Velocity Estimation Research Articles

User velocity estimation in HetNets utilizing vertical and horizontal handover counts via maximum likelihood estimation

In this paper, we propose a maximum likelihood estimation (MLE) of user velocity based on Rayleigh distributed handover counts in heterogeneous networks (HetNets). The high density of base stations (BSs) in HetNets leads to mobile users experiencing unnecessary handovers and service outages, emphasizing the importance of mobility management in HetNets. Since user velocity plays an important role in the handover process, its knowledge is necessary for efficient mobility management. Our proposed velocity estimation strategy involves tracking of both vertical and horizontal handovers over a defined period. We utilized mathematical modeling to estimate that the probability mass function (PMF) of the handover counts in the HetNets scenario follows a Rayleigh distribution. We determined the scale parameter by analyzing the close deployment of small cells and macro-cell BSs using stochastic geometry. Our approach assumes a random distribution of BSs based on a homogeneous Poisson point process and investigates the handover-count PMF considering factors such as user velocity, BS density, and the duration of handover-count measurements. Using these statistics, we derived the Cramer–Rao lower bound (CRLB) and applied a MLE technique to estimate user velocity. The MATLAB simulation method is used to validate our approach for the velocity estimation, and the results show the tight closeness of MLE asymptotic variance with CRLB. The numerical analysis indicates that as user velocity increases, the variance of the estimation also increases. Conversely, the simulation results demonstrate that as BS density and the time span for handover-count measurements increase, the velocity estimation error decreases.

Accurate RNA velocity estimation based on multibatch network reveals complex lineage in batch scRNA-seq data

RNA velocity, as an extension of trajectory inference, is an effective method for understanding cell development using single-cell RNA sequencing (scRNA-seq) experiments. However, existing RNA velocity methods are limited by the batch effect because they cannot directly correct for batch effects in the input data, which comprises spliced and unspliced matrices in a proportional relationship. This limitation can lead to an incorrect velocity stream. This paper introduces VeloVGI, which addresses this issue innovatively in two key ways. Firstly, it employs an optimal transport (OT) and mutual nearest neighbor (MNN) approach to construct neighbors in batch data. This strategy overcomes the limitations of existing methods that are affected by the batch effect. Secondly, VeloVGI improves upon VeloVI’s velocity estimation by incorporating the graph structure into the encoder for more effective feature extraction. The effectiveness of VeloVGI is demonstrated in various scenarios, including the mouse spinal cord and olfactory bulb tissue, as well as on several public datasets. The results show that VeloVGI outperformed other methods in terms of metric performance.

Overview of satellite nav spoofing and anti-spoofing techniques

In recent years, satellite navigation systems have witnessed widespread adoption across diverse fields, including military surveillance, precision agriculture, traffic monitoring, resource exploration, and disaster assessment. However, navigation signals are susceptible to interference, with deceptive interference posing the most significant threat to navigation systems. This paper provides a comprehensive overview of satellite navigation spoofing and anti-spoofing techniques. It reviews the current state of spoofing and anti-spoofing technologies, analyzing advancements in spoofing techniques and the evolution of countermeasures. Furthermore, the paper elaborates on the impact of spoofing interference on receiver performance, examining its effects on positioning, timing, and velocity estimations. A detailed analysis of various anti-spoofing methods is presented, categorizing them into detection, identification, suppression, and localization techniques. This review aims to provide a thorough understanding of the evolving landscape of satellite navigation spoofing and anti-spoofing technologies, fostering further research and development efforts to ensure the integrity and resilience of satellite navigation systems in the face of sophisticated threats.

