrated that there is no need to estimate the model order and that a more accurate torso micro-Doppler trajectory and effective micro-motion features of the feet can be obtained by the proposed method. Finally, 77 GHz FMCW radar was used to collect the echoes of four pedestrians. The classifier was designed based on a support vector machine (SVM), and the classification experiment verified the effectiveness of the extracted gait features. Full article

With ongoing advancements toward 6G networks, the terahertz (THz) band is expected to serve as an essential platform for realizing integrated sensing and communication (ISAC). In particular, maintaining high-data-rate communication while ensuring highly responsive, real-time radar operation in dynamic environments is a critical requirement. This study presents a THz-band ISAC architecture that utilizes a high-speed wavelength-tunable laser for photomixing, enabling simultaneous generation of a fast frequency-swept frequency-modulated continuous-wave (FMCW) radar signal and amplitude-shift keying (ASK) communication. The wavelength-tunable laser enables sub-microsecond frequency sweeps and supports high repetition rates suitable for real-time operation. To address the limitations in waveform design efficiency in conventional time-division ISAC, we experimentally investigate two transmission strategies for simultaneous operation. The first is a frequency-division scheme that reduces mutual interference between radar and communication signals, and the second is a joint-waveform scheme in which both functions share the same THz carrier. Using a single THz transmitter, the proposed system achieves sub-centimeter ranging accuracy together with 15-Gbit/s data transmission. These findings demonstrate that the presented ISAC approach enables efficient integration of radar and communication functions while lowering overall system complexity and implementation cost, offering substantial potential for deployment in future 6G infrastructures. Full article

Autonomous vehicles rely on complex sensing systems to perceive their environment and ensure safe operation. This review analyses the main sensor technologies used in self-driving vehicles, including cameras, LiDAR, radar, ultrasonic sensors and GNSS/IMU-based localisation systems. A core set of 40 primary research articles was systematically analysed to compare the capabilities, limitations and integration challenges of sensing technologies used in autonomous vehicles. In addition to these primary studies, further references were included to provide background information and describe emerging developments in autonomous sensing systems. The review shows that no single sensor technology can provide reliable perception under all environmental conditions. Camera systems offer rich visual information but are sensitive to lighting and weather conditions, while LiDAR provides highly accurate three-dimensional geometry but suffers from signal attenuation in rain and fog. Radar sensors demonstrate superior robustness in adverse weather and enable direct velocity measurement, although their spatial resolution remains limited compared to optical sensors. As a result, modern autonomous vehicles rely on multi-sensor fusion architectures that combine complementary sensing modalities to improve reliability and safety. The analysis also identifies several key research gaps in the current literature. In particular, there is a lack of systematic evaluation of trade-offs between sensor performance, computational requirements and vehicle energy consumption. Furthermore, the safety certification of artificial intelligence-based perception systems and the integration of emerging technologies such as FMCW LiDAR and terahertz radar remain open research challenges. Overall, the results suggest that the future of autonomous vehicle perception will depend not only on improvements in individual sensors but also on robust sensor fusion architectures, safety-certified AI models and energy-efficient sensor processing platforms. These findings provide guidance for researchers and engineers developing next-generation sensing systems for autonomous driving. Full article

Lightweight multi-rotor unmanned aerial vehicles (UAVs) have shown great potential in flexible Earth observation, but they impose strict restrictions on payload, volume, and power consumption. Traditional pulse-Doppler synthetic aperture radar (SAR) systems offer high imaging performance but suffer from high peak power and large volume, making them unsuitable for lightweight UAV platforms. To meet the low-power demand, most existing lightweight UAV SAR systems adopt frequency-modulated continuous-wave (FMCW) schemes, which are compact and low cost yet limited by a low range resolution, poor anti-interference ability, and single imaging modes. Therefore, it is urgent to develop an SAR system that combines the high performance of pulse radar with the lightweight advantage of FMCW radar. To this end, this paper proposes a compact, low-power Ka-band pulse-Doppler SAR system for multi-rotor UAVs. With 1.2 GHz bandwidth and highly integrated RF and antenna design, the system achieves miniaturization and low power consumption while maintaining high-resolution imaging capability. Furthermore, two-step waveform error correction and a signal predistortion method are presented to compensate amplitude and phase errors and improve the purity of the transmitted signal. Experimental results show that the proposed system can obtain clear SAR images with a resolution better than 0.3 m, providing a practical high-performance pulse-SAR solution for lightweight UAV platforms. Full article

