Search Results (42)

Visible Light Positioning (VLP), leveraging Light-Emitting Diodes (LEDs) and smartphone CMOS cameras, provides a high-precision solution for indoor localization. However, existing systems face challenges in accuracy, latency, and robustness due to line-of-sight (LOS) limitations and inefficient signal encoding. To overcome these constraints, this paper introduces a real-time VLP framework that integrates Multi-Pulse Position Modulation (MPPM) with smartphone multi-sensor fusion. By employing MPPM, a high-efficiency encoding scheme, the proposed system transmits LED identifiers (LED-IDs) with reduced inter-symbol interference, enabling robust signal detection even under dynamic lighting conditions and at extended distances. The smartphone’s camera is a receiver that decodes the MPPM-encoded LED-ID, while accelerometer and magnetometer data compensate for device orientation and motion-induced errors. Experimental results demonstrate that the MPPM-driven approach achieves a decoding success rate of over 97% at distances up to 2.4 m, while maintaining a frame processing rate of 30 FPS and sub-35 ms latency. Furthermore, the method reduces angular errors through sensor fusion, yielding 2D positioning accuracy below 10 cm and vertical errors under 16 cm across diverse smartphone orientations. The synergy of MPPM’s spectral efficiency and multi-sensor correction establishes a new benchmark for VLP systems, enabling scalable deployment in real-world environments without requiring complex infrastructure. Full article

High-dynamic airborne gravimetry faces critical challenges from platform-induced noise contamination. Conventional filtering methods exhibit inherent limitations in simultaneously achieving dynamic tracking capability and spectral fidelity. To overcome these constraints, this study proposes a Kalman–FIR fusion filtering (K-F) method, which is validated through engineering implementation on the GIPS-1A airborne gravimeter platform. The proposed framework employs a dual-stage strategy: (1) An adaptive state-space framework employing calibration coefficients (S_x, S_y, S_z) continuously estimates triaxial acceleration errors to compensate for gravity anomaly signals. This approach resolves aliasing artifacts induced by non-stationary noise while preserving low-frequency gravity components that are traditionally attenuated by conventional FIR filters. (2) A window-optimized FIR post-filter explicitly regulates cutoff frequencies to ensure spectral compatibility with downstream processing workflows, including terrain correction. Flight experiments demonstrate that the K-F method achieves a repeat-line internal consistency of 0.558 mGal at 0.01 Hz—a 65.3% accuracy improvement over standalone FIR filtering (1.606 mGal at 0.01 Hz). Concurrently, it enhances spatial resolution to 2.5 km (half-wavelength), enabling the recovery of data segments corrupted by airflow disturbances that were previously unusable. Implemented on the GIPS-1A system, K-F enables precision mineral exploration and establishes a noise-suppressed paradigm for extreme-dynamic gravimetry. Full article

Digital Beamforming SAR (DBF-SAR) provides high-resolution wide-swath imaging capability, yet it is affected by inter-channel amplitude, phase and time-delay errors induced by temperature variations and random error factors. Since all elevation channel data are weighted and summed by the DBF module in real time, conventional record-then-compensate approaches cannot meet real-time processing requirements. To resolve the problem, a real-time calibration architecture for Intermediate Frequency DBF (IFDBF) is presented in this paper. The Field-Programmable Gate Array (FPGA) implementation estimates amplitude errors through simple summation, time-delay errors via a simple counter, and phase errors via single-bin Discrete-Time Fourier Transform (DTFT). The time-delay and phase error information are converted into single-tone frequency components through Dechirp processing. The proposed method deliberately employs a reduced-length DTFT implementation to achieve enhanced delay estimation range adaptability. The method completes calibration within tens of PRIs (under 1 s). The proposed method is analyzed and validated through a spaceborne simulation and X-band 16-channel DBF-SAR experiments. Full article

In sectors that require large components with tight tolerances, the control of machine thermal deformations as a result of ambient temperature variations, motor consumption, and heating of moving components is essential. There are many alternatives for modelling and trying to compensate for this deformation, but structural components are rarely analysed independently to study their influence on positioning errors. This study analysed component temperature and deformation measurements using 49 thermocouples and 14 integral deformation sensors (IDS) installed on a large-scale machine tool. The effect of each heat source on component deformations was studied and those with a predominant effect were identified. The results can ease thermal effect prediction models development and new machine design process to maximise accuracy by focusing effort on the most critical components and most important heat sources. It was found that ambient temperature variations lead to greater but more uniform deformations than internal heat sources, reaching a 60% of total deformations with smaller temperature changes (8.7 °C, against 15–35 °C due to internal heat sources). These deformations are localized mainly in the machine bed (100 $\mu$m in X direction and 170 $\mu$m in the Y direction) and column (150 $\mu$m in the Z direction) and in the axis ball screw bearings (reaching 55 °C). Consequently, it is concluded that improving bearing and motor refrigeration could significantly reduce thermally-induced deformations. Full article

