Velocity Estimation Research Articles

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.

Integrating deep learning and discrete cosine transform for surface waves full waveform inversion

Summary Accurate estimations of near-surface S-wave velocity (Vs) models hold particular significance in geological and engineering investigations. On the one hand, the popular Multichannel Analysis of Surface Waves (MASW) is limited to the 1D and the plane wave assumptions. On the other hand, the more advanced and computationally expensive full-waveform inversion (FWI) approach is often solved within a deterministic framework that hampers an accurate uncertainty assessment and makes the final predictions heavily reliant on the starting model. Here we combine deep learning with Discrete Cosine Transforms (DCT) to solve the FWI of surface waves and to efficiently estimate the inversion uncertainties. Our neural network approach effectively learns the inverse non-linear mapping between DCT-compressed seismograms and DCT-compressed S-velocity models. The incorporation of DCT into the deep learning framework provides several advantages: it notably reduces parameter space dimensionality and alleviates the ill-conditioning of the problem. Additionally, it decreases the complexity of the network architecture and the computational cost for the training phase compared to training in the full domain. A Monte Carlo simulation is also used to propagate the uncertainties from the data to the model space. We first test the implemented inversion method on synthetic data to showcase the generalization capabilities of the trained network and to explore the implications of incorrect noise assumptions in the recorded seismograms and inaccurate wavelet estimations. Further, we demonstrate the applicability of the implemented method to field data. In this case, available borehole information is used to validate our predictions. In both the synthetic and field applications, the predictions provided by the proposed method are compared with those of a deterministic FWI and the outcomes of a network trained in the full data and model spaces. Our experiments confirm that the implemented deep-learning inversion efficiently and successfully solves the FWI problem and yields more accurate and stable results than a network trained without the DCT compression. This opens the possibility to efficiently train a neural network that provides accurate instantaneous predictions of Vs near-surface models and related uncertainties.

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.

Using Rainfall‐Induced Groundwater Temperature Response to Estimate Lateral Flow Velocity

AbstractThis study introduces a novel heat tracing method for estimating lateral groundwater flow velocity induced and sustained by heavy rainfall events in lowland areas, leveraging the distinct temperature difference between rainfall and groundwater. The method is motivated by the observation that the rainfall‐induced groundwater temperature signal dissipates along the flow path. To explain the observed temperature anomaly and then estimate the lateral flow velocity, we develop a semi‐analytical model for heat transport in the aquifer, accounting for conduction losses to adjacent layers. Our findings reveal that interactions between the aquifer, vadose zone, and bedrock significantly influence the temperature signal, thereby affecting velocity estimation. Inaccuracies in measured aquifer properties, such as thickness, porosity, and thermal conductivity of surrounding layers, increase the uncertainty of velocity estimates. However, variations in aquifer thermal conductivity have a minimal effect on the method's overall accuracy. When estimating multiple parameters, velocity estimates tend to be less reliable, especially if aquifer porosity remains uncertain. This is due to the challenges of simultaneously inverting both velocity and porosity. Overall, this work underscores the potential of using heat as a tracer for assessing lateral groundwater flow following rainfall, offering a practical, low‐cost solution applicable in a wide range of settings.

Impact of Systematic Modeling Uncertainties on Kilonova Property Estimation

The precise atomic structure and therefore the wavelength-dependent opacities of lanthanides are highly uncertain. This uncertainty introduces systematic errors in modeling transients like kilonovae and estimating key properties such as mass, characteristic velocity, and heavy metal content. Here, we quantify how atomic data from across the literature as well as choices of thermalization efficiency of r-process radioactive decay heating impact the light curve and spectra of kilonovae. Specifically, we analyze the spectra of a grid of models produced by the radiative transfer code Sedona that span the expected range of kilonova properties to identify regions with the highest systematic uncertainty. Our findings indicate that differences in atomic data have a substantial impact on estimates of lanthanide mass fraction, spanning approximately 1 order of magnitude for lanthanide-rich ejecta, and demonstrate the difficulty in precisely measuring the lanthanide fraction in lanthanide-poor ejecta. Mass estimates vary typically by 25%–40% for differing atomic data. Similarly, the choice of thermalization efficiency can affect mass estimates by 20%–50%. Observational properties such as color and decay rate are highly model dependent. Velocity estimation, when fitting solely based on the light curve, can have a typical error of ∼100%. Atomic data of light r-process elements can strongly affect blue emission. Even for well-observed events like GW170817, the total lanthanide production estimated using different atomic data sets can vary by a factor of ∼6.

