Search Results (131)

Ubiquitous digital array radar (UDAR) extends the integration time of moving targets by deploying a wide transmitting beam and multiple narrow receiving beams to cover the entire observed airspace. By exchanging time for energy, it effectively improves the detection ability for weak targets. Nevertheless, target motion introduces severe across-range unit (ARU), across-Doppler unit (ADU), and across-beam unit (ABU) effects, dispersing target energy across the range–Doppler-beam space. This paper proposes a beam domain angle rotation compensation and keystone-matched filtering (BARC-KTMF) algorithm to address the “three-crossing” challenge. This algorithm first corrects ABU by rotating beam–domain coordinates to align scattered energy into the final beam unit, reshaping the signal distribution pattern. Then, the KTMF method is utilized to focus target energy in the time-frequency domain. Furthermore, a special spatial windowing technique is developed to improve computational efficiency through parallel block processing. Simulation results show that the proposed approach achieves an excellent signal-to-noise ratio (SNR) gain over the typical single-beam and multi-beam long-time coherent integration (LTCI) methods under low SNR conditions. Additionally, the presented algorithm also has the capability of coarse estimation for the target incident angle. This work extends the LTCI technique to the beam domain, offering a robust framework for high-speed weak target detection. Full article

Hyperspectral technology has been widely applied to the retrieval of soil properties, such as soil organic matter (SOM) and particle size distribution (PSD). However, most previous studies have focused on hyperspectral data acquired from the nadir direction, and the influence of viewing geometry on hyperspectral-based soil property retrieval remains unclear. In this study, bidirectional reflectance factors (BRFs) were collected at 48 different viewing angles for 154 soil samples with varying SOM contents and PSDs. SOM and PSD were then retrieved using combinations of ten spectral preprocessing methods (raw reflectance, Savitzky–Golay filter (SG), first derivative (D1), second derivative (D2), standard normal variate (SNV), multiplicative scatter correction (MSC), SG + D1, SG + D2, SG + SNV, and SG + MSC), one sensitive wavelength selection method, and three retrieval algorithms (partial least squares regression (PLSR), support vector machine (SVM), and convolutional neural networks (CNNs)). The influence of viewing geometry on the selection of spectral preprocessing methods, retrieval algorithms, sensitive wavelengths, and retrieval accuracy was systematically analyzed. The results showed that soil BRFs are influenced by both soil properties and viewing angles. The viewing geometry had limited effects on the choice of preprocessing method and retrieval algorithm. Among the preprocessing methods, D1, SG + D1, and SG + D2 outperformed the others, while PLSR achieved a higher accuracy than SVM and CNN when retrieving soil properties. The selected sensitive wavelengths for both SOM and PSD varied slightly with viewing angle and were mainly located in the near-infrared region when using BRFs from multiple viewing angles. Compared with single-angle data, multi-angle BRFs significantly improved retrieval performance, with the R² increasing by 11% and 15%, and RMSE decreasing by 16% and 30% for SOM and PSD, respectively. The optimal viewing zenith angle ranged from 10° to 20° for SOM and around 40° for PSD. Additionally, backward viewing directions were more favorable than forward directions, with the optimal viewing azimuth angles being 0° for SOM and 90° for PSD. These findings provide useful insights for improving the accuracy of soil property retrieval using multi-angle hyperspectral observations. Full article

Low-cost MEMS sensors are widely utilized in UAV platforms to address attitude estimation problems due to their compact size, low power consumption, and cost-effectiveness. Diverse UAV payloads pose new challenges for attitude estimation, such as magnetic interference environments and high dynamic environments. In this paper, we propose a hierarchical decoupled attitude estimation algorithm, termed HDAEA. Initially, a novel hierarchical decoupling approach is introduced for the attitude and angle representation of the direction cosine matrix, enabling the representation of angles in a new manner. This method reduces the data dimensionality and nonlinearity of observation equations. Furthermore, a magnetic interference identification algorithm is proposed to compute the magnetic interference intensity accurately and quantitatively. Combining the quantified errors of estimated state variables, an error model for magnetic interference and attitude angles in high-dynamic environments is constructed. Subsequently, the proposed error model is employed to calibrate the hierarchical decoupled angles using accelerometer and magnetometer measurements, effectively mitigating the impact of magnetic interference on the calculation of pitch angles and roll angles. Moreover, the integration of the proposed hierarchical decoupled attitude estimation algorithm with the error-state extended Kalman filter reduces system nonlinearity and minimizes linearization errors. Experimental results demonstrate that HDAEA exhibits significantly improved attitude estimation accuracy of UAV payloads. Full article

