Robust Velocity Research Articles

Velocity vector estimation in automotive radar networks

Abstract In this work, we develop a method for robust single-cycle measurement velocity vector estimation for automotive radar. Building upon our previous work, we introduce a methodology that leverages spatial diversity for accurate estimation of the velocity vector of targets in the medium to close ranges. We extend our initial conceptual framework, addressing limitations from our first approach and proposing necessary enhancements for real-world applicability. Our improved process excels in target separation, identification, and velocity vector estimation, proving effective across various scenarios and minimizing errors. The system, tested on pedestrians and metal targets, presents a promising avenue for exploring its performance with varying target sizes. Simultaneously, our in-depth study on Doppler-multiplex modulation reveals new relevant constraints, prompting a modulation change for improved response separation. Despite the necessity of increasing module numbers for enhanced performance, our structured approach to target itemization and classification positions our methodology as a valuable framework for future systems, offering a comprehensive solution to diverse challenges in target estimation and classification within the automotive landscape.

A frequency-domain beamforming procedure for extracting Rayleigh wave attenuation coefficients and small-strain damping ratio from 2D ambient noise array measurements

The small-strain damping ratio plays a crucial role in assessing the response of soil deposits to earthquake-induced ground motions and general dynamic loading. The damping ratio can theoretically be inverted for after extracting frequency-dependent Rayleigh wave attenuation coefficients from wavefields collected during surface wave testing. However, determining reliable estimates of in situ attenuation coefficients is much more challenging than achieving robust phase velocity dispersion data, which are commonly measured using both active-source and ambient-wavefield surface wave methods. This article introduces a new methodology for estimating frequency-dependent attenuation coefficients through the analysis of ambient noise wavefield data recorded by two-dimensional (2D) arrays of surface seismic sensors for the subsequent evaluation of the small-strain damping ratio. The approach relies on the application of an attenuation-specific wavefield conversion and frequency-domain beamforming. Numerical simulations are employed to verify the proposed approach and inform best practices for its application. Finally, the practical efficacy of the proposed approach is showcased through its application to field data collected at a deep, soft soil site in Logan, Utah, USA, where phase velocity and attenuation coefficients are extracted from surface wave data and then simultaneously inverted to develop deep shear wave velocity and damping ratio profiles.

Robust estimation of Shear Wave group velocity for ultrasound elastography based on frequency domain analysis.

Shear wave elastography (SWE) is of great significance in measuring the elasticity and in evaluating mechanical properties of biological tissues. The elasticity of biological tissues can be reflected by measuring the propagation velocity of shear waves. Therefore, accurate estimation of shear wave velocity is crucial. In this study, we proposed a robust estimation method based on a cyclic shifting algorithm (CSA) for measuring shear wave group velocity in homogeneous media. To validate the utility of the algorithm, we conducted accuracy analysis and robustness analysis with different noise levels in the digital phantom used for the standardization of shear wave velocity by Quantitative Imaging Biomarker Alliance (QIBA) of the Radiological Society of North America. The estimated shear wave velocities (SWV) of the elastic digital phantoms with Young's moduli 3 kPa, 6 kPa, 15 kPa, 30 kPa are 1.0156 m/s, 1.4065 m/s, 2.1875 m/s, 3.1250 m/s, respectively. When adding Gaussian white noise with 0 dB, the relative errors of the estimated SWVs are 2.5%, 4.8%, 11.8%, 20.5%, respectively. The estimated SWVs in the gelatin phantom with gelatin concentration of 7% and 10% are 2.0442 m/s and 3.1237 m/s. Compared with the existing two representative estimation algorithms, the estimation algorithm proposed in this paper has a higher anti-noise performance due to effective energy accumulation in the frequency domain. The proposed SWV method based on CSA in frequency domain is a robust shear wave group velocity estimation method, which seems to be a useful tool in homogeneous media for ultrasound elastography.

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

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

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.

