Accurate Velocity Estimation Research Articles

Tensor Velocity Imaging With Motion Correction.

This article presents a motion compensation procedure that significantly improves the accuracy of synthetic aperture tensor velocity estimates for row-column arrays. The proposed motion compensation scheme reduces motion effects by moving the image coordinates with the velocity field during summation of low-resolution volumes. The velocity field is estimated using a transverse oscillation cross-correlation estimator, and each image coordinate's local tensor velocity is determined by upsampling the field using spline interpolation. The motion compensation procedure is validated using Field II simulations and flow measurements acquired using a 3-MHz row-column addressed probe and the research scanner SARUS. For a peak velocity of 25 cm/s, a pulse repetition frequency of 2 kHz, and a beam-to-flow angle of 60°, the proposed motion compensation procedure was able to reduce the relative bias from -27.0% to -9.4% and the standard deviation from 8.6% to 8.1%. In simulations performed with a pulse repetition frequency of 10 kHz, the proposed method reduces the bias in all cases with beam-to-flow angles of 60° and 75° and peak velocities between 10 and 150 cm/s.

A note on spectral velocity approximation at shorter intermediate periods

It is common to use the design pseudo spectral acceleration (PSA) spectrum together with a modal combination rule to estimate the peak linear seismic response of a structural system. Some of the modal combination rules proposed so far require an accurate estimation of spectral velocity (SV) spectrum consistent with the given design PSA spectrum in order to account for modal correlations in a simple manner. The traditional pseudo spectral velocity (PSV) approximation of SV ordinates is reliable only for the velocity-sensitive region of the spectrum. More reliable approximations have therefore been developed in the recent years for the acceleration-sensitive and displacement-sensitive regions. In this work, the approximation developed for the acceleration-sensitive region is proposed to be applied on a greater range of periods, involving the shorter periods of the velocity-sensitive region, for a more accurate estimation of the SV spectrum. Regression equations are developed to predict the limiting period of the extended range by using the information available from the given PSA spectrum. A correction factor is also proposed to be applied to the SV estimates in order for those to have a smooth transition near the limiting period.

Experimental and Mathematical Tools to Predict Droplet Size and Velocity Distribution for a Two-Fluid Nozzle

Despite progress in laser-based and computational tools, an accessible model that relies on fundamentals and offers a reasonably accurate estimation of droplet size and velocity is lacking, primarily due to entangled complex breakup mechanisms. Therefore, this study aims at using the integral form of the conservation equations to create a system of equations by solving which, the far-field secondary atomization can be analyzed through predicting droplet size and velocity distributions of the involved phases. To validate the model predictions, experiments are conducted at ambient conditions using water, methanol, and acetone as model fluids with varying formulation properties, such as density, viscosity, and surface tension. Droplet size distribution and velocity are measured with laser diffraction and a high-speed camera, respectively. Finally, an attempt is made to utilize non-scaled parameters to characterize the atomization process, useful for extrapolating the sensitivity analysis to other scales. The merit of this model lies in its simplicity for use in process control and optimization.

Dynamic Image-Based Output Feedback Control for Visual Servoing of Multirotors

This article proposes a novel adaptive image-based output feedback visual servoing approach to control a multirotor to the desired pose by using a minimum onboard sensor suite, which consists of an inertial measurement unit and a monocular camera. Different from “perspective moment,” a new type of image feature is designed as “rotated perspective moment,” whose dynamics is independent of roll, pitch, and yaw rates. On this basis, a nonlinear adaptive observer is designed to estimate the scaled linear velocity, which is more accurate, since the observer does not involve noisy angular velocity measurements. Then, a novel image-based output feedback controller is proposed with the designed image features and the observer, wherein the new saturated integral terms of linear and angular velocity errors are introduced into the controller design, respectively, to compensate system uncertainties. As a result, the steady-state error is decreased considerably. In addition, without the assumption of the separation principle between the observer and the controller, the small-angle approximation, or the time-scale separation assumption, the error signals of image features, attitude, velocity, and observer estimation can all converge to the origin asymptotically, which is proven by rigorous Lyapunov analysis. Comparative experiments are conducted to show the superior performance of the proposed approach in terms of more accurate velocity estimation, smaller steady-state errors, and stronger robustness.

Elastic wave velocity dispersion in polycrystals with elongated grains: Theoretical and numerical analysis.

