Search Results (1,080)

With the rapid development of low-earth-orbit (LEO) internet satellite constellations, LEO satellites are becoming promising illuminators of opportunity for passive radar. However, the moving satellite platform results in a shifted Doppler frequency and increased Doppler spread of the clutter, leading to decreased clutter suppression performance. In this paper, the clutter model for a LEO satellite-based passive radar is analyzed. Based on the properties of the clutter, a Doppler-frequency-shifted normalized least mean square (LMS) filter is proposed to suppress the clutter. Furthermore, an efficient block adaptive method is introduced for fast implementation. Moreover, a Butterworth filter is designed to filter out the residual clutter. Simulations demonstrate the effectiveness of the proposed method. Full article

This research provides a new systematic solution to the essential issue of self-noise interference in underwater acoustic sensing signals induced by unmanned surface vehicles (USVs) operating at sea. The self-noise pertains to the near-field interference noise generated by the growing diversity and volume of acoustic equipment utilized by USVs. The generating mechanism of self-noise is clarified, and a self-noise propagation model is developed to examine its three-dimensional coupling properties within spatiotemporal fluctuation environments in the time-frequency-space domain. On this premise, the YOLOv11 object identification framework is innovatively applied to the delay-Doppler (DD) feature maps of self-noise, thereby overcoming the constraints of traditional time-frequency spectral approaches in recognizing noise with delay spread and overlapping characteristics. A comprehensive comparison with traditional models like YOLOv8 and SSD reveals that the suggested delay-Doppler YOLO (DD-YOLO) algorithm attains an average accuracy of 87.0% in noise source identification. An enhanced denoising method, termed optimized time-frequency regularized overlapping group shrinkage (OTFROGS), is introduced, using structural sparsity alongside non-convex regularization techniques. Comparative experiments with traditional denoising methods, such as the normalized least mean square (NLMS) algorithm, wavelet threshold denoising (WTD), and the original time-frequency regularized overlapping group shrinkage (TFROGS), reveal that OTFROGS outperforms them in mitigating USV self-noise. This study offers a dependable technological approach for optimizing the performance of USV acoustic systems and proposes a theoretical framework and methodology applicable to different underwater acoustic sensing contexts. Full article

Range height indicator (RHI) scans are performed routinely by the long-range Doppler Light Detection and Ranging (LIDAR) systems at the Hong Kong International Airport (HKIA). This paper presents some novel observations of the airflow in the airport region by RHI scans that have not been reported in the literature in the past—namely, areas of reverse flow and vortex shedding in east to southeasterly winds; severe windshear in the easterly; a converging southerly flow; a descending northeasterly jet; and the undercutting of the southwesterly flow by sea breeze. Many of these flow features are associated with low-level windshear, supported by pilot windshear reports, and thus their observations have practical applications. The technical feasibility of forecasting these airflow features is also studied in this paper, and it is found that large eddy simulations based on a mesoscale meteorology model manage to capture these wind features most of the time, but the simulated headwind change is generally slightly smaller than observed. The results in this paper have application value for windshear alerting and forecasting for an airport situated in an area of complex terrain, and they should be of interest for further studies of mountain meteorology. Full article

Maritime search and rescue (SAR) demands reliable sensing and communication under sea clutter. Emerging integrated sensing and communication (ISAC) technology provides new opportunities for the development and modernization of maritime radio communication, particularly in relation to search and rescue. This study investigated the dual-function capability of a phase-coded frequency modulated continuous wave (FMCW) system for search and rescue at sea, in particular for life signs detection in the presence of sea clutter. The detection capability of the FMCW system was enhanced by applying phase-modulated codes on chirps, and radar-centric communication function is supported simultaneously. Various phase-coding schemes including Barker, Frank, Zadoff-Chu (ZC), and Costas were assessed by adopting the peak sidelobe level and integrated sidelobe level of the ambiguity function of the established signals. The interplay of sea waves was represented by a compound K-distribution model. A multiple-input multiple-output (MIMO) architecture with the ZC code was adopted to detect multiple objects with a high resolution for micro-Doppler determination by taking advantage of spatial coherence with beamforming. The effectiveness of the proposed method was validated on the 4-transmit, 4-receive (4 × 4) MIMO system with ZC coded FMCW signals. Monte Carlo simulations were carried out incorporating different combinations of targets and user configurations with a wide range of signal-to-noise ratio (SNR) settings. Extensive simulations demonstrated that the mean squared error (MSE) of range estimation remained low across the evaluated SNR setting, while communication performance was comparable to that of a baseline orthogonal frequency-division multiplexing (OFDM)-based system. The high performance demonstrated by the proposed method makes it a suitable maritime search and rescue solution, in particular for vision-restricted situations. Full article