The Medium Earth Orbit (MEO) spaceborne Synthetic Aperture Radar (SAR) has great coverage ability, which can improve maritime ship target surveillance performance significantly. However, due to the huge computational load required for imaging processing and the severe defocusing caused by ship motions, traditional ship recognition conducted in focused image domains cannot process MEO SAR data efficiently. To address this issue, a multi-level focusing-classification strategy for MEO SAR ship recognition is proposed, which is applied to the range-compressed ship data domain. Firstly, global fast coarse-focusing is conducted to compensate for sailing motion errors. Then, a coarse-classification network is designed to realize major target category classification, based on which local region image slices are extracted. Next, fine-focusing is performed to correct high-order motion errors, followed by applying fine-classification applied to the image slices to realize final ship classification. Equivalent MEO SAR ship images generated by real LEO SAR data are utilized to construct training and testing datasets. Simulated MEO SAR ship data are also used to evaluate the generalization of the whole method. The experimental results demonstrate that the proposed method can achieve high classification precision. Since only local region slices are used during the second-level processing step, the complex computations induced by fine-focusing for the full image can be avoided, thereby significantly improving overall efficiency. Full article

This study addresses the limitations of conventional filtering methods in handling irregular outliers and missing observations, which can compromise filter robustness and accuracy. We propose the Transformer-based Outlier-Robust Kalman Filter (TORKF), a hybrid data and knowledge hybrid-driven framework for stochastic discrete-time systems. Initially, this study derives the filtering formulas applicable when outliers exist in observation vectors and, based on these formulations, proposes a novel method capable of accurately identifying observation vectors containing outliers. In addition, a transformer-based prediction compensation approach is employed to compute the prediction vector compensation value in scenarios involving outliers. This method utilizes a specially designed data structure to ensure the transformer encoder fully extracts the input features. Furthermore, to address outlier-induced inaccuracy in prediction error covariance, a compensation method aggregating all prediction outcomes is proposed, leading to enhanced filtering accuracy. Aircraft tracking presents challenges from complex motion models and outlier-prone observations, making it an ideal testbed for robust filtering algorithms. TORKF demonstrates superior performance, with a 12.7% lower RMSE than state-of-the-art methods across both propeller and jet datasets, while maintaining sub-90 ms single-frame processing to meet real-time requirements. Ablation studies confirm that all three proposed methods enhance accuracy and demonstrate synergistic improvements. Full article

Defects arising from the 3D printing process of continuous fiber-reinforced thermoplastic composites primarily hinder their overall performance. These defects particularly include twisting, folding, and breakage of the fiber bundle, which are induced by printing trajectory errors. This study presents a follow-up theory assumption to address such issues, elucidates the formation mechanism of printing trajectory errors, and examines the impact of key geometric parameters—trace curvature, nozzle diameter, and fiber bundle diameter—on these errors. An error model for printing trajectory is established, accompanied by the proposal of a trajectory error compensation method premised on maximum printable curvature. The presented case study uses CCFRF/PA as an exemplar; here, the printing layer height is 0.1~0.3 mm, the fiber bundle radius is 0.2 mm, and the printing speed is 600 mm/min. The maximum printing curvature, gauged by the printing trajectory of a clothoid, is found to be 0.416 mm⁻¹. Experimental results demonstrate that the error model provides accurate predictions of the printed trajectory error, particularly when the printed trajectory forms an obtuse angle. The average prediction deviations for line profile, deviation kurtosis, and deviation area ratio are 36.029%, 47.238%, and 2.045%, respectively. The error compensation effectively mitigates the defects of fiber bundle folding and twisting, while maintaining the printing trajectory error within minimal range. These results indicate that the proposed method substantially enhances the internal defects of 3D printed components and may potentially be applied to other continuous fiber printing types. Full article

