Search Results (78)

With the evolution of 6G technology toward global coverage and multidimensional integration, OTFS modulation has become a research focus due to its advantages in high-mobility scenarios. However, existing OTFS signal detection algorithms face challenges such as pilot contamination, Doppler spread degradation, and diverse interference in complex environments. This paper proposes the SPARSE-OTFS-Net algorithm, which establishes a comprehensive signal detection solution by innovatively integrating sparse random pilot design, compressive sensing-based frequency offset estimation with closed-loop cancellation, and joint denoising techniques combining an autoencoder, residual learning, and multi-scale feature fusion. The algorithm employs deep learning to dynamically generate non-uniform pilot distributions, reducing pilot contamination by 60%. Through orthogonal matching pursuit algorithms, it achieves super-resolution frequency offset estimation with tracking errors controlled within 20 Hz, effectively addressing Doppler spread degradation. The multi-stage denoising mechanism of deep neural networks suppresses various interferences while preserving time-frequency domain signal sparsity. Simulation results demonstrate: Under large frequency offset, multipath, and low SNR conditions, multi-kernel convolution technology achieves significant computational complexity reduction while exhibiting outstanding performance in tracking error and weak multipath detection. In 1000 km/h high-speed mobility scenarios, Doppler error estimation accuracy reaches ±25 Hz (approaching the Cramér-Rao bound), with BER performance of 5.0 × 10⁻⁶ (7× improvement over single-Gaussian CNN’s 3.5 × 10⁻⁵). In 1024-user interference scenarios with BER = 10⁻⁵ requirements, SNR demand decreases from 11.4 dB to 9.2 dB (2.2 dB reduction), while maintaining EVM at 6.5% under 1024-user concurrency (compared to 16.5% for conventional MMSE), effectively increasing concurrent user capacity in 6G ultra-massive connectivity scenarios. These results validate the superior performance of SPARSE-OTFS-Net in 6G ultra-massive connectivity applications and provide critical technical support for realizing integrated space–air–ground networks. Full article

Wind shear at the Hong Kong International Airport (HKIA) poses a significant safety risk due to terrain-induced airflow disruptions near the runways. Accurate assessment is essential for safeguarding aircraft during take-off and landing, as abrupt changes in wind speed or direction can compromise flight stability. This study introduces a hybrid framework for short-term wind shear prediction based on data collected from Doppler LiDAR systems positioned near the central and south runways of the HKIA. These systems provide high-resolution measurements of wind shear magnitude along critical flight paths. To predict wind shear more effectively, the proposed framework integrates a signal processing technique with a deep learning strategy. It begins with optimized variational mode decomposition (OVMD), which decomposes the wind shear time series into intrinsic mode functions (IMFs), each capturing distinct temporal characteristics. These IMFs are then modeled using bidirectional gated recurrent units (BiGRU), with hyperparameters optimized via the Tree-structured Parzen Estimator (TPE). To further enhance prediction accuracy, residual errors are corrected using Extreme Gradient Boosting (XGBoost), which captures discrepancies between the reconstructed signal and actual observations. The resulting OVMD–BiGRU–XGBoost framework exhibits strong predictive performance on testing data, achieving R² values of 0.729 and 0.926, RMSE values of 0.931 and 0.709, and MAE values of 0.624 and 0.521 for the central and south runways, respectively. Compared with GRUs, LSTM, BiLSTM, and ResNet-based baselines, the proposed framework achieves higher accuracy and a more effective representation of multi-scale temporal dynamics. It contributes to improving short-term wind shear prediction and supports operational planning and safety management in airport environments. Full article

With the rapid development of lightweight unmanned aerial vehicles (UAVs), the combination of UAVs and ground-moving target indication (GMTI) radar systems has received great interest. However, because of size, weight, and power (SWaP) limitations, the UAV may not be able to equip a highly accurate inertial navigation system (INS), which leads to reduced accuracy in the moving target relocation. To solve this issue, we propose using an image registration algorithm, which matches a Doppler beam sharpening (DBS) image of detected moving targets to a synthetic aperture radar (SAR) image containing coordinate information. However, when using conventional SAR image registration algorithms such as the SAR scale-invariant feature transform (SIFT) algorithm, additional difficulties arise. To overcome these difficulties, we developed a new image-matching algorithm, which first estimates the errors of the UAV platform to compensate for geometric distortions in the DBS image. In addition, to showcase the relocation improvement achieved with the new algorithm, we compared it with the affine transformation and second-order polynomial algorithms. The findings of simulated and real-world experiments demonstrate that our proposed image transformation method offers better moving target relocation results under low-accuracy INS conditions. Full article