This study addresses the nonlinear estimation problem in the strapdown inertial navigation system (SINS) and Doppler velocity log (DVL) integrated navigation by proposing an improved filtering algorithm based on $S{E}_2(3)$ Lie group state representation. A dynamic model satisfying the group affine condition is established to systematically construct both left-invariant and right-invariant error state spaces, upon which two nonlinear filtering approaches are developed. Although the fast unscented transformation method is not novel by itself, its first integration with the $S{E}_2(3)$ Lie group model for SINS/DVL integrated navigation represents a significant advancement. Experimental results demonstrate that under large misalignment angles, the proposed method achieves slightly lower attitude errors compared to linear approaches, while also reducing position estimation errors during dynamic maneuvers. The 12,000 s endurance test confirms the algorithm’s stable long-term performance. Compared with conventional unscented Kalman filter methods, the proposed approach not only reduces computation time by 90% but also achieves real-time processing capability on embedded platforms through optimized sampling strategies and hierarchical state propagation mechanisms. These innovations provide an underwater navigation solution that combines theoretical rigor with engineering practicality, effectively overcoming the computational efficiency and dynamic adaptability limitations of traditional nonlinear filtering methods. Full article

Wind turbine radar echoes exhibit significant scattering power and Doppler spectrum broadening effects, which can interfere with the detection of meteorological targets and subsequently impact weather prediction and disaster warning decisions. In operational weather radar applications, the influence of wind farm on radar observations must be fully considered by meteorological departments and related institutions. In this paper, a Wind Turbine Clutter Classification Algorithm based on Random Forest (WTCDA-RF) classification is proposed. The level-II radar data is processed in blocks, and the spatial position invariance of wind farm clutter is leveraged for feature extraction. Samples are labeled based on position information, and valid samples are screened and saved to construct a vector sample set of wind farm clutter. Through training and optimization, the proposed WTCDA-RF model achieves an ACC of 90.92%, a PRE of 89.37%, a POD of 92.89%, and an F1-score of 91.10%, with a CSI of 83.65% and a FAR of only 10.63%. This not only enhances the accuracy of weather forecasts and ensures the reliability of radar data but also provides operational conditions for subsequent clutter removal, improves disaster warning capabilities, and ensures timely and accurate warning information under extreme weather conditions. Full article

Environmental perception is an essential task for autonomous driving, which is typically based on LiDAR or camera sensors. In recent years, 4D mm-Wave radar, which acquires 3D point cloud together with point-wise Doppler velocities, has drawn substantial attention owing to its robust performance under adverse weather conditions. Nonetheless, due to the high sparsity and substantial noise inherent in radar measurements, most radar perception studies are limited to object-level tasks, with point-level tasks such as semantic segmentation remaining largely underexplored. This paper aims to explore the possibility of using 4D radar in semantic segmentation. We set up the ZJUSSet dataset containing accurate point-wise class labels for radar and LiDAR. Then we propose a cross-modal distillation framework RaSS to fulfill the task. An adaptive Doppler compensation module is also designed to facilitate the segmentation. Experimental results on ZJUSSet and VoD dataset demonstrate that our RaSS model significantly outperforms the baselines and competitors. Code and dataset will be available upon paper acceptance. Full article

Background: Fetuin-A, a hepatokine implicated in metabolic regulation, has been associated with both metabolic syndrome and cardiovascular disease. However, its specific role in type 2 diabetes mellitus (T2DM) remains incompletely understood. Objective: This study aimed to investigate the relationship between fetuin-A levels and key components of metabolic syndrome (abdominal obesity, arterial hypertension, hyperglycemia, hypertriglyceridemia and low high-density lipoprotein cholesterol) as well as other cardiovascular risk markers, including metabolic dysfunction-associated fatty liver disease (MAFLD), carotid intima-media thickness (CIMT), and the Homeostatic Model Assessment of Insulin Resistance (HOMA-IR). Methods: A total of 51 patients with T2DM not receiving insulin therapy were enrolled. Participants underwent clinical, biochemical, and imaging evaluations. Hepatic steatosis was assessed via abdominal ultrasonography, and subclinical atherosclerosis was evaluated using CIMT measured with Doppler ultrasonography. Serum fetuin-A was quantified by ELISA. Results: Hepatic steatosis was significantly associated with metabolic syndrome, increased CIMT, and dyslipidemia (elevated total cholesterol, triglycerides, and reduced HDL cholesterol). Although no direct correlation was found between fetuin-A levels and hepatic steatosis, multivariate analysis revealed that fetuin-A concentrations were significantly influenced by total cholesterol and LDL cholesterol levels. Conclusions: Fetuin-A appears to be linked to lipid abnormalities in T2DM and may contribute to cardiovascular risk in this population. These findings support the potential utility of fetuin-A as a biomarker and possible therapeutic target for dyslipidemia management in diabetic patients. Full article