This paper presents a rectifying metasurface (RMS) that enables wide-angle, polarization-insensitive wireless energy harvesting in the Wi-Fi frequency range. The RMS consists of a metasurface integrated with rectifying diodes, a low-pass filter (LPF), and a resistive load. In the structural design, the RMS incorporates four Schottky diodes placed on the bottom structure and connected to the top structure through four metallized vias. This configuration facilitates impedance matching between the metasurface and the diodes, omitting the need for conventional rectifier circuits or external matching networks and removing the impact of soldering variations. A 3 × 3 RMS prototype was manufactured and subjected to experimental validation. The measurements confirm that the RMS achieves a peak RF-to-DC conversion efficiency of 68.3% at 5.8 GHz with a 12.5 dBm input power, while maintaining stable performance across a wide range of incident angles and polarization states. Full article

Rogue waves are reported to wreck ships and claim lives. The prompt detection of their presence is difficult due to their small footprint and unpredictable emergence. The retrieval of sea surface height via remote sensing techniques provides a viable solution for detecting rogue waves. However, conventional synthetic aperture radar (SAR) techniques are ineffective at retrieving the surface height profile of rogue waves in real time due to nonlinearity between surface height and normalized radar cross-section (NRCS), which is not obvious in the absence of rogue waves. In this work, a cross-track interferometric SAR (XTI-SAR) imaging approach is proposed to detect elusive rogue waves over a wide area, with sea-surface profiles embedding rogue waves simulated using a probability-based model. The performance of the proposed imaging approach is evaluated in terms of errors in the position and height of rogue-wave peaks, the footprint area of rogue waves, and a root-mean-square error (RMSE) of the sea-surface height profile. Different rogue-wave events under different wind speeds are simulated, and the reconstructed height profiles are analyzed to determine the proper ranges of look angle, baseline, and mean-filter size, among other operation variables, in detecting rogue waves. The proposed approach is validated by simulations in detecting a rogue wave at a spatial resolution of 3 m × 3 m and height accuracy of decimeters. Full article

The Global Navigation Satellite System (GNSS)/Inertial Navigation System (INS) integrated navigation technology is widely utilized in vehicle positioning. However, in complex environments such as urban canyons or tunnels, GNSS signal outages due to obstructions lead to rapid error accumulation in INS-only operation, with error growth rates reaching 10–50 m per min. To enhance positioning accuracy during GNSS outages, this paper proposes an error compensation method based on CNN-BiLSTM-Attention. When GNSS signals are available, a mapping model is established between specific force, angular velocity, speed, heading angle, and GNSS position increments. During outages, this model, combined with an improved Kalman filter, predicts pseudo-GNSS positions and their covariances in real-time to compute an aided navigation solution. The improved Kalman filter integrates Sage–Husa adaptive filtering and strong tracking Kalman filtering, dynamically estimating noise covariances to enhance robustness and address the challenge of unknown pseudo-GNSS covariances. Real-vehicle experiments conducted in a city in Jiangsu Province simulated a 120 s GNSS outage, demonstrating that the proposed method delivers a stable navigation solution with a post-convergence positioning accuracy of 0.7275 m root mean square error (RMSE), representing a 93.66% improvement over pure INS. Moreover, compared to other deep learning models (e.g., LSTM), this approach exhibits faster convergence and higher precision, offering a reliable solution for vehicle positioning in GNSS-denied scenarios. Full article