Enhanced FFT–Root–MUSIC Algorithm Based on Signal Reconstruction via CEEMD–SVD for Joint Range and Velocity Estimation for FMCW Radar

Frequency-modulated continuous-wave (FMCW) radar is used to extract range and velocity information from the beat signal. However, the traditional joint range–velocity estimation algorithms often experience significant performances degradation under low signal-to-noise ratio (SNR) conditions. To address this issue, this paper proposes a novel approach utilizing the complementary ensemble empirical mode decomposition (CEEMD) combined with singular value decomposition (SVD) to reconstruct the beat signal prior to applying the FFT-Root-MUSIC algorithm for joint range and velocity estimation. This results in a novel joint range–velocity estimation algorithm termed as the CEEMD-SVD-FFT-Root-MUSIC (CEEMD-SVD-FRM) algorithm. First, the beat signal contaminated with additive white Gaussian noise is decomposed using CEEMD, and an appropriate autocorrelation coefficient threshold is determined to select the highly correlated intrinsic mode functions (IMFs). Then, the SVD is applied to the selected highly correlated IMFs for denoising the beat signal. Subsequently, the denoised IMFs and signal residuals are combined to reconstruct the beat signal. Finally, the FFT-Root-MUSIC algorithm is applied to the reconstructed beat signal to estimate both the range and Doppler frequencies, which are then used to calculate the range and velocity estimates of the targets. The proposed CEEMD-SVD-FRM algorithm is validated though simulations and experiments, demonstrating significant improvement in the robustness and accuracy of range and velocity estimates for the FMCW radar due to the effective denoising of the reconstructed beat signal. Moreover, it substantially outperforms the traditional methods in low SNR environments.

Novel robust GNSS velocity estimation with a residual-based multithreshold constraint algorithm

Novel robust GNSS velocity estimation with a residual-based multithreshold constraint algorithm

The vanishing limit of a rigid body in three-dimensional viscous incompressible fluid

AbstractWe consider the evolution of a small rigid body in an incompressible viscous fluid filling the whole space $${{\mathbb {R}}}^3$$ R 3 . When the small rigid body shrinks to a “massless” point in the sense that its density is constant, we prove that the solution of the fluid-rigid body system converges to a solution of the Navier–Stokes equations in the full space. Based on some $$L^p-L^q$$ L p - L q estimates of the fluid–structure semigroup and a fixed point argument, we obtain a uniform estimate of velocity of the rigid body. This allows us to construct admissible test functions which plays a key role in the procedure of passing to the limit.

A robust GNSS velocity estimation method combining Doppler and carrier phase observations in complex urban environments

Accurate and reliable velocity information is crucial for kinematic positioning, as it not only characterizes the carrier state but also serves as constraint information for positioning. However, due to the influence of low-cost Global Navigation Satellite System (GNSS) chips and complex environments, GNSS signals are prone to attenuation and interruptions. Consequently, frequent gross errors and cycle slips occur in observations, significantly degrading the velocity precision and reliability. To address this challenge, we have proposed a method combining Doppler and Time-Differenced Carrier Phase Velocity Estimation (D-TDCPVE). First, we assess the accuracy of both Doppler and carrier phase observations to determine the appropriate weight ratio for their combination. Second, we analyze the correlation between the two observation types and propose an adaptive parameter estimation strategy for estimating one or two clock bias variations to address their potential differences. Finally, we incorporate a combination of pre- and post-detection quality control measures along with additional cycle slip parameters to mitigate the impact of gross errors and cycle slips in complex environments. Experimental results conducted with three low-cost devices in urban environments demonstrate the superior performance of the D-TDCPVE method over both Doppler velocity estimation (DVE) and time-differenced carrier phase velocity estimation (TDCPVE). It enhances the success rate of velocity estimation by 3.3% to 20.1% compared to TDCPVE and improves velocity estimation accuracy by 16.1% to 60.9% compared to DVE. Moreover, the method does not necessitate the combination of dual-frequency observations, making it particularly valuable for cycle slip detection in scenarios involving mixed single- and dual-frequency observations, which is particularly useful in urban environments.