The present study aims to explore the technical feasibility of simulating in advance, and thus forecasting, the occurrence of low-level windshear at an airport in the complex terrain of the Qinghai–Tibet Plateau. Three cases of windshear at Xining Airport are investigated. They are related to synoptic scale subsidence flow and downward momentum transfer, terrain modification of a cold front, and wind convergence arising from synoptic- and mesoscale high-pressure areas. The simulation results are compared with actual Doppler LIDAR observations. It is found that the simulations reproduce the windshear features reasonably well. The low-level wind speed and/or wind direction convergence are clearly represented in the simulations. The simulated LIDAR radial velocity has a correlation coefficient of over 0.9 with the actual LIDAR radial velocity. Though the present study involves a limited number of cases, it is found to be feasible to use a high-resolution numerical weather prediction model to simulate low-level windshear at an airport on the plateau. It is hoped that this methodology could be extended from the Hong Kong International Airport to the plateau airports, and then to airports in other parts of the world. Full article

Currently, there is a lack of methods for detecting the mechanism of gas explosion propagation within flameproof enclosures and the dynamic behavior of flameproof enclosures under explosion impact. Therefore, this paper studies a method for detecting the vibration characteristics of coal mine explosion-proof equipment under internal gas explosions using laser Doppler. First, a model of gas explosion propagation and explosion transmission response in flameproof enclosures is established to reveal the mechanism of gas explosion transmission inside coal mine flameproof enclosures. Second, a laser Doppler measurement method for coal mine flameproof enclosures is proposed, along with a step-by-step progressive vibration characteristic analysis method. This begins with a single-frequency dimension analysis using the Fourier transform (FFT), extends to time–frequency joint analysis using the short-time Fourier transform (STFT) to incorporate a time scale, and then advances to a three-dimensional linkage of scale, time, and frequency using the wavelet transform (DWT) to solve the limitation of the fixed window length of the STFT, thereby achieving a dynamic characterization of the detonation response characteristics. Finally, a non-symmetric Gaussian impact load inversion model is constructed to validate the overall scheme. The experimental results show that the FFT analysis identified a 2000 Hz main frequency, along with the global frequency components of the flameproof enclosure vibration signal, the STFT analysis revealed the dynamic evolution of the 2000 Hz main frequency and global frequency over time, and the wavelet transform achieved higher accuracy positioning of the frequency amplitude in the time domain, with better time resolution. Finally, the experimental platform showed an error of less than 5% compared with the actual measured impact load, and the error between the inverted impact load and the actual load was less than 15%. The experimental platform is feasible, and the inversion model has good accuracy. The laser Doppler measurement method has significant advantages over traditional coal mine flameproof equipment measurement and analysis methods and can provide further failure analysis and prevention, design optimization, and safety performance evaluation of flameproof enclosures in the future. Full article

Multiwavelength modelling of the light variations in the chemically peculiar star CU Vir is presented. The modelling is based on the recent Doppler Imaging of CU Vir, which provides maps of the surface distribution of Si, Fe, He, and Cr. Intensity maps in both individual photometric filters and in the wide wavelength range from UV to NIR were calculated, taking into account the individual chemical abundances on the stellar surface. Comparison with observations revealed good agreement of both the light curves and their amplitude along the spectrum. Additionally, we analysed changes in the photometric period of the CU Vir from 1955 to 2022, including TESS measurements. The data of the last decades clearly indicate a gradual decrease in this period. Measurements of the CU Vir period over the next two decades will be crucial for verifying or refuting the periodic nature of its variations. Full article