High demand for 6G wireless has made photonics-aided D-band (110–170 GHz) communication a research priority. Photonics-aided technology integrates optical and wireless communications to boost spectral efficiency and transmission distance. This study presents a Radio-over-Fiber (RoF) communication system utilizing photonics-aided technology for 4600 m long-distance D-band transmission. We successfully show the transmission of a 50 Gbps (25 Gbaud) QPSK signal utilizing a 128.75 GHz carrier frequency. Notwithstanding these encouraging outcomes, RoF systems encounter considerable obstacles, including pronounced nonlinear distortions and phase noise related to laser linewidth. Numerous factors can induce nonlinear impairments, including high-power amplifiers (PAs) in wireless channels, the operational mechanisms of optoelectronic devices (such as electrical amplifiers, modulators, and photodiodes), and elevated optical power levels during fiber transmission. Phase noise (PN) is generated by laser linewidth. Despite the notable advantages of classical Volterra series and deep neural network (DNN) methods in alleviating nonlinear distortion, they display considerable performance limitations in adjusting for phase noise. To address these problems, we propose a novel post-processing approach utilizing a two-dimensional convolutional neural network (2D-CNN). This methodology allows for the extraction of intricate features from data preprocessed using traditional Digital Signal Processing (DSP) techniques, enabling concurrent compensation for phase noise and nonlinear distortions. The 4600 m long-distance D-band transmission experiment demonstrated that the proposed 2D-CNN post-processing method achieved a Bit Error Rate (BER) of 5.3 × 10⁻³ at 8 dBm optical power, satisfying the soft-decision forward error correction (SD-FEC) criterion of 1.56 × 10⁻² with a 15% overhead. The 2D-CNN outperformed Volterra series and deep neural network approaches in long-haul D-band RoF systems by compensating for phase noise and nonlinear distortions via spatiotemporal feature integration, hierarchical feature extraction, and nonlinear modelling. Full article

UAV-Enabled Corridor Photogrammetry is applied to survey linear transport infrastructure projects’ sites. The corridor flight missions cause a misalignment of the point cloud called the “bowl” effect. The purpose of this study is to offer a methodology based on statistical compensation methods to mitigate this effect and to improve the accuracy and density of the generated point cloud. The aerial images’ post-processing was carried out by varying the aerotriangulation methods. Subsequently, the accuracy improvement was completed by integrating the coordinates of the ground control points (GCPs) through different spatial distributions. Finally, Mean and RANSAC compensations were proposed to address the errors induced by the “bowl” effect on the coordinates of the images’ perspective centres (PCs). The findings indicate that the optimised aerotriangulation using Post-Processed Kinematic (PPK) data significantly contribute to reducing the “bowl” effect. Moreover, the GCP pyramidal spatial distribution allows accuracy improvement to a centimetre level. The Mean compensation method yields optimal outcomes in accuracy. It also helps to optimise on-site survey time and computing resources. RANSAC compensation optimises the accuracy and allows the retrieval of a 5-times-denser point cloud. Furthermore, the results give better accuracy compared to some current approaches. Full article

This article develops a new inductive displacement sensor with a segmented multi-group coil structure, which is suitable for the displacement measurement of control rods in nuclear reactors. Each group coil of the sensor consists of two excitation coils and one sensing coil. The excitation and sensing coils are segmented to extend the linearity range of the displacement sensor. It abandons the traditional sensor’s method of using nonlinear compensation to achieve large-stroke displacement measurement. Providing an alternating current (AC) signal to the excitation coil and processing the induced voltage generated by each sensing coil can directly achieve the high-precision measurement of core displacement. The mathematical model of the variations in the sensing coil voltage caused by the movement of the core is established. The impacts of the excitation coil structure, the number of turns of the excitation coil, and the excitation frequency on the output characteristics of the designed sensor are analyzed by finite element simulation. Based on the analysis and design, a sensor prototype is built and tested in the laboratory. The measurement results show that the linearity error is 0.35% and the maximum measuring error can be limited within 1.5 mm, which is sufficient to meet the practical requirements in a nuclear reactor environment. Full article

To address the manufacturing demands of large-scale aerospace composite components, this study systematically investigates the coordinated motion characteristics of multi-axis systems in fiber placement equipment. This investigation is based on the structural features and process specifications of the equipment. A comprehensive motion control scheme for grid-based fiber placement machines was developed using the Siemens 840D CNC system, integrating filament-winding and tape-laying functionalities on a unified control platform while enabling 10-axis synchronous motion. To mitigate thermal-induced errors, a compensation method incorporating a BP neural network optimized by a genetic algorithm with an enhanced fitness function (GA-BP) was proposed. Experimental results demonstrate significant improvements: the maximum thermal errors of the Z-axis and X3-axis were reduced by 36.7% and 53.3%, respectively, while the core mold placement time was reduced to 61% of the specified duration, with notable enhancements in trajectory accuracy and processing efficiency. This research provides a technical framework for the design of multi-axis cooperative control systems and thermal error compensation in automated fiber placement equipment, offering critical insights for advancing manufacturing technologies in aerospace composite applications. The proposed methodology highlights practical value in balancing precision, efficiency, and system integration for complex composite component production. Full article

