Velocity Error Research Articles

A novel robust adaptive Kalman filter with application to urban vehicle integrated navigation systems

This study addresses the formidable challenge presented by non-Gaussian, time-varying, heavy-tailed process noise in integrated vehicle navigation systems. While robust adaptive filtering methods exist, their efficacy is constrained, and certain algorithms exhibit suboptimal usability due to an excess of a priori parameters. We deduce an adaptive robust Kalman filter, characterizing process noise with a Student's t distribution and measurement noise adhering to a Gaussian distribution. To enhance robustness against unknown or varying noise statistics, our method simultaneously estimates the scale matrix, degrees of freedom parameters, state vector, and measurement noise covariance through the expectation–maximization (EM) algorithm and Bayesian variational inference theory. Rigorous simulations and empirical experiments validate its superior accuracy and stability, requiring minimal a priori parameter information. Vehicle-based experiments further demonstrate positioning error below 2.78 m, velocity error below 0.34 m/s, and heading error less than 1.28° after achieving a steady state. This approach exhibits significant potential for advancing the precision and reliability of vehicle navigation systems.

Metaheuristic and Heuristic Algorithms-Based Identification Parameters of a Direct Current Motor

Direct current motors are widely used in industry applications, and it has become necessary to carry out studies and experiments for their optimization. In this manuscript, a comparison between heuristic and metaheuristic algorithms is presented, specifically, the Steiglitz–McBride, Jaya, Genetic Algorithm (GA), and Grey Wolf Optimizer (GWO) algorithms. They were used to estimate the parameters of a dynamic model that approximates the actual responses of current and angular velocity of a DC motor. The inverse of the Euclidean distance between the current and velocity errors was defined as the fitness function for the metaheuristic algorithms. For a more comprehensive comparison between algorithms, other indicators such as mean squared error (MSE), standard deviation, computation time, and key points of the current and velocity responses were used. Simulations were performed with MATLAB/Simulink 2010 using the estimated parameters and compared to the experiments. The results showed that Steiglitz–McBride and GWO are better parametric estimators, performing better than Jaya and GA in real signals and nominal parameters. Indicators say that GWO is more accurate for parametric estimation, with an average MSE of 0.43%, but it requires a high computational cost. On the contrary, Steiglitz–McBride performed with an average MSE of 3.32% but required a much lower computational cost. The GWO presented an error of 1% in the dynamic response using the corresponding indicators. If a more accurate parametric estimation is required, it is recommended to use GWO; however, the heuristic algorithm performed better overall. The performance of the algorithms presented in this paper may change if different error functions are used.

Critical Examination of 3D Building Modelling through UAV Frame and Video Imaging

Abstract. Data capture in UAV photogrammetry is carried out using two main methodologies: frame frame-based and video video-based. Frame Frame-based data gathering is the preferred method among UAV projects because to its inherent reliability in calibration. Nonetheless, circumstances involving moving objects or occlusions inside the measured region may produce unsatisfactory results utilizing this method. I In response to these challenges, video video-based data collecting appears as a potential option, capable of creating a series of successive images that together alleviate the constraints outlined above. In this study we aim to compare the usefulness of frame and video images in building 3D models, using both oblique and vertical image orientations. Rigorous evaluations produced many outputs, including dense point clouds, digital surface models (DSM), meshes, and orthophotographs. The evaluation criteria included da ta acquisition velocity, processing efficiency, calibration precision, distortion analysis, residual plots, scale correctness, and reprojection error. The empirical results demonstrated the advantages of video video-frame captures in improving the quality of the resulting 3D models. Notably, the use of video frames resulted in a significant reduction in reprojection error by 16%, calibration residuals by 36%, distortions by up to 51%, and processing time by 27%. Thus, it seems that integrating video frames improves data gathering accuracy and speeds up processing, replacing standard frame images with video counterparts for increased efficacy.

Numerical investigation of fluid flow behavior in steel cord with lattice Boltzmann methodology: The impacts of microstructure and loading force.

