Search Results (24)

This paper proposes a micro-Doppler flash detection framework for hovering unmanned aerial vehicle (UAV) detection with linear frequency modulated continuous wave (LFMCW) radar under the dual constraints of strong ground clutter and severe thermal noise conditions. In such scenarios, conventional methods fail not only due to the spectral overlap between hovering targets and clutter but also because of the visual disappearance of micro-Doppler features under heavy noise. The framework consists of three sequential modules. A prior-template orthogonal projection (PTOP) module suppresses clutter via a single-step orthogonal projection, preserving the micro-Doppler flash signature without distortion while approximately maintaining the Gaussian noise statistics required for subsequent detection. A flash power spectrum construction module then collapses the periodic blade flash energy onto a sharp spectral peak in a one-dimensional (1D) power spectrum via Gabor transform, power projection, and fast Fourier transform (FFT). A cell-averaging constant false alarm rate (CA-CFAR) detection module with an analytically derived threshold factor finally renders a reliable detection decision. Simulations under a signal-to-clutter ratio (SCR) of $-21$ dB and signal-to-noise ratio (SNR) of $-23$ dB confirm that the proposed framework achieves reliable detection even when the micro-Doppler flash signatures are visually obscured by residual noise in the time–frequency domain. Parametric SNR sweep curves and a two-dimensional (2D) SCR–SNR detection-probability heatmap under a non-stationary clutter model further quantify the practical performance boundaries of the framework. By transforming these concealed periodic features into a sharp spectral peak, the framework provides robust detection performance where conventional range-Doppler and moving target indication (MTI)-based methods both exhibit severe performance degradation. Full article

Background: Systemic sclerosis (SSc) is characterized by endothelial dysfunction leading to progressive vascular injury and fibrosis. While microvascular involvement is well established as an early disease feature, macrovascular disease has been historically underrecognized and poorly investigated in very early disease stages. Integrated assessments across the SSc spectrum, including very early diagnosis of systemic sclerosis (VEDOSS), remain limited. Methods: In this cross-sectional observational study, patients with established SSc, VEDOSS, and primary Raynaud’s phenomenon (PRP) were prospectively enrolled between October 2023 and April 2025. Participants underwent comprehensive microvascular and macrovascular evaluation, including nailfold videocapillaroscopy, multisegmental arterial Doppler ultrasound (carotid, aortic, and lower limb districts), flow-mediated dilation, and measurement of endothelial biomarkers (vascular cell adhesion molecule 1 (VCAM-1), intercellular adhesion molecule 1 (ICAM-1), and circulating endothelial cells (CECs)). Traditional cardiovascular risk was estimated using Systematic Coronary Risk Estimation 2 (SCORE2). Results: Sixty-two female subjects were included (34 SSc, 14 VEDOSS, and 14 PRP). Microvascular abnormalities followed the expected disease continuum, with capillaroscopic changes present in 57% of VEDOSS and 91% of SSc patients. Although SCORE2 estimates and carotid intima–media thickness were comparable across groups, macrovascular abnormalities were more frequent in SSc (52.9%) and VEDOSS (50%) compared with PRP (21.4%). VCAM-1, ICAM-1, and CEC levels were significantly increased in SSc compared with PRP, whereas no significant differences were observed between VEDOSS and PRP. Conclusions: These findings support a unified micro- and macro-vascular disease model in SSc and demonstrate that macrovascular involvement is detectable already in the VEDOSS phase. Conventional cardiovascular risk scores underestimate the true vascular burden, highlighting the need for disease-specific risk stratification tools integrating vascular imaging and endothelial biomarkers. Full article

Rotor-induced micro-Doppler signatures are essential for radar-based characterization of rotary-wing UAVs, but practical echoes are often dominated by a strong quasi-static body return concentrated near zero Doppler. In hovering or low-speed scenarios, rotor-induced components may intermittently overlap this near-zero region, where hard DC suppression discards informative rotor content and fragments micro-Doppler structures. Data-driven decompositions such as EMD and VMD avoid fixed cutoffs, yet without explicit constraints on rotor periodicity they are vulnerable to mode mixing and residual leakage under low-SNR conditions. This paper proposes a Cyclic-Power-Guided Soft Mask (CPGSM) framework that exploits cyclostationary periodicity as a physically grounded prior for rotor–body separation. A CPS-guided soft masking procedure consisting of a DC-dominant overlap band is first identified from quasi-static dominance; within this band, cyclic power spectrum analysis yields a continuous rotor-consistency score that guides smooth time–frequency soft allocation, while deterministic assignment is applied elsewhere. Simulations demonstrate improved micro-Doppler continuity, reduced body leakage, and more stable performance from 5–30 dB SNR compared with hard DC isolation and EMD/VMD, together with consistent rotor-speed estimates across sensing configurations. Full article