Lyα Emission Line Profiles of Extreme [O iii]- emitting Galaxies at z ≳ 2: Implications for Lyα Visibility in the Reionization Era

JWST observations have recently begun delivering the first samples of Lyα velocity profile measurements at z > 6, opening a new window into the reionization process. Interpretation of z ≳ 6 line profiles is currently stunted by limitations in our knowledge of the intrinsic Lyα profile (before encountering the intergalactic medium (IGM)) of the galaxies that are common at z ≳ 6. To overcome this shortcoming, we have obtained resolved (R ∼ 3900) Lyα spectroscopy of 42 galaxies at z = 2.1–3.4 with similar properties as are seen at z > 6. We quantify a variety of Lyα profile statistics as a function of [O iii]+Hβ equivalent width (EW). Our spectra reveal a new population of z ≃ 2–3 galaxies with large [O iii]+Hβ EWs (>1200 Å) and a large fraction of Lyα flux emerging near the systemic redshift (peak velocity ≃0 km s−1). These spectra indicate that low-density neutral hydrogen channels are able to form in a subset of low-mass galaxies (≲1 × 108 M ⊙) that experience a burst of star formation (sSFR > 100 Gyr−1). Other extreme [O iii] emitters show weaker Lyα that is shifted to higher velocities (≃240 km s−1) with little emission near the line center. We investigate the impact the IGM is likely to have on these intrinsic line profiles in the reionization era, finding that the centrally peaked Lyα emitters should be strongly attenuated at z ≳ 5. We show that these line profiles are particularly sensitive to the impact of resonant scattering from infalling IGM and can be strongly attenuated even when the IGM is highly ionized at z ≃ 5. We compare these expectations against a new database of z ≳ 6.5 galaxies with robust velocity profiles measured with JWST/NIRSpec.

Trajectory Tracking with Obstacle Avoidance for Nonholonomic Mobile Robots with Diamond-Shaped Velocity Constraints and Output Performance Specifications.

In this paper, we address the trajectory-/target-tracking and obstacle-avoidance problem for nonholonomic mobile robots subjected to diamond-shaped velocity constraints and predefined output performance specifications. The proposed scheme leverages the adaptive performance control to dynamically adjust the user-defined output performance specifications, ensuring compliance with input and safety constraints. A key feature of this approach is the integration of multiple constraints into a single adaptive performance function, governed by a simple adaptive law. Additionally, we introduce a robust velocity estimator with a priori-determined performance attributes to reconstruct the unmeasured trajectory/target velocity. Finally, we validate the effectiveness and robustness of the proposed control scheme, through extensive simulations and a real-world experiment.

A Compressible Formulation of the One-Fluid Model for Two-Phase Flows

In this paper, we introduce a compressible formulation for dealing with 2D/3D compressible interfacial flows. It integrates a monolithic solver to achieve robust velocity–pressure coupling, ensuring precision and stability across diverse fluid flow conditions, including incompressible and compressible single-phase and two-phase flows. Validation of the model is conducted through various test scenarios, including Sod’s shock tube problem, isothermal viscous two-phase flows without capillary effects, and the impact of drops on viscous liquid films. The results highlight the ability of the scheme to handle compressible flow situations with capillary effects, which are important in computational fluid dynamics (CFD).

Wind velocity estimates from wave observing platforms

ABSTRACT Near-surface ocean wind measurements are important for weather forecasting, determining surface transports, and estimating air-sea interactions; however, in-situ wind observations are often limited. Previous studies indicate that the equilibrium range of the surface gravity wave spectrum can be used to estimate surface wind velocity. This approach is tested using spectral wave measurements from the Coastal Data Information Program (CDIP) buoy array off Monterey, California. Quality controlled wind vectors inferred from wave spectra are statistically compared to measurements from a nearby National Data Buoy Center (NDBC) buoy, demonstrating strong agreement with the control observations with root mean square errors for speed and direction of 1.8 m/s for all wind speeds and 13.2° for wind speeds greater than 7 m/s. We expand this estimation method to account for biofouling, which causes high-frequency damping of the wave spectrum, and the effects of form factor, which impact the platform’s dynamic response to high-frequency waves. The method produces wind-proxy measurements solely from wave spectra and wave-based drag parametrizations, making it useful for operational integration. This work demonstrates the ability to make robust wind velocity estimates using wave data from multiple sources, increasing the coverage of wind information over the coastal and open ocean.