From PIV to LSPIV: Harnessing deep learning for environmental flow velocimetry

The inference of velocity fields from the displacement of objects and/or fields visible within a series of consecutive images over known time intervals has been explored extensively within experimental fluid dynamics. Real image sequences of environmental hydrodynamic flows, however, pose additional challenges for velocity field inference due to factors such as lighting inhomogeneity, particle density, camera orientation and stability. Here we investigate the performance of classical and deep learning based velocity estimation methods on three experimental datasets; a hydrodynamics laboratory dataset of different flow types and two open-source datasets of aerial river footage from field campaigns. The river datasets are accompanied by observational datasets of in-situ measurements. In particular, we investigate the generalisation of deep learning based methods from ideal training conditions to real images. We consider three deep learning approaches; recurrent all-pairs-field transforms (RAFT), a physics-informed approach and an unsupervised learning approach (UnLiteFlowNet-PIV). Results indicate that RAFT, which achieves state-of-the-art performance on particle image datasets, showed good generalisation to the laboratory dataset and field imagery. The physics-informed approach performed similarly to RAFT across the laboratory dataset whilst generalisation to drone-based data proved challenging. Across the laboratory dataset, UnLiteFlowNet-PIV showed good performance within wake regions but an underestimation of channel flows and freestream regions with limited vorticity, also suffering under poor seeding density. Limited fine-tuning of UnLiteFlowNet-PIV on laboratory data, however, led to improved performance in these regions, indicating the potential of the unsupervised learning approach for environmental flows where 2D ground truth data sources are unavailable for training.

Gyro-free attitude reorientation control of spacecraft with state and input constraints

This paper introduces an explicit reference governor-based control scheme to address the gyro-free spacecraft attitude reorientation problem, considering specific constraints such as pointing, angular velocity, and input saturation. The proposed control scheme operates in two layers, ensuring the asymptotic stability of the attitude while adhering to the aforementioned constraints. The inner layer employs output feedback control utilizing an angular velocity observer based on immersion and invariance technology. Through an analysis of the geometry associated with the pointing constraint, determination of the upper bound of angular velocity, and optimization of the control input solution, the reference layer establishes a safety boundary described by the invariant set. Additionally, we introduce the dynamic factor related to the angular velocity estimation error into the invariant set to prevent states from exceeding the constraint set due to unmeasurable angular velocity information. The shortest guidance path is then designed in the reference layer. Finally, we substantiate the efficacy of the proposed constrained attitude control algorithm through numerical simulations.

A simplified method for estimating the alpha coefficient in surface velocity based river discharge measurements

Remote sensing techniques for river monitoring facilitate faster measurement campaigns compared to traditional methods, reduce risks to personnel and instruments, and allow measurements under critical flow conditions. An alpha coefficient (α) is commonly employed to convert surface velocities, obtained by contactless techniques, into depth-averaged velocities, which are used for the application of the velocity-area method for assessing discharge. Some optical-based software programs use a constant α value, based on a theoretical “standard”. However, analyses of empirical vertical velocity profiles in real cases reveal that α can significantly deviate from this standard due to various factors (roughness, turbulence, etc.).This study analyzes several ADCP (Acoustic Doppler Current Profiler)-based measurements in Sicily, Italy, to explore factors influencing flow velocity distribution and potential errors from using the standard α for discharge estimation via surface velocity-based methods. The results confirmed substantial variability in α, which is functionally related to some geometric factors characterizing the cross-section shape and the specific vertical where the velocity profile is computed. The generated dataset of empirical α values is also used to implement an Artificial Neural Network (ANN), offering a straightforward tool suitable for non-contact techniques. The ANN predicts α at any vertical of a measurement transect as a function of variables however necessary for discharge assessment by non-intrusive methods, leading to depth-averaged velocity estimates from surface velocities that are more accurate than those derived from conventional approaches, as demonstrated by four test cases.