Determination of nozzle quality ratings based on macroscopic and microscopic parameters generally requires the use of separate measurement methods in research. The guiding idea determining the direction of the conducted research was to use a 2D (two-dimensional) laser analyzer LDA/PDA (laser Doppler anemometry/phase Doppler anemometry) to evaluate the values of selected micro and macro parameters (microstructure characterization with simultaneous evaluation of lateral distribution) of the spray. The research was conducted for variable measurement times. The main issue of the research was an attempt to reduce the measurement cycle time, important in the case of point tests performed with an analyzer. The scope of the conducted research covered three areas. In the first stage of the research, the variability of the coefficients characterizing the spray spectrum as a function of variable measurement time was analyzed. In the next, the value of the coefficient of transverse volume distribution (for a single sprayer) was determined. The results were determined on the basis of the volume diameters obtained from measuring the droplets with a 2D LDA/PDA analyzer. In the third stage, an attempt was made to combine the volume distribution results obtained for single nozzles on the boom. The results obtained were compared with those determined using a groove table. Both measurement methods used a different representativeness in volume measurement (sampling method and significantly different amounts of liquid analyzed); nevertheless, the results of the transverse volume distribution were found to be consistent. Full article

Micro-Doppler signatures play a crucial role in capturing target features for the radar classification task, and the time–frequency distribution method is widely used to represent micro-Doppler signatures in many applications including human activities, ground moving target identification, and different types of drones distinguishing. However, most existing studies that utilize radar micro-Doppler spectrograms often require extended observation times to effectively represent the cyclostationarity and periodic modulation of radar signals to achieve promising classification results. In addition, the presence of noise in real-world environments poses challenges by generating weak micro-Doppler features and a low signal-to-noise ratio (SNR), leading to a significant decline in classification accuracy. In this paper, we present a novel one-dimensional (1D) denoising and classification cascaded framework designed for low-resolution radar targets using a micro-Doppler spectrum. This framework provides an effective signal-based solution for feature extraction and recognition from the single-frame micro-Doppler spectrum in a conventional pulsed radar system, which boasts high real-time efficiency and low computation requirements under conditions of low resolution and a short dwell time. Specifically, the proposed framework is implemented using two cascaded subnetworks: Firstly, for radar micro-Doppler spectrum denoising, we propose an improved 1D DnCNN subnetwork to enhance noisy or weak micro-Doppler signatures. Secondly, an AlexNet subnetwork is cascaded for the classification task, and the joint loss is calculated to update the denoising subnetwork and assist with optimal classification performance. We have conducted a comprehensive set of experiments using six types of targets with a ground surveillance radar system to demonstrate the denoising and classification performance of the proposed cascaded framework, which shows significant improvement over separate training of denoising and classification models. Full article

Driver status plays a critical role in ensuring driving safety. However, the current visual recognition-based methods for detecting driver actions and status are often limited to factors such as ambient light condition, occlusion, and privacy concerns. In contrast, millimeter-wave radar offers various advantages such as high accuracy, ease of integration, insensitivity to light condition, and low cost; therefore, it has been widely used for monitoring vital signals and in action recognition. Despite this, the existing studies on driver action recognition have been hindered by limited accuracy and a narrow range of detectable actions. In this study, we utilized a 77 GHz millimeter-wave frequency-modulated continuous-wave radar to construct a dataset encompassing seven types of driver head–hand cooperative actions. Furthermore, a deep learning network model based on VGG16-LSTM-CBAM using micro-Doppler spectrograms as input was developed for action classification. The experimental results demonstrated that, compared to the existing CNN-LSTM and ALEXNET-LSTM networks, the proposed network achieves a classification accuracy of 99.16%, effectively improving driver action detection. Full article

This study conducted a classification analysis of hydrometeor types during a typical stratiform mixed cloud precipitation event in the rainy season using data from the Liupan Mountains micro rain radar power spectra. The primary research findings are as follows: (1) Utilizing the RaProM method synthesizes the information of particle falling velocity, equivalent radar reflection coefficient, particle scale characteristics at different stages, and the location of the bright zone in the zero-degree layer to classify hydrometeors during this precipitation process, and the results show that drizzle and raindrop distribution time periods do not match with the raindrop spectra and rain intensities observed by the DSG5 ground-based precipitation gauge. (2) Sensitivity experiments conducted on the RaProM method revealed that after modifying the discrimination thresholds for drizzle and raindrops, the distributions of drizzle and raindrops were more aligned with ground-based raindrop spectrum observations. Furthermore, these adjustments also showed better consistency with the radar reflectivity factor, Doppler velocity, and velocity spectrum width thresholds used by existing millimeter-wave cloud radars to discriminate between drizzle and raindrops. (3) Various kinds of hydrometeors show different vertical distribution characteristics in three precipitation stages: weak, strong, and weak. In the two weak precipitation stages, hydrometeors mainly existed in the form of snowflakes at altitudes above the zero-degree layer and in the form of drizzle at altitudes below the zero-degree layer. The vertical distribution disparity of hydrometeors between the mountain peak and base sites demonstrates that terrain significantly influences hydrometeors during the precipitation process. Full article