The phase velocity dispersion of longitudinal waves in polycrystals with elongated grains of arbitrary crystallographic symmetry is studied in all frequency ranges by the theoretical second-order approximation (SOA) and numerical three-dimensional finite element (FE) models. The SOA and FE models are found to be in excellent agreement for three studied polycrystals: cubic Al, Inconel, and a triclinic material system. A simple Born approximation for the velocity, not containing the Cauchy integrals, and the explicit analytical quasi-static velocity limit (Rayleigh asymptote) are derived. As confirmed by the FE simulations, the velocity limit provides an accurate velocity estimate in the low-frequency regime where the phase velocity is nearly constant on frequency; however, it exhibits dependence on the propagation angle. As frequency increases, the phase velocity increases towards the stochastic regime and then, with further frequency increase, behaves differently depending on the propagation direction. It remains nearly constant for the wave propagation in the direction of the smaller ellipsoidal grain radius and decreases in the grain elongation direction. In the Rayleigh and stochastic frequency regimes, the directional velocity change shows proportionalities to the two elastic scattering factors even for the polycrystal with the triclinic grain symmetry.

A 17.8-MS/s Compressed Sensing Radar Accelerator Using a Spiking Neural Network

A prototype compressed sensing radar processor boosts the accuracy of target range and velocity estimations by over 6 $\times $ compared with conventional processing techniques. The prototype numerically solves basis pursuit denoising with a biologically plausible spiking neural network. A unique form of weight compression allows on-chip storage of all weights for the large fully connected network. Capable of producing over 200000 range-velocity scene reconstructions per second, the prototype improves throughput by 8 $\times $ and efficiency by 18 $\times $ over the state of the art.

Investigating the Benefits of Vector-Based GNSS Receivers for Autonomous Vehicles under Challenging Navigation Environments

There is a growing demand for robust and accurate positioning information for various applications, including the self-driving car industry. Such applications rely mainly on the Global Navigation Satellite System (GNSS), including the Global Positioning System (GPS). However, GPS positioning accuracy relies on several factors, such as satellite geometry, receiver architecture, and navigation environment, to name a few. In urban canyons in which there is a significant probability of signal blockage of one or more satellites and/or interference, the positioning accuracy of scalar-based GPS receivers drastically deteriorates. On the other hand, vector-based GPS receivers exhibit some immunity to momentary outages and interference. Therefore, it is becoming necessary to consider vector-based GPS receivers for several applications, especially safety-critical applications, including next-generation navigation technologies for autonomous vehicles. This paper investigates a vector-based receiver’s performance and compares it to its scalar counterpart in signal degraded conditions. The realistic simulation experiments in this paper are conducted on GPS L1 C/A signals generated using the SpirentTM simulation system to create a fully controlled environment to examine and validate the performance. The results show that the vector tracking system outperforms the scalar tracking in terms of position and velocity estimation accuracy in signal-degraded environments.

Strong tracking extended particle filter for manoeuvring target tracking

To improve the stability and accuracy of manoeuvring target tracking in three-dimensional space based on the angle of arrival (AOA) and its rate of change observations, this study presents a new observation fusion method by fusing the received signal strength (RSS) with AOA and the rate of change of AOA. To enhance the adaptive ability of traditional strong tracking extended particle filter (TSTEPF) against model mismatch, this study re-determines the position of the fading factor in the strong tracking extended Kalman filter based on the orthogonal principle and gives the calculating method. And by combining the method with the particle filter, a new strong tracking extended particle filter (STEPF) algorithm is proposed. Simulation results show that after fusing RSS into the observation model, the tracking speed and precision are both improved, especially precision, as the position root-mean-square error has a 58% decline on average. And it is found that STEPF proposed in this study has a more stable adaptive ability than TSTEPF, and is superior in terms of position, velocity, and acceleration estimation accuracy.