Steel cord materials were found to have internal porous microstructures and complex fluid flow properties. However, current studies have rarely reported the transport behavior of steel cord materials from a microscopic viewpoint. The computed tomography (CT) scanning technology and lattice Boltzmann method (LBM) were used in this study to reconstruct and compare the real three-dimensional (3D) pore structures and fluid flow in the original and tensile (by loading 800 N force) steel cord samples. The pore-scale LBM results showed that fluid velocities increased as displacement differential pressure increased in both the original and tensile steel cord samples, but with two different critical values of 3.3273 Pa and 2.6122 Pa, respectively. The original steel cord sample had higher maximal and average seepage velocities at the 1/2 sections of 3D construction images than the tensile steel cord sample. These phenomena should be attributed to the fact that when the original steel cord sample was stretched, its porosity decreased, pore radius increased, flow channel connectivity improved, and thus flow velocity increased. Moreover, when the internal porosity of tensile steel cord sample was increased by 1 time, lead the maximum velocity to increase by 1.52 times, and the average velocity was increased by 1.66 times. Furthermore, when the density range was determined to be 0-38, the pore phase showed the best consistency with the segmentation area. Depending on the Zou-He Boundary and Regularized Boundary, the relative error of simulated average velocities was only 0.2602 percent.

An Improved Initial Alignment Method Based on SE2(3)/EKF for SINS/GNSS Integrated Navigation System with Large Misalignment Angles.

This paper proposes an improved initial alignment method for a strap-down inertial navigation system/global navigation satellite system (SINS/GNSS) integrated navigation system with large misalignment angles. Its methodology is based on the three-dimensional special Euclidean group and extended Kalman filter (SE2(3)/EKF) and aims to overcome the challenges of achieving fast alignment under large misalignment angles using traditional methods. To accurately characterize the state errors of attitude, velocity, and position, these elements are constructed as elements of a Lie group. The nonlinear error on the Lie group can then be well quantified. Additionally, a group vector mixed error model is developed, taking into account the zero bias errors of gyroscopes and accelerometers. Using this new error definition, a GNSS-assisted SINS dynamic initial alignment algorithm is derived, which is based on the invariance of velocity and position measurements. Simulation experiments demonstrate that the alignment method based on SE2(3)/EKF can achieve a higher accuracy in various scenarios with large misalignment angles, while the attitude error can be rapidly reduced to a lower level.

Adaptive merging migration

Velocity errors and data noise are inevitable for seismic imaging of field data sets in current production; therefore, it is desirable to improve the seismic images as part of the migration process to mitigate the influence of such errors and noise. To address this, we develop a new method of adaptive merging migration (AMM). Our method can produce migrated sections of equal quality to conventional migration methods given a correct velocity model and noise-free data. In addition, it can ameliorate the seismic image quality when applied with erroneous migration velocity models or noisy seismic data. AMM uses an efficient recursive Radon transform to generate multiple p-component images, representing migrated sections associated with different local plane slopes. It then adaptively merges the subsections from those p-component images that are less distorted by velocity errors or noise into the whole image. Such merging is implemented by computing adaptive weights followed by a selective stacking. We use three synthetic velocity models and one field data set to evaluate the AMM performance on isolated Gaussian velocity errors, inaccurate smoothed velocities, velocity errors around high-contrast and short-wavelength interfaces, and noisy seismic data. Numerical tests conducted on synthetic and field data sets validate that AMM can effectively improve the seismic image quality in the presence of different types of velocity errors and data noise.