(1) Background: Hospitalized preterm infants often require months of vital signs monitoring in the neonatal intensive care unit. Today, wired sensors are essential for survival, but are associated with numerous disadvantages including sensor dislocations, skin trauma and hygiene risks. Non-contact vital sign monitoring would therefore represent a significant improvement in the care of hospitalized neonates. (2) Objective: This study aims to lay the foundation for non-contact radar-based monitoring of the respiratory rate, which could be used in the neonatal intensive care unit. (3) Methods: We developed a radar-based vital parameter monitoring system for recording the respiratory rate of premature infants in a pediatric incubator. The novel system employs a four-channel I/Q FMCW radar with compact, application-specific antennas optimized to cover the defined area of interest on the infant’s thorax. As a proof-of-principle study, the system was tested in six anesthetized newborn piglets. (4) Results: Using the radar-based system, thorax movements were detected and the respiratory rate was calculated. We observed a high accordance between the signals of respiration detected by the novel radar sensor with the signals of the cable-bound monitor in resting piglets. (5) Conclusions: The novel radar sensor is suited for measuring respiration in the piglet model. In future, the sensor should be optimized in order to improve its robustness against disturbances body movements and in order to allow detection of heartbeat. Full article

Accurate absolute pressure measurement is of great importance in industrial control, environmental monitoring, and aerospace. Traditional fiber-optic Fabry–Perot (F-P) pressure sensors usually involve complex microfabrication and high-cost demodulation systems, while conventional diaphragm capsule sensors are limited in sensitivity and resolution. This work presents a low-cost, high-resolution fiber-optic F-P absolute pressure sensor. The sensor uses a vacuum capsule as one reflective surface and a partially reflective fiber collimator as the other, forming a low-finesse F-P interferometer. The cavity length is linearly modulated by the elastic deformation of the capsule under pressure, and high-precision demodulation is realized using frequency modulated continuous wave (FMCW) interferometry instead of conventional spectral methods. Static experiments from 10 to 110 kPa show that the sensor exhibits a high sensitivity of 15,105 nm/kPa and a resolution of 3.3 Pa. Furthermore, the sensor operates normally within the range of −20 °C to 70 °C, exhibiting a pressure–temperature cross-sensitivity of 0.081 kPa/°C and a cavity length drift of 496 nm/h. With the advantages of high performance, simple structure, low cost, and good scalability by selecting different capsules, the proposed sensor has promising potential for practical applications in pressure measurement fields. Full article

In frequency-modulated continuous-wave (FMCW) radar systems, the detection of Doppler-extended targets (DETs) is a critical challenge. The micro-Doppler effects induced by the motion of extended targets such as pedestrians cause the echo energy to spread along the Doppler dimension. As a result, a single range-Doppler cell is unlikely to form a pronounced amplitude peak above the background noise level. Consequently, existing constant false alarm rate (CFAR) methods that rely on single-cell amplitude decisions tend to suffer from performance degradation in DET scenarios and exhibit limited adaptability under varying clutter conditions. To solve these issues, we propose a nonlinear transform–based variability index CFAR detector for DET (DET-NTVI-CFAR), with the aim of improving detection probability and maintaining stable false alarm control in complex clutter backgrounds. This work constructs a detection statistic by applying a nonlinear transform to the accumulated power cells and derives the threshold from the corresponding probability distribution model. A variability index CFAR (VI-CFAR) decision strategy is introduced to select the appropriate detection branch under different operating conditions. In the threshold design stage, the false alarm probability expressions of three sub-detection methods are derived to guide the selection of threshold parameters. Simulation results demonstrate that the proposed method achieves stable false alarm control and improves detection probability in various environments. Field test results also confirm the applicability of the DET-NTVI-CFAR detector. Full article

Gait is a sensitive marker of mobility decline and fall risk, motivating unobtrusive sensing methods that can extract spatiotemporal parameters outside specialized gait laboratories. This paper presents a physics-based comparison of two millimeter-wave frequency-modulated continuous-wave (FMCW) radar deployment paradigms using a low-cost, system-on-chip (SoC) 60 GHz Infineon BGT60TR13C radar sensor: (i) a fixed (tripod-mounted) corridor observer and (ii) a shoe-mounted body-centric configuration attached to the medial side of the left shoe. Four healthy adult author-participants performed repeated 30 s corridor trials under five gait styles (regular, slow, fast, simulated festination, and simulated freezing-of-gait), including brief pauses during turns; an empty-corridor recording was acquired to characterize static clutter. Step events were detected using peak-picking on foot-related velocity envelopes with adaptive thresholds, and step count, cadence, step time, and step-time variability were derived. Performance of the fixed and shoe-mounted configurations was quantitatively compared to video ground truth using mean absolute percentage error (MAPE) for step count estimation. Across all gait styles, the shoe-mounted FMCW radar consistently reduced step-count error relative to the fixed corridor-mounted configuration, with the largest gains under irregular patterns (e.g., festination: 37.1% fixed vs. 9.6% shoe-mounted). These findings highlight the advantages of body-centric millimeter-wave radar sensing and support low-cost SoC radar as a pathway toward wearable, privacy-preserving gait monitoring in real-world environments. Full article