On the Uncertainty of the Image Velocimetry Method Parameters

Image velocimetry is a popular remote sensing method mainly because of the very modest cost of the necessary equipment. However, image velocimetry methods employ parameters that require high expertise to select appropriate values in order to obtain accurate surface flow velocity estimations. This introduces considerations regarding the subjectivity introduced in the definition of the parameter values and its impact on the estimated surface velocity. Alternatively, a statistical approach can be employed instead of directly selecting a value for each image velocimetry parameter. First, probability distribution should be defined for each model parameter, and then Monte Carlo simulations should be employed. In this paper, we demonstrate how this statistical approach can be used to simultaneously produce the confidence intervals of the estimated surface velocity, reduce the uncertainty of some parameters (more specifically, the size of the interrogation area), and reduce the subjectivity. Since image velocimetry algorithms are CPU-intensive, an alternative random number generator that allows obtaining the confidence intervals with a limited number of iterations is suggested. The case study indicated that if the statistical approach is applied diligently, one can achieve the previously mentioned threefold objective.

A Reliable Vehicle Lateral Velocity Estimation Methodology Based on SBI-LSTM During GPS-Outage

Vehicle lateral velocity is critical for the high-level automatic driving technology, andyyfusing Global Position System (GPS) in the lateral velocity estimation method can greatly improve the estimation accuracy. However, the method fusing GPS is seldom reported, since the problem of GPS-outage often appears. Accordingly, this article proposes a novel lateral velocity estimation method (VLVEM) based on SBI-LSTM in GPS-outage environment. Additionally, VLVEM integrating Inertial Navigation System-aided GPS (INS-aided GPS) is derived from Federated Kalman Filter (FKF) algorithm. Furthermore, during GPS-outage, induced from the Stack Bidirectional Long Short-Term Memory Recurrent Neural Network (SBI-LSTM RNN), an INS-aided GPS fault reconstructor (IGFR) is designed to reconstruct INS-aided GPS model. Finally, the simulation results show that compared with most of the existing methods which only consider the in-vehicle sensors' signals, the proposed method fusing GPS has higher lateral velocity estimation accuracy. Besides, when GPS-outage causes INS-aided GPS failure, IGFR can reconstruct INS-aided GPS model, and VLVEM still has high estimation accuracy. Combining the in-vehicle sensors and GPS, VLVEM exhibits great robustness and fault tolerance.

A Novel Moving Target Detection Method Based on RPCA for SAR Systems

Clutter background suppression and velocity estimation for moving targets are two critical problems in synthetic aperture radar-ground moving target indication (SAR-GMTI). A robust principal component analysis (RPCA) method is used to separate the sparse matrix of moving targets from the low-rank matrix of static backgrounds by using amplitude information in the image domain. However, the nonsparsity of the moving target echoes limits the performance of the RPCA in SAR-GMTI, and the velocity of the moving target cannot be estimated since the phase information is destroyed by the soft-thresholding operator in the RPCA process. To solve these problems, a novel moving target detection method based on RPCA (NRPCA) for SAR systems is proposed in this article. An atomic norm-based optimization program is first constructed to transform the data sparsity requirement into a moving target sparsity requirement. Although this optimization program is NP-hard, it is transformed to semidefinite programming by relaxation. Furthermore, accurate velocity estimation is performed using dual function theory and the alternating direction method of multipliers (ADMM) algorithm while the selection of the sparsity order k is avoided. Simulations and analyses based on experimental data illustrate the effectiveness of the proposed method.

Rossby and Yanai Modes of Tropical Instability Waves in the Equatorial Pacific Ocean and a Diagnostic Model for Surface Currents

AbstractMesoscale activities over the equatorial Pacific Ocean are dominated by the Rossby and Yanai modes of tropical instability waves (TIWs). The TIW-induced surface velocity has not been accurately estimated in previous diagnostic models, especially for the meridional component across the equator. This study develops a diagnostic model that retains the acceleration terms to estimate the TIW surface velocity from the satellite-observed sea surface height. Validated against moored observations, the velocity across the equator is accurately estimated for the first time, much improved from existing products. The results identify the Rossby- and Yanai-mode TIWs as the northwest–southeastward (NW–SE) velocity oscillations north of the equator and the northeast–southwestward (NE–SW) velocity oscillations on the equator, respectively. Barotropic instability is the dominant energy source of the two TIW modes. The NE–SW velocity oscillation of the Yanai mode is associated with the counterclockwise shear of the South Equatorial Current on the equator. The two TIW modes induce different sea surface temperature patterns and vertical motions. Accurate estimates of TIW velocity are important for studying equatorial ocean dynamics and climate variability in the tropical Pacific Ocean.