Biosensors based on the surface plasmon resonance (SPR) technique are widely used for analyte detection due to their high selectivity and real-time detection capabilities. However, conventional SPR spectrum analysis can be affected by experimental noise and environmental variations, reducing the accuracy of results. To address these limitations, this study presents the development of an open-source computational tool to optimize SPR biosensor characterization, implemented using MATLAB App Designer (Version R2024b). The tool enables the importation of experimental data, application of different smoothing methods, and integration of traditional and hybrid approaches to enhance accuracy in determining the resonance angle. The proposed tool offers several innovations, such as integration of both traditional and hybrid (angle vs wavelength) analysis modes, implementation of four advanced curve smoothing techniques, including Gaussian filter, Savitzky–Golay, smoothing splines, and EWMA, as well as a user-friendly graphical interface supporting real-time data visualization, experimental data import, and result export. Unlike conventional approaches, the hybrid framework enables multidimensional optimization of SPR parameters, resulting in greater accuracy and robustness in detecting resonance conditions. Experimental validation demonstrated a marked reduction in spectral noise and improved consistency in resonance angle detection across conditions. The results confirm the effectiveness and practical relevance of the tool, contributing to the advancement of SPR biosensor analysis. Full article

The jet-type self-cleaning screen filter integrates industrial jet-cleaning technology into the self-cleaning process of screen filters in the drip irrigation system, which has the advantages of low water consumption, high cleaning capacity, and wide applicability compared to traditional filters. However, its commercialization faces challenges as the optimal jet cleaning mode and optimization method have not been determined. This study proposes a framework that combines computational fluid dynamics (CFD), artificial neural networks (ANN), and genetic algorithms (GA) for optimizing jet-cleaning parameters to improve the performance. The results show that, among the main influencing parameters of the nozzle, the incident section diameter d and the V-groove half angle β have the most significant effects on the peak wall shear stress, action area, and water consumption for cleaning. The ANN has a higher accuracy in predicting the performance (R² = 0.9991, MAE = 9.477), and it can effectively replace the CFD model for predicting the jet-cleaning performance and optimizing the parameters. The optimization resulted in a 1.34% reduction in the peak wall shear stress, a 16.82% reduction in cleaning water consumption, and a 7.6% increase in the action area for the optimal model compared to the base model. The optimization framework combining CFD, ANN, and GA can provide an optimal cleaning parameter scheme for jet-type self-cleaning screen filters. Full article

Intercropping is a key cultivation strategy for safeguarding national food and oil security. Accurate early-stage yield prediction of intercropped soybeans is essential for the rapid screening and breeding of high-yield soybean varieties. As a widely used technique for crop yield estimation, the accuracy of 3D reconstruction models directly affects the reliability of yield predictions. This study focuses on optimizing the 3D reconstruction process for intercropped soybeans to efficiently extract canopy structural parameters throughout the entire growth cycle, thereby enhancing the accuracy of early yield prediction. To achieve this, we optimized image acquisition protocols by testing four imaging angles (15°, 30°, 45°, and 60°), four plant rotation speeds (0.8 rpm, 1.0 rpm, 1.2 rpm, and 1.4 rpm), and four image acquisition counts (24, 36, 48, and 72 images). Point cloud preprocessing was refined through the application of secondary transformation matrices, color thresholding, statistical filtering, and scaling. Key algorithms—including the convex hull algorithm, voxel method, and 3D α-shape algorithm—were optimized using MATLAB, enabling the extraction of multi-dimensional canopy parameters. Subsequently, a stepwise regression model was developed to achieve precise early-stage yield prediction for soybeans. The study identified optimal image acquisition settings: a 30° imaging angle, a plant rotation speed of 1.2 rpm, and the collection of 36 images during the vegetative stage and 48 images during the reproductive stage. With these improvements, a high-precision 3D canopy point-cloud model of soybeans covering the entire growth period was successfully constructed. The optimized pipeline enabled batch extraction of 23 canopy structural parameters, achieving high accuracy, with linear fitting R² values of 0.990 for plant height and 0.950 for plant width. Furthermore, the voxel volume-based prediction approach yielded a maximum yield prediction accuracy of R² = 0.788. This study presents an integrated 3D reconstruction framework, spanning image acquisition, point cloud generation, and structural parameter extraction, effectively enabling early and precise yield prediction for intercropped soybeans. The proposed method offers an efficient and reliable technical reference for acquiring 3D structural information of soybeans in strip intercropping systems and contributes to the accurate identification of soybean germplasm resources, providing substantial theoretical and practical value. Full article