Horizontal-axis turbines have been well-studied; however, there is a serious lack of information on the behaviour of vertical-axis turbines under unsteady operating conditions. Among unsteady flows, waves can cause significant mechanical fatigue and modify the flow downstream of the tidal turbines. Consequently, this paper aims to characterize the effects of waves on the hydrodynamic performance and wake development of a 1/20 scale model of a ducted twin vertical axis 1 MW-rated demonstrator. Power measurements were taken from the turbine and the velocity measurements downstream of the machine using a three-component Laser Doppler Velocimeter. The results show that, in the presence of waves, the mean wake characteristics present greater average height and width compared to the current-only condition. Moreover, the wake recovery happens faster downstream due to the sheared wake region homogenization, induced by the presence of higher intensity vortices. Through the Turbulence Kinetic Energy estimation, we also observe some increased fluctuations around the turbine and close to the free surface due to the presence of waves. Full article

Over the past decade, several studies based on coupled ocean–atmosphere simulations have shown that the oceanic surface current feedback to the atmosphere (CFB) leads to a slow-down of the mean oceanic circulation and, overall, to the so-called eddy killing effect, i.e., a sink of kinetic energy from oceanic eddies to the atmosphere that damps the oceanic mesoscale activity by about 30%, with upscaling effects on large-scale currents. Despite significant improvements in the representation of western boundary currents and mesoscale eddies in numerical models, some discrepancies remain when comparing numerical simulations with satellite observations. These discrepancies include a stronger wind and wind stress response to surface currents and a larger air–sea kinetic energy flux from the ocean to the atmosphere in numerical simulations. However, altimetric gridded products are known to largely underestimate mesoscale activity, and the satellite observations operate at different spatial and temporal resolutions and do not simultaneously measure surface currents and wind stress, leading to large uncertainties in air–sea mechanical energy flux estimates. ODYSEA is a new satellite mission project that aims to simultaneously monitor total surface currents and wind stress with a spatial sampling interval of 5 km and 90% daily global coverage. This study evaluates the potential of ODYSEA to measure surface winds, currents, energy fluxes, and ocean–atmosphere coupling coefficients. To this end, we generated synthetic ODYSEA data from a high-resolution coupled ocean–wave–atmosphere simulation of the Gulf Stream using ODYSIM, the Doppler scatterometer simulator for ODYSEA. Our results indicate that ODYSEA would significantly improve the monitoring of eddy kinetic energy, the kinetic energy cascade, and air–sea kinetic energy flux in the Gulf Stream region. Despite the improvement over the current measurements, the estimates of the coupling coefficients between surface currents and wind stress may still have large uncertainties due to the noise inherent in ODYSEA, and also due to measurement capabilities related to wind stress. This study evidences that halving the measurement noise in surface currents would lead to a more accurate estimation of the surface eddy kinetic energy and wind stress coupling coefficients. Since measurement noise in surface currents strongly depends on the square root of the transmit power of the Doppler scatterometer antenna, noise levels can be reduced by increasing the antenna length. However, exploring other alternatives, such as the use of neural networks, could also be a promising approach. Additionally, the combination of wind stress estimation from ODYSEA with other satellite products and numerical simulations could improve the representation of wind stress in gridded products. Future efforts should focus on the assessment of the potential of ODYSEA in quantifying the production of eddy kinetic energy through horizontal energy fluxes and air–sea energy fluxes related to divergent and rotational motions. Full article

With the advancement of wireless communication technology, the significance of efficient and accurate channel estimation methods has grown substantially. Recently, deep learning-based methods are being adopted to estimate channels with higher precision than traditional methods, even in the absence of prior channel statistics. In this paper, we propose two deep learning-based channel estimation models, CAMPNet and MSResNet, which are designed to consider channel characteristics from a multiscale perspective. The convolutional attention and multiscale parallel network (CAMPNet) accentuates critical channel characteristics by utilizing parallel multiscale features and convolutional attention, while the multiscale residual network (MSResNet) integrates information across various scales through cross-connected multiscale convolutional structures. Both models are designed to perform robustly in environments with complex frequency domain information and various Doppler shifts. Experimental results demonstrate that CAMPNet and MSResNet achieve superior performance compared to existing channel estimation methods within various channel models. Notably, the proposed models show exceptional performance in high signal-to-noise ratio (SNR) environments, achieving up to a 48.98% reduction in mean squared error(MSE) compared to existing methods at an SNR of $25\mathrm{dB}$. In experiments evaluating the generalization capabilities of the proposed models, they show greater stability and robustness compared to existing methods. These results suggest that deep learning-based channel estimation models have the potential to overcome the limitations of existing methods, offering high performance and efficiency in real-world communication environments. Full article