Full 2D optimized window coefficients for improved range velocity estimation in 5G joint communication and sensing

Abstract This work presents an approach for optimization of window coefficients for 5G user equipment side sensing, using orthogonal frequency division multiplexing radar-based range and velocity estimation, based on the sounding reference signal (SRS) from the 5G New Radio (NR) standard. The signal configuration and the corresponding waveform are generated in compliance with the 3rd Generation Partnership Project (3GPP) standard for 5G. The limitations of conventional signal processing for resources available for sensing with the SRS are highlighted. The proposed approach, which optimizes the window coefficients to improve the sensing capabilities, is implemented through two methods. The first method employs a decoupled optimization strategy for range and velocity, showing high computational efficiency. Our results demonstrate that this method significantly improves the peak sidelobe level (PSL) of the velocity profile by over $\mathrm{15}\,\mathrm{dB}$ , although it does not address the issue of diagonally located sidelobes, which occur due to non-uniform resource distribution. The second method adopts a comprehensive full 2D optimization technique. While it requires more computational resources and does not improve the PSL beyond the first method’s achievements, it mitigates the diagonally located sidelobes issue. The level of these have been improved by more than $\mathrm{3}\,\mathrm{dB}$ .

Sensorless Position Control in High-Speed Domain of PMSM Based on Improved Adaptive Sliding Mode Observer

To improve the speed buffering and position tracking accuracy of medium–high-speed permanent magnet synchronous motor (PMSM), a sensorless control method based on an improved sliding mode observer is proposed. By the mathematical model of the built-in PMSM, an improved adaptive super-twisting sliding mode observer is constructed. Based on the LSTA-SMO with a linear term of observation error, a sliding mode coefficient can be adjusted in real time according to the change in rotational speed. In view of the high harmonic content of the output back electromotive force, the adaptive adjustment strategy for the back electromotive force is adopted. In addition, in order to improve the estimation accuracy and resistance ability of the observer, the rotor position error was taken as the disturbance term, and the third-order extended state observer (ESO) was constructed to estimate the rotational speed and rotor position through the motor mechanical motion equation. The proposed method is validated in Matlab and compared with the conventional linear super twisted observer. The simulation results show that the proposed method enables the observer to operate stably in a wide velocity domain and reduces the velocity estimation error to 6.7 rpm and the position estimation accuracy error to 0.0005 rad at high speeds, which improves the anti-interference capability.

Modeling the velocity of evolving lineages and predicting dispersal patterns

Accurate estimation of the dispersal velocity or speed of evolving organisms is no mean feat. In fact, existing probabilistic models in phylogeography or spatial population genetics generally do not provide an adequate framework to define velocity in a relevant manner. For instance, the very concept of instantaneous speed simply does not exist under one of the most popular approaches that models the evolution of spatial coordinates as Brownian trajectories running along a phylogeny. Here, we introduce a family of models-the so-called Phylogenetic Integrated Velocity (PIV) models-that use Gaussian processes to explicitly model the velocity of evolving lineages instead of focusing on the fluctuation of spatial coordinates over time. We describe the properties of these models and show an increased accuracy of velocity estimates compared to previous approaches. Analyses of West Nile virus data in the United States indicate that PIV models provide sensible predictions of the dispersal of evolving pathogens at a one-year time horizon. These results demonstrate the feasibility and relevance of predictive phylogeography in monitoring epidemics in time and space.

Time-lapse 4D full-waveform inversion for ocean-bottom cable seismic data with seawater velocity changes

Time-lapse (4D) full-waveform inversion (FWI) is a valuable technology for high-resolution imaging of reservoir changes caused by hydrocarbon production and CO$_2$ storage. However, it still faces challenges in dealing with non-repeatability issues due to changes in seawater or near-surface velocity between baseline and monitor surveys. Despite recent advances, the ability of 4D FWI to address this problem has rarely been demonstrated. {\color{red}We} investigate the effectiveness of current 4D FWI strategies, including parallel, double-difference, sequential, and common-model strategies, in resolving non-repeatability issues for 4D OBC (ocean-bottom cable) seismic data. Additionally, a three-stage 4D FWI strategy is developed for 4D OBC seismic data, involving the estimation of seawater velocities in the baseline and monitor models, obtaining a good common starting model, and final convergence to obtain subsurface 4D changes. The synthetic data tests conducted with varying levels of seawater velocity changes indicate that among the current strategies tested, the common-model strategy performs the best, and that our new strategy can further enhance the 4D inversion results.