Probing the inner Galactic halo with blue horizontal-branch stars

Context. Stars that are found on the blue horizontal-branch (BHB) evolved from low-mass stars that have completed their core hydrogen-burning main sequence (MS) stage and undergone the helium flash at the end of their red giant phase. Hence, they are very old objects that can be used as markers in studying galactic structure and formation history. The fact that their luminosity is virtually constant at all effective temperatures also makes them good standard candles. Aims. We have compiled a catalogue of BHB stars with stellar parameters calculated from spectral energy distributions (SEDs) constructed using data from multiple large-scale photometric surveys. In addition, we update our previous Gaia Early Data Release 3 (EDR3) catalogue of BHB stars with parallax errors less than 20% by using the SED results to define the selection criteria. The purpose of these catalogues is to create a set of BHB star candidates with reliable stellar parameters. In addition, they provide a more complete full-sky catalogue with candidate objects found along the whole BHB from where RR-Lyrae are found on the instability strip to the extreme horizontal-branch (EHB). Methods. We selected a large dataset of Gaia Data Release 3 (DR3) objects based only on their position on the colour-magnitude diagram (CMD), along with the tangential velocity and parallax errors. The SEDs were then used to evaluate contamination levels in the dataset and derive optimised data quality acceptance constraints. This allowed us to extend the Gaia DR3 colour and absolute magnitude criteria further towards the EHB. The level of contamination found using SED analysis was confirmed by acquiring spectra using the Ondrejov Echelle spectrograph, attached to the Perek 2m telescope at the Astronomical Institute of the Czech Academy of Sciences. Results. We present a catalogue of 9172 Galactic halo BHB candidate stars with atmospheric and stellar parameters calculated from synthetic SEDs. We also present an extended Gaia DR3-based catalogue of 22 335 BHB candidate stars with a wider range of effective temperatures and Gaia DR3 parallax errors of less than 20%. This represents an increase of 33% compared to the our 2021 catalogue, with a contamination level of 10%.

Kinematic analysis of inverted planetary roller screw considering skew error

To reveal the motion characteristics of inverted planetary roller screws under the influence of axial deviation error, the working principle of inverted planetary roller screws was elaborated, and a motion analysis model of inverted planetary roller screws based on deviation error was established. This paper analyzed the angle error, axial displacement error, and relative velocity error of the roller, and comprehensively analyzed the influence of errors and parameters on the motion state.

Research on deflection correction performance of movable crossbeam during pressurization of ultra-large hydraulic press

In this paper, the deflection of the movable crossbeam during the pressurization process of the ultra-large hydraulic press is studied. The electromechanical and hydraulic systems of the ultra-large hydraulic press are analyzed, and a joint AMESim-ADAMS simulation model of the mechanical system, the oil system, and the correction control system are established. The simulation analyzes the deflection control of the working cylinder in the 1600 MN pressurized state, the movable crossbeam in the positive load without deflection correction, positive load with deflection correction, eccentric load with 300 mm of deflection in X-positive direction, and eccentric load with 300 mm of deflection in XZ-positive direction. The results show that the correction control system needs to be turned on synchronously at the beginning of the pressurization; under the pressure of 1600 MN and the maximum eccentricity of 300 mm, the velocity error of the movable crossbeam can be controlled within 7 %, and the parallel control precision can reach 0.14 mm/m.

A Robust Vector-Tracking Loop Based on KF and RTS Smoothing for Shipborne Navigation

High-precision navigation systems are crucial for unmanned autonomous vessels. However, commonly used Global Navigation Satellite System (GNSS) signals are often severely affected by environmental obstruction, leading to reduced positioning accuracy or even the inability to locate. To address the issues caused by signal obstruction in high-precision navigation systems, the research presented in this paper proposes a vector-tracking loop (VTL) structure based on the forward Kalman Filter (KF) and the backward Rauch Tung Striebel (RTS) smoothing algorithm. The introduction of loop filters in the signal-tracking loop improves the tracking accuracy of the carrier and code, thereby enhancing the stability and robustness of the navigation system. The traditional scalar-tracking loop (STL), traditional VTL, and Kalman Filter (KF)-based VTL were compared through shipborne motion experiments, and the proposed method demonstrated superior signal-tracking capability and navigation accuracy. In the experiment, there were three blocking areas along the experimental path. The experimental results show that, when there are signal blockages of 12 s, 18 s, and 40 s, compared to the traditional VTL method, the proposed method can reduce the horizontal position error by 93.9%, 95.8%, and 94.5%, respectively, as well as the horizontal velocity error by 71.1%, 95.8%, and 97.6%, respectively.