Extremely severe cyclonic storms over the North Indian Ocean increased by approximately 10% during the past 30 years. The climatological characteristics of tropical cyclones for 38 years were assessed over the Bay of Bengal (BoB). A total of 24 ESCSs formed over the BoB, having their genesis in the southeast BoB, and the intensity and duration of these storms have increased in recent times. The Advanced Research version of the Weather Research and Forecasting (ARW) model is utilized to simulate the five extremely severe cyclonic storms (ESCSs) over the BoB during the past two decades using the Indian Monsoon Data Assimilation and Analysis (IMDAA) data. The initial and lateral boundary conditions are derived from the IMDAA datasets with a horizontal resolution of 0.12° × 0.12°. Five ESCSs from the past two decades were considered: Sidr 2007, Phailin 2013, Hudhud 2014, Fani 2019, and Amphan 2020. The model was integrated up to 96 h using double-nested domains of 12 km and 4 km. Model performance was evaluated using the 4 km results, compared with the available observational datasets, including the best-fit data from the India Meteorological Department (IMD), the Tropical Rainfall Measuring Mission (TRMM) satellite, and the Doppler Weather Radar (DWR). The results indicated that IMDAA provided accurate forecasts for Fani, Hudhud, and Phailin regarding the track, intensity, and mean sea level pressure, aligning well with the IMD observational datasets. Statistical evaluation was performed to estimate the model skills using Mean Absolute Error (MAE), the Root Mean Square Error (RMSE), the Probability of Detection (POD), the Brier Score, and the Critical Successive Index (CSI). The calculated mean absolute maximum sustained wind speed errors ranged from 8.4 m/s to 10.6 m/s from day 1 to day 4, while mean track errors ranged from 100 km to 496 km for a day. The results highlighted the prediction of rainfall, maximum reflectivity, and the associated structure of the storms. The predicted 24 h accumulated rainfall is well captured by the model with a high POD (96% for the range of 35.6–64.4 mm/day) and a good correlation (65–97%) for the majority of storms. Similarly, the Brier Score showed a value of 0.01, indicating the high performance of the model forecast for maximum surface winds. The Critical Successive Index was 0.6, indicating the moderate model performance in the prediction of tracks. It is evident from the statistical analysis that the performance of the model is good in forecasting storm structure, intensity and rainfall. However, the IMDAA data have certain limitations in predicting the tracks due to inadequate representation of the large-scale circulations, necessitating improvement. Full article

Background/Objectives: The aim of this study is to assess the inter- and intra-observer reproducibility of the identification of direct and indirect ultrasonographic features of adenomyosis as defined by the revised Morphological Uterus Sonographic Assessment (MUSA) consensus (2022). Methods: A cohort of 74 women, aged 18 to 45, were recruited from the recurrent miscarriage and general gynaecology clinic at a university-based fertility centre. All the participants underwent 2D and 3D transvaginal Ultrasound scan (TVS) examination in the late follicular and early luteal phase. Conventional grey scale and power Doppler image volumes were acquired and stored. Subsequently, the stored 3D ultrasound images were independently re-evaluated offline by the two observers for the direct and indirect features of adenomyosis as outlined by the revised MUSA group. The intra- and the inter-observer reproducibility was estimated using Cohen’s Kappa coefficient. Results: The intra- and interobserver reproducibility (K −0.27, 95% CI 0.06–0.48 and K 0.13, 95% CI −0.10–0.37, respectively) for at least one direct feature of adenomyosis was only modest. Amongst the individual direct features, the interobserver variability of identifying myometrial cysts was fair (K 0.21, 95% CI −0.00–0.42), whereas the intra-observer variability was moderate (K 0.44, 95% CI 0.26–0.63). While hyperechogenic islands identification achieved a fair level of intra- (K 0.31, 95% CI 0.09–0.53) and interobserver (K 0.24, 95% CI 0.01–0.47) agreement, the reproducibility of reporting sub-endometrial lines/buds was fair for the intra-observer (K 0.22, 95% CI −0.02 0.47) and poor for the interobserver (K 0.00, 95% CI −0.20–0.19). The interobserver agreement for indirect features varied from poor to moderate, while the intra-observer agreement ranged between poor to good. Conclusions: The reporting of adenomyosis using direct features suggested by the revised MUSA group consensus showed only modest interobserver and intra-observer agreement. The definitions of ultrasound features for adenomyosis need further refining to enhance the reliability of diagnosis criteria of adenomyosis. Full article