This study presents a full-system simulation methodology for MEMS, addressing the critical need for reliable performance prediction in microsystem design. While existing digital tools have been widely adopted in related fields, current approaches often remain fragmented and focused on specific aspects of device behavior. In contrast, our proposed framework conducts comprehensive device physics-level analysis by integrating mechanical, thermal and electrical modeling with process simulation. The methodology features a streamlined workflow that enables direct implementation of simulation results into fabrication processes. We model a MEMS gyroscope as an example to verify our simulation approach. Multiphysics coupling is considered to capture real-world device behavior, followed by quantitative assessment of manufacturing variations through virtual prototyping and experimental validation demonstrating the method’s accuracy and practicality. The proposed approach not only improves design efficiency but also provides a robust framework for MEMS gyroscope development. With its ability to predict device performance, this methodology is expected to become an essential tool in microsensor research and development. Full article

To predict the variation in the displacement position of hydraulic cylinder piston rods, a neural network model is proposed to enhance the displacement control accuracy of hydraulic cylinders. The innovation of this paper is that by calculating the compressibility-induced flow loss of hydraulic fluid, mathematical models for both the internal and external leakage of hydraulic cylinders are established, identifying seven primary factors influencing piston rod displacement. Because there are many influencing factors and complex parameters different from traditional backpropagation (BP) neural network used in previous studies, this paper innovatively proposes a three-layer BP neural network ensemble model for predicting input–output characteristic curves of hydraulic cylinders. In the process of model improvement, a nonlinear adaptive decreasing weight mechanism is introduced to improve the optimization accuracy of the algorithm, facilitating the search for optimal solutions. The most reasonable weight and bias parameters are determined via the iterative training and testing of each BP neural network layer. This model enables the real-time prediction of piston rod displacement output curves after a specified time interval based on external input parameters. The predicted time is utilized to compensate for the response delays caused by directional valve switching and hydraulic fluid buffering, thereby enabling proactive displacement prediction. Validation results demonstrate that the maximum predicted displacement error is reduced to 0.5491 µm, with the model’s maximum runtime being 27.82 ms. The maximum allowable time allocated for directional valve switching and fluid buffering in the hydraulic system is extended to 74.57 ms, achieving the objective of enhancing both displacement control accuracy and operational efficiency. Full article

In order to solve the testing and verification problems at the early development stage of a high-speed Maglev positioning and speed measurement system (MPSS), a hardware-in-the-loop (HIL) simulation platform is presented, which includes induction loops, transmitting antennas, a power driver unit, a simulator based on a field-programmable gate array (FPGA), a host computer, etc. This HIL simulation platform simulates the operation of a high-speed Maglev train and generates the related loop-induced signals to test the performance of a real ground signal processing unit (GSPU). Furthermore, an absolute position detection method based on Gray-coded loops is proposed to identify which Gray-coded period the train is in. A relative position detection method based on height compensation is also proposed to calculate the exact position of the train in a Gray-coded period. The experimental results show that the positioning error is only 2.58 mm, and the speed error is 6.34 km/h even in the 600 km/h condition. The proposed HIL platform also effectively simulates the three kinds of operation modes of high-speed Maglev trains, which verifies the effectiveness and practicality of the HIL simulation strategy. This provides favorable conditions for the development and early validation of high-speed MPSS. Full article

Inertial navigation systems augmented with visual and wheel odometry measurements have emerged as a robust solution to address uncertainties in robot localization and odometry. This paper introduces a novel data-driven approach to compensate for wheel slippage in visual-inertial-wheel odometry (VIWO). The proposed method leverages Gaussian process regression (GPR) with deep kernel design and long short-term memory (LSTM) layers to model and mitigate slippage-induced errors effectively. Furthermore, a feature confidence estimator is incorporated to address the impact of dynamic feature points on visual measurements, ensuring reliable data integration. By refining these measurements, the system utilizes a multi-state constraint Kalman filter (MSCKF) to achieve accurate state estimation and enhanced navigation performance. The effectiveness of the proposed approach is demonstrated through extensive simulations and experimental validations using real-world datasets. The results highlight the ability of the method to handle challenging terrains and dynamic environments by compensating for wheel slippage and mitigating the influence of dynamic objects. Compared to conventional VIWO systems, the integration of GPR and LSTM layers significantly improves localization accuracy and robustness. This work paves the way for deploying VIWO systems in diverse and unpredictable environments, contributing to advancements in autonomous navigation and multi-sensor fusion technologies. Full article