This research presents a novel fuzzy-logic-based algorithm aimed at detecting and removing interference lines from Micro Rain Radar (MRR-2) data. Interference lines, which are non-meteorological echoes with unknown origins, can severely obscure meteorological signals. Leveraging an understanding of interference line characteristics, such as temporal continuity, we identified and utilized eight key variables to distinguish interference lines from meteorological signals. These variables include radar moments, Doppler spectrum peaks, and the spatial/temporal continuity of Doppler velocity. The algorithm was developed and validated using data from MRR installations at three sites (Seoul, Suwon, and Incheon) in South Korea, from June to September 2021–2023. While there is a slight tendency to eliminate some weak precipitation, results indicate that the algorithm effectively removes interference lines while preserving the majority of genuine precipitation signals, even in complex scenarios where both interference and precipitation signals are present. The developed software, written in Python 3 and available as open-source, outputs in NetCDF4 format, with customizable parameters for user flexibility. This tool offers a significant contribution to the field, facilitating the accurate interpretation of MRR-2 data contaminated by interference. Full article

Human action recognition based on optical and infrared video data is greatly affected by the environment, and feature extraction in traditional machine learning classification methods is complex; therefore, this paper proposes a method for human action recognition using Frequency Modulated Continuous Wave (FMCW) radar based on an asymmetric convolutional residual network. First, the radar echo data are analyzed and processed to extract the micro-Doppler time domain spectrograms of different actions. Second, a strategy combining asymmetric convolution and the Mish activation function is adopted in the residual block of the ResNet18 network to address the limitations of linear and nonlinear transformations in the residual block for micro-Doppler spectrum recognition. This approach aims to enhance the network’s ability to learn features effectively. Finally, the Improved Convolutional Block Attention Module (ICBAM) is integrated into the residual block to enhance the model’s attention and comprehension of input data. The experimental results demonstrate that the proposed method achieves a high accuracy of 98.28% in action recognition and classification within complex scenes, surpassing classic deep learning approaches. Moreover, this method significantly improves the recognition accuracy for actions with similar micro-Doppler features and demonstrates excellent anti-noise recognition performance. Full article

Micro-Doppler time–frequency analysis has been regarded as an important parameter extraction method for conical micro-motion objects. However, the micro-Doppler effect caused by micro-motion can modulate the frequency of lidar echo, leading to coupling between structure and micro-motion parameters. Therefore, it is difficult to extract parameters for micro-motion cones. We propose a new method for parameter extraction by combining the range profile of a micro-motion cone and the micro-Doppler time–frequency spectrum. This method can effectively decouple and accurately extract the structure and the micro-motion parameters of cones. Compared with traditional time–frequency analysis methods, the accuracy of parameter extraction is higher, and the information is richer. Firstly, the range profile of the micro-motion cone was obtained by using an FMCW (Frequency Modulated Continuous Wave) lidar based on simulation. Secondly, quantitative analysis was conducted on the edge features of the range profile and the micro-Doppler time–frequency spectrum. Finally, the parameters of the micro-motion cone were extracted based on the proposed decoupling parameter extraction method. The results show that our method can effectively extract the cone height, the base radius, the precession angle, the spin frequency, and the gravity center height within the range of a lidar LOS (line of sight) angle from 20° to 65°. The average absolute percentage error can reach below 10%. The method proposed in this paper not only enriches the detection information regarding micro-motion cones, but also improves the accuracy of parameter extraction and establishes a foundation for classification and recognition. It provides a new technical approach for laser micro-Doppler detection in accurate recognition. Full article

A comprehensive understanding of the linear/nonlinear dynamic behavior of wireless microresonators is essential for micro-electromechanical systems (MEMS) design optimization. This study investigates the dynamic behaviour of a magnetoelectric (ME) microresonator, using a finite element method (FEM) and machine learning algorithm. First, the linear/nonlinear behaviour of a fabricated thin-film ME microactuator is assessed in both the time domain and frequency spectrum. Next, a data driven system identification (DDSI) procedure and simulated annealing (SA) method are implemented to reconstruct differential equations from measured datasets. The Duffing equation is employed to replicate the dynamic behavior of the ME microactuator. The Duffing coefficients such as mass, stiffness, damping, force amplitude, and excitation frequency are considered as input parameters. Meanwhile, the microactuator displacement is taken as the output parameter, which is measured experimentally via a laser Doppler vibrometer (LDV) device. To determine the optimal range and step size for input parameters, the sensitivity analysis is conducted using Latin hypercube sampling (LHS). The peak index matching (PIM) and correlation coefficient (CC) are considered assessment criteria for the objective function. The data-driven developed models are subsequently employed to reconstruct/predict mode shapes and the vibration amplitude over the time domain. The effect of driving signal nonlinearity and total harmonic distortion (THD) is explored experimentally under resonance and sub-resonance conditions. The vibration measurements reveal that as excitation levels increase, hysteresis variations become more noticeable, which may result in a higher prediction error in the Duffing array model. The verification test indicates that the first bending mode reconstructs reasonably with a prediction accuracy of about 92 percent. This proof-of-concept study demonstrates that the simulated annealing approach is a promising tool for modeling the dynamic behavior of MEMS systems, making it a strong candidate for real-world applications. Full article