State-of-the-art research on narrow-linewidth external cavity semiconductor lasers has provided limited discussion on the capability of continuous wavelength tuning. In this study, we present a 780 nm tunable external cavity diode laser (ECDL) with narrow linewidth. An angle-adjustable interference filter (IF) is employed as the mode-selection element, enabling a wide wavelength tuning range. Precise, mode-hop-free continuous tuning is achieved through a combination of current modulation and piezoelectric ceramic transducer (PZT) control, with a tuning accuracy of 1.65 pm/mA. Experimental optimization of the interference filter external cavity diode laser (IF-ECDL) operating conditions resulted in a narrow linewidth of 55 kHz and a high output power of 51 mW. Furthermore, by integrating current and PZT tuning, continuous wavelength tuning of the IF-ECDL output is demonstrated over a specified range. Full article

With the development of piezoelectric-on-insulator (POI) substrates, X-cut LiNbO₃ thin-film resonators with interdigital transducers are widely investigated due to their adjustable resonant frequency (f_s) and effective electromechanical coupling coefficient ($K_{eff}^2$). This paper presents an in-depth study of simulations and measurements of laterally excited bulk acoustic wave resonators based on an X-cut LiNbO₃/SiO₂/Si substrate and a LiNbO₃ thin film to analyze the effects of electrode angle rotation ($\theta$) on the modes, f_s, and $K_{eff}^2$. The rotated $\theta$ leads to different electric field directions, causing mode changes, where the resonators without cavities are longitudinal leaky SAWs (LLSAWs, $\theta$ = 0$°$) and zero-order shear horizontal SAWs (SH₀-SAWs, $\theta$ = 90$°$) and the resonators with cavities are zero-order-symmetry (S₀) lateral vibrating resonators (LVRs, $\theta$ = 0$°$) and SH₀ plate wave resonators (PAW, $\theta$ = 90$°$). The resonators are fabricated based on a 400 nm X-cut LiNbO₃ thin-film substrate, and the measured results are consistent with those from the simulation. The fabricated LLSAW and SH₀-SAW without cavities show a $K_{eff}^2$ of 1.62% and 26.6% and a Bode-Q_max of 1309 and 228, respectively. Meanwhile, an S₀ LVR and an SH₀-PAW with cavities present a $K_{eff}^2$ of 4.82% and 27.66% and a Bode-Q_max of 3289 and 289, respectively. In addition, the TCF with a different rotated $\theta$ is also measured and analyzed. This paper systematically analyzes resonators on X-cut LiNbO₃ thin-film substrates and provides potential strategies for multi-band and multi-bandwidth filters. Full article

LIDAR technology is widely used in autonomous driving and environmental sensing, but its accuracy is significantly affected by variations in vehicle surface reflectivity. This study models and predicts the impact of different LIDAR sensor specifications and vehicle surface paints on laser intensity measurements. Laser intensity data from the experiments of Shung et al. were analyzed alongside vehicle color, angle, and distance. Multiple machine learning models were tested, with Gaussian Process Regression (GPR) performing best (RMSE = 0.87451, R² = 0.99924). To enhance the model’s physical interpretability, laser intensity values were correlated with LIDAR optical power equations, and curve fitting was applied to refine the relationship. The model was validated using the input parameters from Shung et al.’s experiments, comparing predicted intensity values with reference measurements. The results show that the model achieves an overall accuracy of 99% and is successful in laser intensity prediction. To assess real-world performance, the model was tested on the CUPAC dataset, which includes various traffic and weather conditions. Spatial filtering was applied to isolate laser intensities reflected only from the vehicle surface. The highest accuracy, 98.891%, was achieved for the SW-Gloss (White) surface, while the lowest accuracy, 98.195%, was recorded for the SB-Matte (Black) surface. The results confirm that the model effectively predicts laser intensity across different surface reflectivity conditions and remains robust across different channels LIDAR systems. Full article