Evaluation of Norm-Constrained Capon Method on Improving Doppler Peak Localization of Antenna-Arrayed High-Frequency Coastal Radar

Abstract Antenna-arrayed high-frequency coastal radar is widely used to monitor the ocean and obtain metocean parameters such as sea surface current, sea wave height, and surface wind. However, the accuracy of these parameters can be significantly influenced by the spectral width and Doppler velocity of the sea echo signals across azimuthal directions, and insufficient spectrum resolution increases uncertainties in the estimates of spectral width and Doppler velocity. To address this, we demonstrate an alternative approach to beamforming by utilizing the norm-constrained Capon (NC-Capon) method to enhance the Doppler spectral resolution and improve the localization accuracy of the spectral peaks. The efficacy of the NC-Capon method is exemplified through an application to a coastal radar dataset collected from 16 receiving channels, operated at a central frequency of 27.75 MHz. A comparative investigation of the NC-Capon beamforming method with the conventional Fourier beamforming method showed that the widths of the spectral peaks at different range cells and azimuthal angles are noticeably improved at lower signal-to-noise ratio (SNR) conditions. Given this, the NC-Capon beamforming method exhibits more robustness to noise and could effectively enhance the concentration of the radar sea echo signals in the Doppler frequency spectrum, thereby reducing the uncertainties of the spectral width and Doppler/radial velocity of the first-order sea echoes. These characteristics are substantiated by the comparative analysis of spectral parameters between the two beamforming methods across various ranges, beamforming angles, and SNR levels. Finally, the computed radial velocities are benchmarked against in situ measurements obtained from a bottom-mounted acoustic current profiler to confirm the validity of the NC-Capon method. Significance Statement We have recently implemented a high-frequency coastal radar equipped with an antenna array to monitor metocean variables within the offshore waters adjacent to Taichung Harbor in Taiwan. Our study focuses on enhancing the Doppler spectral resolution of radar sea echoes by employing an adaptive beamforming technique known as the norm-constrained Capon method. In comparison with the traditional linear beamforming approach, the norm-constrained Capon method demonstrates superior noise resilience. We anticipate that the norm-constrained Capon method holds promise as a dependable approach for refining the accuracy of metocean parameters obtained from antenna-arrayed high-frequency coastal radar systems.

Experimental Investigation on void structure velocity and estimation of velocity jump boundaries in a 3X3 rod bundle at low gas flow conditions

Experimental Investigation on void structure velocity and estimation of velocity jump boundaries in a 3X3 rod bundle at low gas flow conditions

A Real-Time and Interactive Fluid Modeling System for Mixed Reality.

Within the realm of mixed reality, the capability to dynamically render environmental effects with high realism plays a crucial role in amplifying user engagement and interaction. Fluid dynamics, in particular, stand out as essential elements for crafting immersive virtual settings. This includes the simulation of phenomena like smoke, fire, and clouds, which are instrumental in enriching the virtual experience. This work showcases a cutting-edge system developed to produce dynamic and interactive fluid effects that mirror real captured data in real-time for mixed reality applications. This innovative system seamlessly incorporates fluid reconstruction alongside velocity estimation processes within the Unity engine environment. Our approach leverages a novel physics-based differentiable rendering technique, grounded in the principles of light transport in participating media, to simulate the intricate behaviors of fluid while ensuring high fidelity in visual appearance. To further enhance realism, we have expanded our framework to include the estimation of velocity fields, addressing the critical need for fluid motion simulation. The practical application of these techniques demonstrates the system's capacity to offer a robust platform for fluid modeling in mixed reality environments. Through extensive evaluations, we illustrate the effectiveness of our approach in various scenes, underscoring its potential to transform mixed reality content creation by providing developers with the tools to incorporate highly realistic and interactive fluid seamlessly.

Velocity Estimation of Passive Target Based on Sparse Bayesian Learning Cross-Spectrum.

To solve the poor performance or even failure of the cross-spectrum (CS) method in hydroacoustic weak-target passive velocimetry, a sparse Bayesian learning cross-spectrum method (SBL-CS), combining phase compensation with sparse Bayesian learning (SBL) is proposed in this paper. Firstly, the cross-correlation sound intensity is taken as the observation quantity and compensates for each frequency point of the cross-spectrum, which enables the alignment of cross-spectrum results at different frequencies. Then, the inter-correlation sound intensity of all frequencies is fused in the iterative estimation of the target velocity, verifying the proposed method's ability to suppress the background noise when performing multi-frequency processing. The simulation results show that the proposed method is still effective in estimating the target velocity when the CS method fails and that the performance of the proposed method is better than the CS method with a decrease in SNR. As verified using the SWellEx-96 sea trial dataset, the RMSE of the proposed method for surface vessel speed measurement is 0.3545 m/s, which is 46.1% less than the traditional CS method, proving the feasibility and effectiveness of the proposed SBL-CS method for the estimation of the radial speed of a passive target.