GNSS real‐time instantaneous velocimetry based on moving‐window polynomial modelling

The instantaneous kinematic velocity obtained by using a stand-alone global navigation satellite system (GNSS) receiver has attracted increasing attention, and has significant for numerous applications. Using the time difference carrier phase (TDCP) measurements, or the displacements they produced, we propose two real-time instantaneous velocity determination methods based on the moving-window polynomial model. The two methods are called the observation-domain and the coordinate-domain, correspondingly. In the calculation process, the Cholesky update method is utilized to improve numerical efficiency. The proposed methods are a finite impulse in nature, and hence are robust against modeling uncertainties, such as temporal outlying measurements, inaccurate information of noise statistics. Two vehicle-mounted experiments were performed to verify the performance of the coordinate-domain and observation-domain methods. The reference velocities used to evaluate the accuracy of velocity estimates are chosen as GNSS/INS smoothing solution and high-rate RTK solution in these two experiments, respectively. The results show that the coordinate-domain and observation-domain methods were comparable in performance, both better than that of the Nonlinear Tracking Differentiator (NTD) method. Specifically, the improvement in terms of root mean square of the proposed methods, compared with the NTD, can be 29.3–40.3% and 6.25–16.4% in the two experiments, respectively.

A correction scheme for wall-bounded two-way coupled point-particle simulations

The accuracy of Euler-Lagrange point-particle models employed in particle-laden fluid flow simulations depends on accurate estimation of the particle force through closure models. Typical force closure models require computation of the slip velocity at the particle location, which in turn requires accurate estimation of the undisturbed fluid velocity. Such an undisturbed velocity is not readily available when the fluid and particle phases are two-way coupled, due to the disturbance created by the particle's force in the nearby fluid velocity field. A common practice is to use the disturbed velocity to compute the particle force which can result in errors as much as 100% in predicting the particle dynamics. In this work, a correction scheme is developed that facilitates accurate estimation of the undisturbed fluid velocity in particle-laden fluid flows with and without no-slip walls. The model is generic and can handle particles of different size and density, arbitrary interpolation and projection functions, anisotropic grids with large aspect ratios, and wall-bounded flows. The present correction scheme is motivated by the recent work of Esmaily & Horwitz (JCP, 2018) on unbounded particle-laden flows. Modifications necessary for wall-bounded flows are developed such that the undisturbed fluid velocity at any wall distance is accurately recovered, asymptotically approaching the result of unbounded schemes for particles far away from walls. A detailed series of verification tests was conducted on settling velocity of a particle in parallel and perpendicular motions to a no-slip wall. A range of flow parameters and grid configurations; involving anisotropic rectilinear grids with aspect ratios typically encountered in particle-laden turbulent channel flows was considered in detail. When the wall effects are accounted for, the present correction scheme reduces the errors in predicting the near-wall particle motion by one order of magnitude smaller values compared to the unbounded correction schemes.

RNN-Aided Human Velocity Estimation from a Single IMU.

Pedestrian Dead Reckoning (PDR) uses inertial measurement units (IMUs) and combines velocity and orientation estimates to determine a position. The estimation of the velocity is still challenging, as the integration of noisy acceleration and angular speed signals over a long period of time causes large drifts. Classic approaches to estimate the velocity optimize for specific applications, sensor positions, and types of movement and require extensive parameter tuning. Our novel hybrid filter combines a convolutional neural network (CNN) and a bidirectional recurrent neural network (BLSTM) (that extract spatial features from the sensor signals and track their temporal relationships) with a linear Kalman filter (LKF) that improves the velocity estimates. Our experiments show the robustness against different movement states and changes in orientation, even in highly dynamic situations. We compare the new architecture with conventional, machine, and deep learning methods and show that from a single non-calibrated IMU, our novel architecture outperforms the state-of-the-art in terms of velocity (≤0.16 m/s) and traveled distance (≤3 m/km). It also generalizes well to different and varying movement speeds and provides accurate and precise velocity estimates.