Microseismic Velocity Inversion Based on Deep Learning and Data Augmentation

Microseismic monitoring plays an essential role for reservoir characterization and earthquake disaster monitoring and early warning. The accuracy of the subsurface velocity model directly affects the precision of event localization and subsequent processing. It is challenging for traditional methods to realize efficient and accurate microseismic velocity inversion due to the low signal-to-noise ratio of field data. Deep learning can efficiently invert the velocity model by constructing a mapping relationship from the waveform data domain to the velocity model domain. The predicted and reference values are fitted with mean square error as the loss function. To reduce the feature mismatch between the synthetic and real microseismic data, data augmentation is also performed using correlation and convolution operations. Moreover, a hybrid training strategy is proposed by combining synthetic and augmented data. By testing real microseismic data, the results show that the Unet is capable of high-resolution and robust velocity prediction. The data augmentation method complements more high-frequency components, while the hybrid training strategy fully combines the low-frequency and high-frequency components in the data to improve the inversion accuracy.

Robust Source and Velocity Inversion for Deep Seismic Reflection Profile at Kumkol Basin by Double-Time-Shift Waveform Inversion

Robust Source and Velocity Inversion for Deep Seismic Reflection Profile at Kumkol Basin by Double-Time-Shift Waveform Inversion

Adaptive Integration of Active-Passive Seismic Data for Robust Velocity Inversion With Deep Learning

Adaptive Integration of Active-Passive Seismic Data for Robust Velocity Inversion With Deep Learning

Identification of challenging gas-bearing reservoir based on machine learning (ML) and computed conversion-based AVO analysis: a study from Jaisalmer Sub-basin, India

Amplitude variation with offset (AVO) analysis is an important tool for identifying natural gas-bearing reservoirs. The changes in seismic amplitudes based on the variation of density and velocity of the rock matrix are captured through the AVO analysis. The current work was performed in the Ghotaru region of the Jaisalmer Sub-basin area, where limited and discrete hydrocarbon discoveries were observed from the Lower Goru Formation during the earlier various exploration campaigns. The discrete nature of the reservoir lithofacies developed challenging scenarios for the successful exploratory campaign. The campaign encountered more difficulties because of limited advanced datasets, which affected the study to capture the extension of hydrocarbon-bearing reservoir lithofacies and its characterization towards a successful exploration campaign. This study shows the way to overcome these challenges using an existing conventional dataset. The study shows the possibility of AVO analysis using a post-stack seismic dataset. A unique conversion method from post-stack to pre-stack seismic is introduced in this study based on the uses of the integrated velocity model. An integrated, robust velocity model was developed with consideration of anisotropy factors. Introducing a machine learning-based algorithm in the petrophysical study, including the conventional approach, provides a robust validation of this work. Intercept (A) and gradient (B) are the basic outcome of AVO analysis. The well-based study and AVO analysis based on intercept (A) and gradient (B) complement each other for finding hydrocarbon-bearing reservoir rock. Cross-plots and AVO analysis show the reservoir's lithofacies extension and fluids. The study reveals the potential of natural gas retained in the Lower Goru Formation, which is composed of patchy sandstone. Two AVO classes (Class I and Class III) of gas-bearing sandstone have been identified in this study. This study presents a unique method for identifying natural gas reservoirs with limited old conventional data.

Real-time gesture-based control of UAVs using multimodal fusion of FMCW radar and vision

Gesture-based control has gained prominence as an intuitive and natural means of interaction with unmanned aerial vehicles (UAVs). This paper presents a real-time gesture-based control system for UAVs that leverages the multimodal fusion of Frequency Modulated Continuous Wave (FMCW) radar and vision sensors, aiming to enhance user experience through precise and responsive UAV control via hand gestures. The research focuses on developing an effective fusion framework that combines the complementary advantages of FMCW radar and vision sensors. FMCW radar provides robust range and velocity measurements, while vision sensors capture fine-grained visual information. By integrating data from these modalities, the system achieves a comprehensive understanding of hand gestures, resulting in improved gesture recognition accuracy and robustness. The proposed system comprises three main stages: data acquisition, gesture recognition, and multimodal fusion. In the data acquisition stage, synchronized data streams from FMCW radar and vision sensors are captured. Then, machine learning algorithms are employed in the gesture recognition stage to classify and interpret hand gestures. Finally, the multimodal fusion stage aligns and fuses the data, creating a unified representation that captures the spatial and temporal aspects of hand gestures, enabling real-time control commands for the UAV. Experimental results demonstrate the system‘s effectiveness in accurately recognizing and responding to hand gestures. The multimodal fusion of FMCW radar and vision sensors enables a robust and versatile gesture-based control interface.