Evaluating the performance of automatic wave speed estimators of natural shear waves to assess myocardial stiffness

Abstract Introduction Cardiac Shear Wave Elastography is an emerging non-invasive technique using ultrafast echocardiographic imaging. Natural shear waves (SW) occur after mitral valve closure (MVC) and aortic valve closure (AVC), and their propagation velocities are directly related to myocardial stiffness. However, the current assessment of SW velocities lacks a gold standard method, and relies on manual measurements with their inherent variability. Purpose To explore new automated methods for shear wave velocity estimates, aiming to achieve greater accuracy, less variability, and high feasibility. Methods To visualize shear waves, anatomical M-modes were drawn (4±1 cm) along ventricular septum in parasternal long axis views (PLAX), acquired with high-frame-rate echocardiography (1055±159 Hz). M-modes were colour coded for tissue acceleration. SWs appear as tilted green bands after MVC & AVC, the slope of which represents the SW propagation velocity. One hundred-twenty different slopes from healthy volunteers (n=67) and patients (n=53) were measured manually and with 9 different automatic methods. All automatic methods were initiated by indicating the approximate position of the shear wave in the M-mode with a mouse click of the user. Measurements were repeated three times to assess measurement reproducibility. (figure 1, A). In order to determine the influence of display settings, three different acceleration colour scales were tested (0.5, 1, 2 m/s²). (Figure 1, B). Failure of correctly identifying shear waves was counted to assess feasibility. Due to the lack of a gold standard, manual measurements of an experienced reader served as reference. Results Three of nine automatic methods: Radon Transformation (RT), Regression Segmentation Maximum value (RM), Regression Segmentation Weighted Maximum (RMW), revealed the least variability with repeated measurements (0.0±0.0 m/s for all) and colour scales (0.0±0.0, 0.1±0.2 and 0.1±0.2 m/s, resp.). Those three methods showed moderate to good agreement (r=0.66, ICC 0.77 [CI 0.64- 0.85]; r= 0.67, ICC 0.75 [CI 0.50 -0.86]; and r= 0.77, ICC 0.85 [CI 0.73-0.90], resp.) with manual measurements (r=0.96, ICC 0.98 [CI 0.97- 0.99] (figure 2). Conclusion RT, RM, and RMW showed a good reproducibility while RMW showed the best agreement with manual measurements. Our data suggest, that the RMW method could offer an objective alternative for SW velocity estimations. Further research is needed to explore their applicability in clinical scenarios.

Determine deformation energy in side impact by incorporating contact area in crash algorithm

This research paper explores potential enhancements to the CRASH algorithm by proposing a hypothesis that relates deformation to applied stress instead of force. By incorporating stress instead of force, the calculation can account for the contact area, leading to a more precise estimation of impact velocity, particularly in side impacts. An initial evaluation of this energy absorption calculation formula is presented, focusing on side impacts in vehicle "2022 Hyundai Ion." Two side impact reports for the vehicle from the National Highway Traffic Safety Administration (NHTSA) database were utilized. One report involved the vehicle tilted at a 45-degree angle against a fixed pole with a 254 mm diameter, while the other examined the vehicle colliding with a moving deformable barrier (MDB) at various speeds. Additionally, a Monte Carlo simulation was conducted to validate the model's applicability. The verification process involved estimating stiffness coefficients from the first report and employing them to calculate energy absorption during the crash against the moving deformable barrier. The analysis demonstrates promising improvements in accurately calculating deformation energy absorbed during impacts.

Mechanical wave velocities in acute myocardial infarction: an exploratory study using three-dimensional high frame rate echocardiography

Abstract Background Evaluation of cardiac function is crucial for management of patients with acute myocardial infarction (AMI). Recently, high frame rate (HFR) echocardiography has enabled identification of fast-propagating mechanical waves (MW) within the myocardium. These waves have the potential to offer unique insights into myocardial tissue properties, including edema and fibrosis. However, the potential of 3D HFR echocardiography to accurately quantify MW velocities and thereby assess myocardial stiffness in AMI patients remains unexplored. Purpose To assess the feasibility of a novel method for estimating MW velocities using 3D HFR echocardiography in patients with AMI compared to healthy controls. Methods Twenty patients with first time ST-elevation myocardial infarction were included within 48 hours after reperfusion therapy. We used a commercially available ultrasound scanner to acquire conventional 3D high-quality (~20 frames per second) acquisitions and subsequent 3D HFR recordings (~820 frames per second). The high-quality recording was used to extract a myocardial mask to define the region of interest in the HFR acquisition to detect the atrial kick wave and generate a comprehensive 3D map of MW propagation and velocities. We compared MW velocities between these patients and twenty age- and sex-matched healthy controls. Results MW velocities were successfully measured in 93% of participants (17 patients and 20 controls). Global MW velocities were significantly higher in AMI patients compared to healthy controls (2.1±0.6m/s vs. 1.5±0.2m/s, p<0.001). Territorial values, corresponding to the theoretical perfusion area of the three main coronary arteries, were significantly higher in infarct-related territories in patients when compared to same territories in controls for all coronary territories: Right coronary artery: 1.9±0.7 m/s vs. 1.4±0.3m/s, p<0.05; Circumflex artery: 3.1±1.5m/s vs. 1.7±0.4m/s, p<0.01; and Left anterior descending artery: 1.8±0.5m/s vs. 1.4±0.2m/s, p<0.01. Conclusions Estimation of MW velocities using a novel 3D HFR echocardiography method was feasible in most patients with AMI and all healthy controls. MW velocities were higher in patients compared to healthy controls and infarcted compared to non-infarcted coronary territories. These findings confirm the potential of non-invasive myocardial tissue characterization by MW imaging.Boxplot of MW velocities3D HFR MW visualisation