Cross-Correlation Sensitivity-Based Electrostatic Direct Velocity Tomography

Electrostatic tomography (EST) has been widely used in gas–solid two-phase flows to measure particle velocity in many industrial processes. However, EST is a passive measurement technique, and this results in rare independent measurements compared with other tomographic modalities. In this article, an electrostatic direct velocity tomography (EDVT) method is proposed to increase the number of independent measurements and to improve the accuracy of velocity estimation. For a two-plane electrostatic sensor, the cross-correlation (CC) velocities between the electrodes from the two planes at different circumferential positions can represent the particle velocities in different regions. First, the CC velocities between the electrodes on different planes are calculated and used as new measurements. In this way, the number of independent measurements of a 16-electrode EST sensor can be easily increased from 16 to 120. Second, by analyzing the relationship between the CC velocities and the particle charge and particle velocity distribution, a CC sensitivity matrix is established. Finally, a tomographic model to represent the velocity distribution of charged particles can be established based on the new measurements and the CC sensitivity matrix. Simulation and experimental results show that the velocity distribution reconstructed by EDVT can represent the particle velocity distribution in the pipeline and is promising to provide a method to study the mechanism of gas–solid two-phase flows.

Global optical flow-based estimation of velocity for multicopters using monocular vision in GPS-denied environments

Velocity estimation is a requisite for multicopters to guarantee flight stability and maneuverability. In this paper, a global optical flow-based approach is proposed for estimation of multicopter velocity using the off-the-shelf onboard sensors, including a downward-looking monocular camera, an Inertial Measurement Unit (IMU) and a sonar facing downwards in GPS-denied environments. The proposed method does not require any prior information or artificial landmark of the environment. First, a feature-based estimation method is proposed for richly textured surfaces while an image pyramids-based method extended for low-textured backgrounds to obtain the pixel motion. Then, combined with the estimated pixel motion, vision-based velocity estimation is developed, offering the visual observations. Furthermore, a classical linear Kalman filter for velocity estimation by fusing all the available information from sensors is derived. Finally, the proposed method is validated and demonstrated with a collection of synthetic data based on a Microsoft AirSim high-fidelity visual simulation platform as well as flight experiments with real data from a commercial DJI Matrice 100 quadcopter equipped with a Guidance visual sensing module. Simulation and experimental results indicate that the proposed method is capable of providing an accurate estimate of multicopter velocity in GPS-denied or confined environments.

An An Accurate Estimation of Shear Wave Velocity Using Well Logging Data for Khasib Carbonate Reservoir - Amara Oil Field


 Shear and compressional wave velocities, coupled with other petrophysical data, are vital in determining the dynamic modules magnitude in geomechanical studies and hydrocarbon reservoir characterization. But, due to field practices and high running cost, shear wave velocity may not available in all wells. In this paper, a statistical multivariate regression method is presented to predict the shear wave velocity for Khasib formation - Amara oil fields located in South- East of Iraq using well log compressional wave velocity, neutron porosity and density. The accuracy of the proposed correlation have been compared to other correlations. The results show that, the presented model provides accurate estimates of shear wave velocity with correlation coefficient of about unity than other currently available methods.

Reference Spreading: Tracking Performance for Impact Trajectories of a 1DoF Setup

In this brief, trajectory tracking control for nonperiodic motions with impacts is analyzed by means of physical experiments. This brief aims at demonstrating the effectiveness and robustness of the control method called reference spreading (RS) on a physical setup. An actuated rebounding pendulum, a one-degree-of-freedom system specifically designed for performing trajectories with partially elastic impacts, acts as a test bench. RS control is compared to both classic proportional derivative (PD) feedback control and distance function-based control, showing superior performance. Evidence of RS’s robustness to delayed impact detection, deteriorated velocity estimation accuracy, and model inaccuracies is moreover reported.

Joint power and time width allocation in collocated MIMO radar for multi‐target tracking

Collocated multiple-input multiple-output radar can track multiple targets simultaneously by transmitting multiple orthogonal beams and adopting the digital beamforming technology. In this scenario, the authors propose a joint power and time width allocation approach, which combines a cognitive tracking model based on the posterior Cramer-Rao lower bound (PCRLB) and the square-root cubature Kalman filter. The aim of the optimisation model is to improve the velocity estimation accuracy by minimising the sum of the PCRLBs of the velocity of multiple targets, which are predicted based on the feedback information from the cognitive tracking model. However, there are two finite working resources in the optimisation model: the total transmit power of multiple beams and the total effective time width of each corresponding signal. The resource allocation problem can be transformed into a non-convex optimisation problem, which can be converted into a standard convex optimisation problem by the linear relationship between the optimal power and the optimal time width. In this way, the joint power and time width allocation scheme is established as an adaptive closed-loop system. Numerical results demonstrate that the velocity tracking accuracy can be improved efficiently by the proposed algorithm.