Continuous Shear Wave Elastography for Liver Using Frame-to-Frame Equalization of Complex Amplitude.

This study addresses a crucial necessity in the field of noninvasive liver fibrosis diagnosis by introducing the concept of continuous shear wave elastography (C-SWE), utilizing an external vibration source and color Doppler imaging. However, an application of C-SWE to assess liver elasticity, a deep region within the human body, arises an issue of signal instability in the obtained data. To tackle this challenge, this work proposes a method involving the acquisition of multiple frames of datasets, which are subsequently compressed. Furthermore, the proposed frame-to-frame equalization method compensates discrepancies in the initial phase that might exist among multiple-frame datasets, thereby significantly enhancing signal stability. The experimental validation of this approach encompasses both phantom tests and in vivo experiments. In the phantom tests, the proposed technique is validated through a comparison with the established shear wave elastography (SWE) technique. The results demonstrate a remarkable agreement, with an error in shear wave velocity of less than 4.2%. Additionally, the efficacy of the proposed method is confirmed through in vivo tests. As a result, the stabilization of observed shear waves using the frame-to-frame equalization technique exhibits promising potential for accurately assessing human liver elasticity. These findings collectively underscore the viability of C-SWE as a potential diagnostic instrument for liver fibrosis.

Indirect tuning of a complementary orientation filter using velocity data and a genetic algorithm

In this paper, the accuracy of inertial sensor orientation relative to the level frame is improved through optimal tuning of a complementary filter by a genetic algorithm. While constant filter gains have been used elsewhere, these may introduce errors under dynamic motions when gyroscopes should be trusted more than accelerometers. Optimal gains are prescribed by a Mamdani fuzzy rule base whose membership functions are found using a genetic algorithm and experimental data. Furthermore, model fitness is not based directly on orientation but the error between estimated and ground truth velocities. This paper has three interrelated novel elements. The main novelty is the indirect tuning method, which is simple, low-cost and requires a single camera and inertial sensor. The method is shown to increase tracking accuracy compared with popular baseline filters. Secondary novel elements are the bespoke genetic algorithm and the time agnostic velocity error metric. The contributions from this work can help improve the localization accuracy of assets and human personnel. This research has a direct impact in command and control by improving situational awareness and the ability to direct assets to safe locations using safer routes. This results in increasing safety in applications such as firefighting and battlespace.

Data-Driven Identification of Crane Dynamics Using Regularized Genetic Programming

The meaningful problem of improving crane safety, reliability, and efficiency is extensively studied in the literature and targeted via various model-based control approaches. In recent years, crane data-driven modeling has attracted much attention compared to physics-based models, particularly due to its potential in real-time crane control applications, specifically in model predictive control. This paper proposes grammar-guided genetic programming with sparse regression (G3P-SR) to identify the nonlinear dynamics of an underactuated crane system. G3P-SR uses grammars to bias the search space and produces a fixed number of candidate model terms, while a local search method based on an l0-regularized regression results in a sparse solution, thereby also reducing model complexity as well as reducing the probability of overfitting. Identification is performed on experimental data obtained from a laboratory-scale overhead crane. The proposed method is compared with multi-gene genetic programming (MGGP), NARX neural network, and Takagi-Sugeno fuzzy (TSF) ARX models in terms of model complexity, prediction accuracy, and sensitivity. The G3P-SR algorithm evolved a model with a maximum mean square error (MSE) of crane velocity and sway prediction of 1.1860 × 10−4 and 4.8531 × 10−4, respectively, in simulations for different testing data sets, showing better accuracy than the TSF ARX and MGGP models. Only the NARX neural network model with velocity and sway maximum MSEs of 1.4595 × 10−4 and 4.8571 × 10−4 achieves a similar accuracy or an even better one in some testing scenarios, but at the cost of increasing the total number of parameters to be estimated by over 300% and the number of output lags compared to the G3P-SR model. Moreover, the G3P-SR model is proven to be notably less sensitive, exhibiting the least deviation from the nominal trajectory for deviations in the payload mass by approximately a factor of 10.