This paper focuses on detecting and tracking maneuvering weak targets in the range–Doppler (RD) plane with the track-before-detect (TBD) algorithm based on dynamic programming (DP). Traditional DP-TBD algorithms integrate target detection and tracking in their framework while searching the paths provided by a predefined model of the kinematic properties within the constraints allowed. However, both the approximate motion model used in the RD plane and the model mismatch caused when the target undergoes a maneuver can degrade the TBD performance sharply. To address these issues, this paper accurately describes the evolution of the RD equation based on Constant Acceleration (CA) and Coordinated Turn (CT) motion models with the process noise in the Cartesian coordinate system, and it also employs a recursive method to estimate the parameters in the equations for efficient energy accumulation and path searches. Facing the situation that targets energy accumulation during the DP iteration process will lead to an expansion of the target energy accumulation process. This paper designs a more efficient Optimization Function (OF) to inhibit the expansion effect, improve the resolution of the nearby targets, and increase the detection probability of the weak targets simultaneously. In addition, to search the trajectory more efficiently and accurately, we improved the process of DP multi-frame accumulation, thus reducing the computation amount of large-scale searches. Finally, the effectiveness of the proposed method for CA and CT motion target detection and tracking is verified by many of the simulation experiments that were conducted in this paper. Full article

Sea surface wind, waves, and currents are the three basic parameters that describe the dynamic process of sea surface, and they are coupled with each other. To more accurately describe large-scale ocean motion and extract the ocean dynamic parameters, we adopt the spaceborne Doppler measurement to estimate the radial Doppler velocity generated by the sea surface motion. Due to the presence of wind and waves, the Doppler spectrum will be formed, shifted and broadened. Pulse-pair phase interference is used to obtain the Doppler spectrum from the sea surface echo. We simulate the Doppler spectrum with different look geometry and ocean states in a spaceborne condition. In this paper, the Doppler centroid variations are estimated after reducing the platform Doppler velocity under different observation conditions. With the increase in wind speed, the measured Doppler shift increases, and the simulated Doppler centroid accuracy is estimated. In addition, the measurement error along the trace direction is at the maximum, and the error in the cross-track is the smallest. At moderate wind-wave conditions, the Doppler velocity offset can be less than 0.1 m/s. Full article

In this study, flow behavior in a meandering channel was investigated experimentally and numerically. The experiments were carried out for nine different cases on a channel consisting of 180° and 120° angle bends following successively. Measurements were conducted employing Acoustic Doppler Velocimetry (ADV) at 13 different points in the inner, middle, and outer bends of the sections located at significant bends along the channel. Depth-averaged velocity, velocity profiles, bed shear stress, and turbulence kinetic energy parameters were considered to understand the flow behavior in the meandering channel. A 1:1 scale numerical model of the experimental setup was generated using the Computational Fluid Dynamics (CFD) method through the verified FLOW-3D software (HYDRO 2022R1). It was found to be successful in estimating all parameters and was capable of investigating the flow behavior in the meandering channel. Additionally, a mesh independence study was performed, and four different turbulence models were compared. As a result, as the flow encountered the first meander in the channel, secondary flow occurred, and lateral momentum transfer took place. Therefore, velocity increased by approximately 30% from the first meander of 180° angles to the second meander of 120° angles. Therefore, the most critical zone was the inner bend of the 120-angle meander. Full article

Multi-rotor aircraft have the advantages of a simple structure, low cost, and flexible operation in the unmanned aerial vehicle (UAV) family, and have developed rapidly in recent years. Radar surveillance and classification of the growing number of multi-rotor aircraft has become a challenging problem due to their low-slow-small (LSS) characteristics. Estimation of the blade number is an important step in distinguishing LSS targets. However, most of the current research on micro-motion parameters estimation has focused on the analysis of rotational frequency, length, and the initial phase of blades with a prior of blade number, affecting its ability to identify LSS targets. In this article, a micro-motion parameters estimation method for multi-rotor targets without a prior is proposed. On the basis of estimating the flashing frequency of the blades, a validation function is constructed through spectral analysis to judge the number of blades, and then the rotational frequency is estimated. The blade length is calculated by estimating the maximum Doppler shift. Moreover, the variational mode decomposition (VMD)-based atomic scaling orthogonal matching pursuit (AS-OMP) method is jointly applied to estimate the blade length when suffering from the low PRF and insufficient SNR conditions. Extensive experiments on the simulated and measured data demonstrate that the proposed method outperforms robust micro-motion parameter estimation capability in low PRF and insufficient SNR conditions compared to the traditional time-frequency analysis methods. Full article