Robust maritime radar object detection and tracking in maritime clutter environments is critical for maritime safety and security. Conventional Constant False Alarm Rate (CFAR) detectors have limited performance in processing complex-valued radar echoes, especially in complex scenarios where phase information is critical and in the real-time processing of successive echo pulses, while existing deep learning methods usually lack native support for complex-valued data and have inherent shortcomings in real-time compared to conventional methods. To overcome these limitations, we propose a dual-branch sequence feature fusion (DFF) detector designed specifically for complex-valued continuous sea-clutter signals, drawing on commonly used methods in video pattern recognition. The DFF employs dual parallel complex-valued U-Net branches to extract multilevel spatiotemporal features from distance profiles and Doppler features from distance–Doppler spectrograms, preserving the critical phase–amplitude relationship. Subsequently, the sequential feature-extraction module (SFEM) captures the temporal dependence in both feature streams. Next, the Adaptive Weight Learning (AWL) module dynamically fuses these multimodal features by learning modality-specific weights. Finally, the detection module generates the object localisation output. Extensive evaluations on the IPIX and SDRDSP datasets show that DFF performs well. On SDRDSP, DFF achieves 98.76% accuracy and 68.75% in F1 score, which significantly outperforms traditional CFAR methods and state-of-the-art deep learning models in terms of detection accuracy and false alarm rate (FAR). These results validate the effectiveness of DFF for reliable maritime object detection in complex clutter environments through multimodal feature fusion and sequence-dependent modelling. Full article

Prolonged sedentary behavior in office environments is a key risk factor for musculoskeletal disorders and metabolic health issues. While workplace stretching interventions can mitigate these risks, effective monitoring solutions are often limited by privacy concerns and constrained sensor placement. This study proposes a ceiling-mounted ultra-wideband (UWB) radar system for privacy-preserving classification of working and stretching postures in office settings. In this study, data were collected from ten participants in five scenarios: four posture classes (seated working, seated stretching, standing working, standing stretching), and empty environment. Distance and Doppler information extracted from the UWB radar signals was transformed into modality-specific images, which were then used as inputs to two classification models: ConcatFusion, a baseline model that fuses features by concatenation, and AttnFusion, which introduces spatial attention and convolutional feature integration. Both models were evaluated using leave-one-subject-out cross-validation. The AttnFusion model outperformed ConcatFusion, achieving a testing accuracy of 90.6% and a macro F1-score of 90.5%. These findings demonstrate the effectiveness of a ceiling-mounted UWB radar combined with attention-based modality fusion for unobtrusive office posture monitoring. The approach offers a privacy-preserving solution with potential applications in real-time ergonomic assessment and integration into workplace health and safety programs. Full article

Nowadays, guided acoustic waves (GAW) are used for many sensor and actuator applications. The use of numerical methods can facilitate the development and optimization process enormously. In this work, a universally applicable finite element method (FEM)-based model is introduced to determine the dispersion relations of guided acoustic waves. A 2-dimensional unit cell model with Floquet periodicity is used to calculate the corresponding band structure diagrams. Starting from a free plate the model is expanded to encompass single-sided fluid loading. Followed by a straightforward algorithm for post-processing, the data is presented. Additionally, a parametric optimizer is used to adapt the simulations to experimental data measured by a laser Doppler vibrometer on an aluminum plate. Finally, the accuracy of the FEM model is compared to two reference models, achieving good consistency. In the case of the fluid-loaded model, the behavior of critical interactions between the dispersion curves and model-based artifacts is discussed. This approach can be used to model 2D structures like phononic crystals, which cannot be simulated by common GAW models. Moreover, this method can be used as input for advanced multiphysics simulations, including acoustic streaming applications. Full article

To improve the unmanned aerial vehicle (UAV) detection and recognition rate based on radar detection technology, this paper proposes to take the radar range-Doppler planar graph that characterizes the echo information of the UAV as the input of the improved YOLOv8 network, uses the YOLOv8n-RFL network to detect and identify the UAV target. In the detection method of the UAV target, first, we detect the echo signal of the UAV through radar, and take the received echo model as the foundation, utilize the principle of generating range-Doppler planar data to convert the received UAV echo signals into range-Doppler planar graphs, and then, use the improved YOLOv8 network to train and detect the UAV target. In the detection algorithm, the range-Doppler planar graph is taken as the input of the YOLOv8n backbone network, the UAV target is extracted from the complex background through the C2f-RVB and C2f-RVBE modules to obtain more feature maps containing multi-scale UAV feature information; the shallow features from the backbone network and deep features from the neck network are integrated through the feature semantic fusion module (FSFM) to generate high-quality fused UAV feature maps with rich details and deep semantic information, and then, the lightweight sharing detection head (LWSD) is utilized to conduct unmanned aerial vehicle (UAV) feature recognition based on the generated fused feature map. By detecting the collected echo data of the unmanned aerial vehicle (UAV), it was found that the proposed improved algorithm can effectively detect the UAV. Full article