Evidence for Low Effective Stress Within the Crust of the Subducted Gorda Plate from the 2022 December Mw 6.4 Ferndale Earthquake Sequence

Abstract Stress levels on and adjacent to megathrust faults at seismogenic depths remain a key but difficult-to-constrain parameter for assessing seismic hazard in subduction zones. Although strong ground motions have been observed to be generated from distinct, high-stress regions on the down-dip end of the megathrust rupture areas in many great earthquakes, we lack direct constraints on the stress level in the lower seismogenic portion of the Cascadia megathrust. On 20 December 2022, an Mw 6.4 strike-slip earthquake occurred near Ferndale, California, in southern Cascadia and likely ruptured the Gorda slab crust in the lower seismogenic portion, providing an opportunity to assess the stress level in this region. Here, we relocate the Ferndale mainshock and the first two weeks of aftershocks using a high-resolution 3D velocity model and estimate rupture dimensions, directivity, and stress drop for several Mw 4–5 aftershocks and recent earthquakes. The aftershocks define a strike-slip fault in the slab crust striking east-northeast, consistent with the mainshock focal mechanism. The orientation of this fault is about 45° off the ideally oriented fault plane given the stress state in the slab. The aftershock zone is extensive and broad in the forward direction of the mainshock rupture but still constrained within the volume of high VP/VS in the slab crust. Our stress-drop estimates are generally lower for Mw 4–5 earthquakes located in the slab crust compared to those a few kilometers deeper in the slab mantle. Combined, our results support a relatively low effective stress level in the vicinity of the megathrust in the lower portion of the seismogenic zone in southern Cascadia, likely due to elevated fluid pressures. Consequently, the ground motion in the onshore region above this low-stress seismogenic portion in southern Cascadia may not be as intense as that observed during great earthquakes in other subduction zones.

One-way reflection waveform inversion with depth-dependent gradient preconditioning

Summary Reflection Waveform Inversion (RWI) is a technique that uses pure reflection data to estimate subsurface background velocity, relying on evolving seismic images. Conventional RWI operates in a cyclic workflow, with two key components in each cycle—migration and reflection tomography. Conventional RWI may result in suboptimal background velocity estimation, partly due to limited or unresolved resolution within each component in each cycle. While gradient preconditioning with the reciprocal of Hessian information helps resolve this issue in both components of RWI, it becomes impractical for a large number of model parameters. One-way reflection waveform inversion (ORWI) is a reflection waveform inversion technique in which the forward modeling scheme operates in one direction (downward and then upward) via virtual parallel depth levels within the medium. Leveraging the ORWI framework, we decompose and reduce the linear Hessian operator (also known as the approximate Hessian or Gauss-Newton Hessian) into multiple smaller sub-operators. In particular, the diagonal blocks of the mono-frequency approximate Hessian operators, each corresponding to a single depth level within the medium, are extracted and inverted to precondition the corresponding mono-frequency gradients in both the migration and reflection tomography components of ORWI. This depth-dependent gradient preconditioning transforms standard ORWI into a high-resolution, yet computationally feasible version aimed at addressing suboptimal velocity estimation, referred to as high-resolution ORWI (HR-ORWI). The effectiveness of the proposed approach is demonstrated through successful applications to synthetic data examples.

Synthetic Wind Estimation for Small Fixed-Wing Drones

Wind estimation is crucial for studying the atmospheric boundary layer. Traditional methods such as weather balloons offer limited in situ capabilities; besides an Air Data System (ADS) combined with inertial measurements and satellite positioning is required to estimate the wind on fixed-wing drones. As pressure probes are an important constituent of an ADS, they are susceptible to malfunctioning or failure due to blockages, thus affecting the capability of wind sensing and possibly the safety of the drone. This paper presents a novel approach, using low-fidelity aerodynamic models of drones to estimate wind synthetically. In our work, the aerodynamic model parameters are derived from post-processed flight data, in contrast to existing approaches that use expensive wind tunnel calibration for identifying the same. In sum, our method integrates aerodynamic force and moment models into a Vehicle Dynamic Model (VDM)-based navigation filter to yield a synthetic wind estimate without relying on an airspeed sensor. We validate our approach using two geometrically distinct drones, each characterized by a unique aerodynamic model and different quality of inertial sensors, altogether tested across several flights. Experimental results demonstrate that the proposed cross-platform method provides a synthetic wind velocity estimate, thus offering a practical backup to traditional techniques.