To enhance the positioning accuracy of autonomous underwater vehicles (AUVs), a new adaptive filtering algorithm (RHAUKF) is proposed. The most widely used filtering algorithm is the traditional Unscented Kalman Filter or the Adaptive Robust UKF (ARUKF). Excessive noise interference may cause a decrease in filtering accuracy and is highly likely to result in divergence by means of the traditional Unscented Kalman Filter, resulting in an increase in uncertainty factors during submersible mission execution. An estimation model for system noise, the adaptive Unscented Kalman Filter (UKF) algorithm was derived in light of the maximum likelihood criterion and optimized by applying the rolling-horizon estimation method, using the Newton–Raphson algorithm for the maximum likelihood estimation of noise statistics, and it was verified by simulation experiments using the Lie group inertial navigation error model. The results indicate that, compared with the UKF algorithm and the ARUKF, the improved algorithm reduces attitude angle errors by 45%, speed errors by 44%, and three-dimensional position errors by 47%. It can better cope with complex underwater environments, effectively address the problems of low filtering accuracy and even divergence, and improve the stability of submersibles. Full article

This paper presents a novel approach to address the challenge of trajectory tracking control in unmanned vehicles (UVs) operating on roads with varying ad hesion coefficients and subject to wind disturbances. The proposed method integrates real-time estimation of road adhesion coefficients with a wind disturbance model, embedding both into the trajectory tracking control system. Specifically, the Unscented Kalman Filter (UKF) is employed to dynamically estimate the road adhesion coefficients. These estimates are then combined with the wind disturbance model and incorporated into a Model Predictive Control (MPC) framework, enhancing the robustness and accuracy of trajectory tracking under varying conditions. The integrated simulation results from CarSim/Simulink demonstrate high accuracy in road adhesion coefficient estimation for trajectory tracking control, with tracking errors consistently remaining below 0.1 across different road adhesion conditions. For example, the road adhesion coefficient estimation error for the docking surface is 0.5%, and for the split road surface, it is 0.75%, indicating the approach’s applicability across a wide range of road conditions. Notably, when the wind speed reaches level-five intensity and a shelter-type vehicle is used as a test case, the proposed controller significantly outperforms the controller that does not consider wind effects. Under both high and low adhesion conditions, the controller reduces the yaw angle by approximately 15% and 30%, respectively, and the yaw rate by approximately 25% and 20%. These results provide strong evidence for the effectiveness of the proposed trajectory tracking controller in enhancing the stability and control performance of unmanned vehicles. Full article

The accurate estimation of flow angles is crucial for enhancing flight performance and aircraft safety. Flow angles of fixed-wing small unmanned aerial vehicles (SUAVs) are more vulnerable due to their low airspeed. Current flow angle measurement devices have not been widely implemented in SUAVs due to their substantial cost and size constraints. Moreover, there are no general estimation methods suitable for SUAVs based on their rudimentary sensor suite. This study presents a generalized optimization-assisted filter estimation (OAFE) method for estimating the relative velocity and flow angles of fixed-wing SUAVs based on a standard sensor suite. This OAFE method mainly consists of a cubature Kalman filter and an optimizer. The filter serves as the main loop with which to generate flow angles in real time by fusing the acceleration, angular rate, attitude, and airspeed. Without flow angle measurements, the optimizer generates approximate aerodynamic derivatives, which serve as pseudo-measurements with which to refine the performance of the filter. The results demonstrate that the estimated angle of attack and side slip angle displayed root mean square errors of around 0.11° and 0.24° in the simulation. The feasibility was also verified in field tests. The OAFE method does not require flow angle measurements, the prior acquisition of aerodynamic parameters, or model training, making it suitable for quick deployment on different SUAVs. Full article