Adaptive UAV Navigation Method Based on AHRS.

To address the inaccuracy of the Constant Acceleration/Constant Velocity (CA/CV) model as the state equation in describing the relative motion state in UAV relative navigation, an adaptive UAV relative navigation method is proposed, which is based on the UAV attitude information provided by Attitude and Heading Reference System (AHRS). The proposed method utilizes the AHRS output attitude parameters as the benchmark for dead reckoning and derives a relative navigation state equation with attitude error as process noise. By integrating the extended Kalman filter output for relative state estimation and employing an adaptive decision rule designed using the innovation of the filter update phase, the proposed method recalculates motion states deviating from the actual motion using the Tasmanian Devil Optimization (TDO) algorithm. The simulation results show that, compared with the CA/CV model, the proposed method reduces the relative position errors by 12%, 23%, and 32% in the X, Y, and Z directions, respectively, and that it reduces the relative velocity errors by 350%, 330%, and 300%, respectively. There is a significant improvement in the relative navigation accuracy.

Trajectory tracking of binocular vision system for picking robot based on fast non-singular terminal sliding mode control

This paper proposes a non-singular fast terminal sliding mode control method for a binocular active vision platform of a picking robot with unknown dynamics. The method uses radial basis function (RBF) neural networks to achieve trajectory tracking accuracy and enhance robustness against external interference. A non-singular fast terminal sliding mode controller is designed for the system’s convergence within a limited time. An adaptive neural network approximates the unknown nonlinear function of the dynamic model. Stability and finite-time convergence of the closed-loop system are established using Lyapunov theory. Experimental verification on the binocular vision platform demonstrates position and speed errors converging to the desired trajectory within 2 and 1 second, respectively. Moreover, when subjected to external interference, the position and velocity errors converge within 0.1 seconds. Simulation experiments confirm the method’s effectiveness in improving convergence speed, trajectory tracking accuracy, and robustness against external interference, while reducing system chattering.

Microwave Photonics Broadband Doppler Velocity Simulator with High Spurious Suppression Ratio by Using Serrodyne Modulation

A Doppler velocity simulation method based on serrodyne modulation is proposed to achieve the frequency shift from hundred hertz to megahertz. One sub-phase modulation (PM) in a dual-parallel dual-drive Mach–Zehnder modulator loads a sawtooth signal to achieve a small frequency shift of the optical carrier. The other three sub-PMs implement carrier-suppressed double-band modulation of the RF signal. The RF signal is directly coupled from the receiving antenna to the modulator’s RF port without any electrical devices like a 90° hybrid, which ensures a broad operational bandwidth of the system. After filtering out one of the RF modulation sidebands by an optical filter, Doppler frequency shifting (DFS) is realized through frequency beating. The half-wave voltage of modulators rapidly decreases at low frequency shifts, leading to an increase in spurious signals. In order to improve the spurious suppression ratio (SSR) of DFS, a digital pre-distortion compensation based on the measured half-wave voltage is implemented in the frequency domain. Experimental results show that SSRs are larger than 35 dB when frequency shifts range from 0.1 kHz to 1 MHz. The RF operation bandwidth covers 2–40 GHz. The effectiveness of a Doppler velocity simulator is evaluated, and the simulation velocity error is less than 0.06 km/h. The proposed method has potential applications in both broadband electronic warfare and traffic metering applications.

The Analytical Validity of Stride Detection and Gait Parameters Reconstruction Using the Ankle-Mounted Inertial Measurement Unit Syde®.