The technology for human activity recognition has diverse applications within the Internet of Things spectrum, including medical sensing, security measures, smart home systems, and more. Predominantly, human activity recognition methods have relied on contact sensors, and some research uses inertial sensors embedded in smartphones or other devices, which present several limitations. Additionally, most research has concentrated on recognizing discrete activities, even though activities in real-life scenarios tend to be continuous. In this paper, we introduce a method to classify continuous human activities, such as walking, running, squatting, standing, and jumping. Our approach hinges on the micro-Doppler (MD) features derived from continuous-wave radar signals. We first process the radar echo signals generated from human activities to produce MD spectrograms. Subsequently, a bidirectional gate recurrent unit (Bi-GRU) network is employed to train and test these extracted features. Preliminary results highlight the efficacy of our approach, with an average recognition accuracy exceeding 90%. Full article

Spectrum aliasing occurs in signal echoes when the sampling frequency does not comply with the Nyquist Sampling Theorem. In this scenario, the extraction of micro-motion parameters becomes challenging. This paper proposes a spectral de-aliasing method for micro-motion signals based on a complex-valued U-Net network. Zero interpolation is employed to insert zeros into the echo, effectively increasing the sampling frequency. After zero interpolation, the micro-motion signal contains both real micro-motion signal frequency components and new frequency components. Short-Time Fourier Transform (STFT) is then applied to transform the zero-interpolated echo from the time domain to the time–frequency domain. Furthermore, a complex-valued U-Net training model is utilized to eliminate redundant frequency components generated by zero interpolation, thereby achieving the frequency reconstruction of micro-motion signal echoes. Finally, the training models are employed to process the measured data. The theoretical analysis, simulations, and experimental results demonstrate that this method is robust and feasible, and is capable of addressing the problem of micro-motion signal echo spectrum aliasing in narrowband radar. Full article

Micro-Doppler (m-D) analysis is the most effective mechanism for detecting rotating targets or components; however, it fails when the target rotation plane is perpendicular to the radar line of sight (LOS). The vortex electromagnetic wave (VEMW) provides a unconventional structure of wavefront phase modulation on the cross-plane of the radar LOS, on which the radial m-D vanishes while the rotational Doppler (RD) appears. In the absence of the position of rotation center, this paper focuses on the micromotion parameters estimation based on RD effect for rotating target, and then proposes an estimation procedure, referred to as the two-step method. The micromotion parameters of the rotating target include the rotation attitude, the rotation radius and the position of the rotation center while the latter is coupled to the former two. Firstly, the micromotion parameters are roughly estimated based on the RD curve parameters obtained from the time-frequency (TF) spectrum of the received signal. Secondly, the maximum likelihood estimation (MLE) is used to accurately estimate the micromotion parameters. In addition, the Cramér–Rao bound (CRB) of parameter estimation is derived. The simulation studies the influencing factors of estimation performance and verifies that the proposed estimation method can provide excellent estimation accuracy of the micromotion parameters. Full article

To address the dangerous driving behaviors prevalent among current car drivers, it is necessary to provide real-time, accurate warning and correction of driver’s driving behaviors in a small, movable, and enclosed space. In this paper, we propose a method for detecting dangerous behaviors based on frequency-modulated continuous-wave radar (mm-DSF). The highly packaged millimeter-wave radar chip has good in-vehicle emotion recognition capability. The acquired millimeter-wave differential frequency signal is Fourier-transformed to obtain the intermediate frequency signal. The physiological decomposition of the local micro-Doppler feature spectrum of the target action is then used as the eigenvalue. Matrix signal intensity and clutter filtering are performed by analyzing the signal echo model of the input channel. The signal classification is based on the estimation and variety of the feature vectors of the target key actions using a modified and optimized level fusion method of the SlowFast dual-channel network. Nine typical risky driving behaviors were set up by the Dula Hazard Questionnaire and TEIQue-SF, and the accuracy of the classification results of the self-built dataset was analyzed to verify the high robustness of the method. The recognition accuracy of this method increased by 1.97% compared with the traditional method. Full article