Dependence of peculiar velocity on the host properties of the gravitational wave sources and its impact on the measurement of Hubble constant

ABSTRACT Accurate measurement of the Hubble constant from standard sirens such as the gravitational wave (GW) sources with electromagnetic counterparts relies on the robust peculiar velocity correction of the redshift of the host galaxy. We show in this work that the peculiar velocity of the host galaxies exhibits a correlation with the properties of the host galaxy primarily such as its stellar mass and this correlation also evolves with redshift. As the galaxies of higher stellar mass tend to form in galaxies with higher halo masses which are located in spatial regions having a non-linear fluctuation in the density field of the matter distribution, the root mean square peculiar velocity of more massive galaxies is higher. As a result, depending on the formation channel of the binary compact objects, the peculiar velocity contamination to the galaxies will be different. The variation in the peculiar velocity of the host galaxies can lead to a significant variation in the estimation of the Hubble constant inferred using sources such as binary neutron stars. For the network of GW detectors such as LIGO-Virgo-KAGRA (LVK), LVK+LIGO-India, and Cosmic Explorer + Einstein Telescope, the variation in the precision of Hubble constant inferred from 10 bright siren events can vary from $\sim 5.4 - 6~{{\ \rm per \, cent}}$, $\sim 4.5 - 5.3~{{\ \rm per \, cent}}$, and $\sim 1.1 - 2.7~{{\ \rm per \, cent}}$, respectively. The impact of such a correlation between peculiar velocity and stellar mass on the inference of the Hubble constant is not only limited to GW sources but also applicable to type-Ia supernovae.

Spatiotemporal frequency domain analysis for blood velocity measurement during embolization procedures.

Currently, determining procedural endpoints and treatment efficacy of vascular interventions is largely qualitative and relies on subjective visual assessment of digital subtraction angiography (DSA) images leading to large interobserver variabilities and poor reproducibility. Quantitative metrics such as the residual blood velocity in embolized vessel branches could help establish objective and reproducible endpoints. Recently, velocity quantification techniques based on a contrast enhanced X-ray sequence such as qDSA and 4D DSA have been proposed. These techniques must be robust, and, to avoid radiation dose concerns, they should be compatible with low dose per frame imageacquisition. To develop and evaluate a technique for robust blood velocity quantification from low dose contrast enhanced X-ray image sequences that leverages the oscillating signal created by pulsatile bloodflow. The proposed spatiotemporal frequency domain (STF) approach quantifies velocities from time attenuation maps (TAMs) representing the oscillating signal over time for all points along a vessel centerline. Due to the time it takes a contrast bolus to travel along the vessel centerline, the resulting TAM resembles a sheared sine wave. The shear angle is related to the velocity and can be determined in the spatiotemporal frequency domain after applying the 2D Fourier transform to the TAM. The approach was evaluated in a straight tube phantom using three different radiation dose levels and compared to ultrasound transit-time-based measurements. The STF velocity results were also compared to previously published approaches for the measurement of blood velocity from contrast enhanced X-ray sequences including shifted least squared (SLS) and phase shift (PHS). Additionally, an in vivo porcine study (n = 8) was performed where increasing amounts of embolic particles were injected into a hepatic or splenic artery with intermittent velocity measurements after each injection to monitor the resulting reduction invelocity. At the lowest evaluated dose level (average air kerma rate 1.3 mGy/s at the interventional reference point), the Pearson correlation between ultrasound and STF velocity measurements was . This was significantly higher ( ) than corresponding correlation results between ultrasound and the previously published SLS and PHS approaches ( and , respectively). In the in vivo study, a reduction in velocity was observed in of cases after injection of 1 mL, after 3 mL, and after 4 mL of embolicparticles. The results show good agreement of the spatiotemporal frequency domain approach with ultrasound even in low dose per frame image sequences. Additionally, the in vivo study demonstrates the ability to monitor the physiological changes due to embolization. This could provide quantitative metrics during vascular procedures to establish objective and reproducibleendpoints.

Autocorrelation analysis-based OCT velocimetry for axial blood flow velocity imaging of the cerebral capillary network.