Polarization diversity has been widely used in maritime radars to improve target detection performance. Full utilization of the polarimetric characteristics of sea clutter is the key to designing effective polarimetric detectors. For high-resolution maritime radars, the HH-HV dual-polarization is an affordable and effective mode to monitor small targets, owing to the simple configuration of single-polarimetric transmit and dual-polarimetric reception and lower clutter powers at the HH and HV polarizations. Enlightened by the analytical coherent detector in compound-Gaussian clutter with inverse Gamma texture, this paper investigates dual-polarimetric coherent detection in dual-polarimetric compound-Gaussian clutter with independent inverse Gamma distributed textures. The analytical dual-polarimetric near-optimum coherent detector is derived, which is a fusion of the generalized likelihood ratio test linear threshold detectors (GLRT-LTDs) at the two polarizations. For short, it is referred to as the P-GLRT-LTD. It is proven that the P-GLRT-LTD is of constant false alarm rate with respect to the Doppler steering vector, scale parameters of textures, and speckle covariance matrices. Moreover, the thresholds of the P-GLRT-LTD are given analytically. Experiments using simulated sea clutter data with the estimated scale and shape parameters from the two measured intelligent pixel processing radar (IPIX) datasets and two measured IPIX datasets with test targets are made to compare P-GLRT-LTD with other existing dual-polarimetric coherent detectors. The results show that the P-GLRT-LTD attains the same detection performance as the existing best-performance detector. The P-GLRT-LTD has a lower computational cost than the existing best-performing one. Full article

In this paper, we explore several widely available software-defined radio (SDR) platforms that could be used for locating with the signal Doppler frequency (SDF) method. In the SDF, location error is closely related to the accuracy of determining the Doppler frequency shift. Therefore, ensuring high frequency stability of the SDR, which is utilized in the location sensor, plays a crucial role. So, we define three device classes based on the measured frequency stability of selected SDRs without and with an external rubidium clock. We estimate the localization accuracy for these classes for two scenarios, i.e., short- and long-range. Using an external frequency standard reduces the location error from 20 km to 30 m or 15 km to 2 m for long- and short-range scenarios, respectively. The obtained simulation results allowed us to choose an SDR with appropriate stability. The studies showed that using an external frequency standard is necessary for minimizing SDR frequency instability in the Doppler effect-based location sensor. Additionally, we review small-size frequency oscillators. For further research, we propose two location sensor systems with small size and weight, low power consumption, and appropriate frequency stability. In our opinion, the SDF location sensor should be based on the bladeRF 2.0 micro xA4 or USRP B200mini-i SDR platform, both with the chip-scale atomic clock CSAC SA.45s, which will allow for minor positioning errors in the radio emitters. Full article

Unmanned aerial vehicles (UAVs) can support low-cost, highly mobile communications while making it possible to establish dedicated terrestrial networks. To overcome the pointing error (PE) and beam misalignment of millimeter-wave large-scale multiple-input multiple-output/free-space optics (MIMO/FSO) caused by UAV jitter, a millimeter-wave massive MIMO/FSO hybrid beamforming method based on tensor train decomposition is proposed. This approach is used for reconfigurable intelligent surface (RIS) network-assisted UAV millimeter-wave massive MIMO/FSO to improve system spectral efficiency. Firstly, the high-dimensional channel of RIS-assisted millimeter-wave massive MIMO/FSO in UAV is represented as a low-dimensional channel by tensor training decomposition. Secondly, the two-way gated recursive unit attention neural network model can effectively solve the FSO PE caused by UAV jitter, and the fast fading channel and Doppler frequency shift are estimated by the Fast Cyclic Tensor Power Method (FCTPM) based on tensor training decomposition. Finally, the RIS phase shift matrix is optimized by singular value decomposition. The hybrid beamforming and RIS phase-shift matrices were estimated by using the low-complexity phase extraction alternating minimization method to solve the beam misalignment problem. Simulation results show that by using the proposed method, the spectrum utilization rate is improved by 23.6% compared with other methods. Full article