The increasing use of inertial measurement units (IMU) in biomedical sciences brings new possibilities for clinical research. The aim of this paper is to demonstrate the accuracy of the IMU-based wearable Syde® device, which allows day-long and remote continuous gait recording in comparison to a reference motion capture system. Twelve healthy subjects (age: 23.17 ± 2.04, height: 174.17 ± 6.46 cm) participated in a controlled environment data collection and performed a series of gait tasks with both systems attached to each ankle. A total of 2820 strides were analyzed. The results show a median absolute stride length error of 1.86 cm between the IMU-based wearable device reconstruction and the motion capture ground truth, with the 75th percentile at 3.24 cm. The median absolute stride horizontal velocity error was 1.56 cm/s, with the 75th percentile at 2.63 cm/s. With a measurement error to the reference system of less than 3 cm, we conclude that there is a valid physical recovery of stride length and horizontal velocity from data collected with the IMU-based wearable Syde® device.

Optimization of Space-Time image velocimetry based on deep residual learning

Space-Time Image Velocimetry (STIV) is considered to be a highly promising method for river flow measurement due to its safe and efficient features. However, the traditional STIV method is challenged to obtain stable and accurate results when disturbed by external environments such as obstacles, waves and noise. As a consequence, a residual network-based STIV method is constructed by combining STIV with deep learning. In the synthetic dataset validation, benefited from the unique residual module and the relation-aware global attention mechanism, the recognition accuracy of STI texture angle is as high as 99.9 %, which is much higher than that of the Efficient Net b3, ViT16 and VGG19 networks. In the real dataset, the proposed method shows stronger stability and lower error by compared with gradient tensor (GT) and fast Fourier transform (FFT) methods, whose overall recognition accuracy is above 90 % under all types of STI cases, angle error is within 2°, and flow velocity error is around 0.10 m/s. Furthermore, the model was validated by a one-year comparative measurement at the Qilijie hydrological station in 2022, and the results were compared with LSPIV, GT, and FFT methods. The R2 of the model to the measured values is 0.98, the RMSE is 0.17, and the MAE is 0.12 m/s, indicating that the method has sufficient accuracy and robustness for long-term stable and real-time monitoring of river flow.

Statement of Removal

A randomly fluctuated vortex-induced vibration (VIV) affects the localization of moving targets in an underwater acoustic wireless sensor network (UAWSN) to cause an offset time and time synchronization error which disturbs the localization of the non-cooperative moving target. Definition 2.1, theorem 2.2 has been introduced in the present work to describe the genesis and consequences of the VIV effect in UAWSN. An elliptic localization introduces the direct and indirect path measurements of the moving target. The standard CRLB parameter quantifies the VIV effect. The compensation of vortex-induced vibrations (CVV) algorithm is proposed to quantify the degradation of localization performance and to estimate the position of the moving target. The theoretical measurements of position error, velocity error with respect to the variation of noise, number of receiver sensors, and probability of line of sight (LOS) errors are compared to the standard methods. The proposed CVV algorithm improves the localization performance by 32.24% and reduces 34.36% of position errors, and 37.46% of velocity errors up to 600 meters. It can be useful for the sub-aquatic internet of underwater things.

A modified CFD-DEM method for accurate prediction of the minimum fluidization velocity

Monodisperse fluidized bed experiments have been carried out on 13 kinds of particles with different particle sizes in a cylindrical fluidized bed. The accuracy of nine commonly used monodisperse drag models, including the Gidsapow, BVK, and Zhou–Fan models is verified. It is found that the Zhou–Fan model is the most accurate at low and moderate values of the particle Reynolds number. It is found that with the traditional computational fluid dynamics–discrete element method (CFD-DEM) method it is difficult to obtain an accurate minimum fluidization velocity owing to difficulty in obtaining an accurate value of the minimum fluidization solid volume fraction. A modified method is proposed to increase the accuracy of prediction of the minimum fluidized solid volume fraction by using a virtual particle size in the calculation of the particle collision process. The effectiveness of the modified method is verified by comparing the CFD-DEM simulation results with experimental results for Geldart-D particles. The relative error of the minimum fluidization velocity is found to be reduced from 24.6% to 2%.