Sensitivity of wavelet-based optical flow velocimetry (wOFV) to common experimental error sources

Abstract The influence of several potential error sources and non-ideal experimental effects on the accuracy of a wavelet-based optical flow velocimetry (wOFV) method when applied to tracer particle images is evaluated using data from a series of synthetic flows. Out-of-plane particle displacements, severe image noise, laser sheet thickness reduction, and image intensity non-uniformity are shown to decrease the accuracy of wOFV in a similar manner to correlation-based particle image velocimetry (PIV). For the error sources tested, wOFV displays a similar or slightly increased sensitivity compared to PIV, but the wOFV results are still more accurate than PIV when the magnitude of the non-ideal effects remain within expected experimental bounds. For the majority of test cases, the results are significantly improved by using image pre-processing filters and the magnitude of improvement is consistent between wOFV and PIV. Flow divergence does not appear to have an appreciable effect on the accuracy of wOFV velocity estimation, even though the underlying fluid transport equation on which wOFV is based implicitly assumes that the motion is divergence-free. This is a significant finding for the broader applicability of planar velocimetry measurements using wOFV. Finally, it is noted that the accuracy of wOFV is not reduced notably in regions of the image between tracer particles, as long as the overall seeding density is not too sparse i.e. below 0.02 particles per pixel. This explicitly demonstrates that wOFV (when applied to particle images) yields an accurate whole field measurement, and not only at or adjacent to the discrete particle locations.

Improved analytical solution with optimization constraints using TDOA and FDOA measurements for USV/AUV collaborative localization

The paper introduces an enhanced constrained two-step weighted least squares (WLS) analytic localization algorithm designed to determine the AUV’s position and velocity within the collaborative localization system, utilizing time difference of arrival and frequency difference of arrival measurements. This algorithm integrates optimization constraints between intermediate variables and target motion parameters, producing precise analytical solution based on WLS minimization criterion. The proposed process not only reduces computational burden by providing an analytical solution but also enhances estimation accuracy through the incorporation of optimization constraints. Analytical assessments and statistical simulations highlight the superior estimation accuracy of the proposed algorithm, and demonstrate that the position and velocity estimation accuracy align with the Cramér-Rao lower bound under appropriate measurement noise conditions.

Field Validation of the Superelevation Method for Debris‐Flow Velocity Estimation Using High‐Resolution Lidar and UAV Data

AbstractEstimating flow velocities is key to assessing hazards associated with debris flows. One approach to post‐event velocity estimation is the superelevation method, which uses debris‐flow mudlines to measure the cross‐channel surface inclination, or superelevation, produced by centripetal forces acting on the flow in a bend. Flow velocities are then calculated using a subjective parameterization of the forced vortex equation modified to include a debris‐flow specific correction factor. Subjective parameterization of this equation leads to substantial variability and uncertainty in the resulting flow velocities. We present an analysis of the reliability of the superelevation method using a large UAV‐based data set of 14 debris flows with front velocities of ∼0.8–6.5 m s−1 and cross‐channel surface inclinations of ∼0.6–8.5°, as well as a validation for a single debris flow measured using high‐resolution, high‐frequency 3D lidar data fused to video imagery. The validation event indicates that when the flow surface inclination can be measured directly, the forced vortex equation produces excellent results without needing a correction factor for Froude numbers ranging from 0.7 to 1.5. This finding indicates that the main challenge with the superelevation method lies in obtaining accurate measurements of superelevation from the mudlines, and that a correction factor may serve to compensate for measurement difficulties rather than variable flow properties. For very small and highly subcritical flows, the superelevation method may generate a large overestimation of flow velocities.