The accurate measurement of blood flow velocity in the capillary network is challenging due to the small size of the vessels and the slow flow of red blood cells (RBCs) within the vessel. Here, we introduce an autocorrelation analysis-based optical coherence tomography (OCT) method that takes less acquisition time to measure the axial blood flow velocity in the capillary network. The axial blood flow velocity was obtained from the phase change in the decorrelation period of the first-order field autocorrelation function (g1) of the OCT field data, which was acquired with M-mode acquisition (repeated A-scans). The rotation center of g1 in the complex plane was first re-centralized to the origin, then the phase change due to the movement of RBCs was extracted in the g1 decorrelation period which is usually 0.2-0.5 ms. In phantom experiments, the results suggest that the proposed method could accurately measure the axial speed with a wide range of 0.5-15 mm/s. We further tested the method on living animals. Compared with the phase-resolved Doppler optical coherence tomography (pr-DOCT), the proposed method can obtain robust axial velocity measurements with more than five times shorter acquisition time.

Advanced Fourier migration for Plane-Wave vector flow imaging

Ultrafast ultrasound imaging modalities have been studied extensively in the ultrasound community. It breaks the compromise between the frame rate and the region of interest by imaging the whole medium with wide unfocused waves. Continuously available data allow monitoring fast transient dynamics at hundreds to thousands of frames per second. This feature enables a more accurate and robust velocity estimation in vector flow imaging (VFI). On the other hand, the huge amount of data and real-time processing demands are still challenging in VFI. A solution is to provide a more efficient beamforming approach with smaller computation complexity than the conventional time-domain beamformer like delay-and-sum (DAS). Fourier-domain beamformers are shown to be more computationally efficient and can provide equally good image quality as DAS. However, previous studies generally focus on B-mode imaging. In this study, we propose a new framework for VFI which is based on two advanced Fourier migration methods, namely, slant stack migration (SSM) and ultrasound Fourier slice beamform (UFSB). By carefully modifying the beamforming parameters, we successfully apply the cross-beam technique within the Fourier beamformers. The proposed Fourier-based VFI is validated in simulation studies, in vitro, and in vivo experiments. The velocity estimation is evaluated via bias and standard deviation and the results are compared with conventional time-domain VFI using the DAS beamformer. In the simulation, the bias is 6.4%, −6.2%, and 5.7%, and the standard deviation is 4.3%, 2.4%, and 3.9% for DAS, UFSB, and SSM, respectively. In vitro studies reveal a bias of 4.5%, −5.3%, and 4.3% and a standard deviation of 3.5%, 1.3%, and 1.6% from DAS, UFSB, and SSM, respectively. The in vivo imaging of the basilic vein and femoral bifurcation also generate similar results using all three methods. With the proposed Fourier beamformers, the computation time can be shortened by up to 9 times and 14 times using UFSB and SSM.

Near-infrared and Optical Nebular-phase Spectra of Type Ia Supernovae SN 2013aa and SN 2017cbv in NGC 5643

We present multiwavelength time-series spectroscopy of SN 2013aa and SN 2017cbv, two Type Ia supernovae (SNe Ia) on the outskirts of the same host galaxy, NGC 5643. This work utilizes new nebular-phase near-infrared (NIR) spectra obtained by the Carnegie Supernova Project-II, in addition to previously published optical and NIR spectra. Using nebular-phase [Fe ii] lines in the optical and NIR, we examine the explosion kinematics and test the efficacy of several common emission-line-fitting techniques. The NIR [Fe ii] 1.644 μm line provides the most robust velocity measurements against variations due to the choice of the fit method and line blending. The resulting effects on velocity measurements due to choosing different fit methods, initial fit parameters, continuum and line profile functions, and fit region boundaries were also investigated. The NIR [Fe ii] velocities yield the same radial shift direction as velocities measured using the optical [Fe ii] λ7155 line, but the sizes of the shifts are consistently and substantially lower, pointing to a potential issue in optical studies. The NIR [Fe ii] 1.644 μm emission profile shows a lack of significant asymmetry in both SNe, and the observed low velocities elevate the importance for correcting for any velocity contribution from the host galaxy’s rotation. The low [Fe ii] velocities measured in the NIR at nebular phases disfavor progenitor scenarios in close double-degenerate systems for both SN 2013aa and SN 2017cbv. The time evolution of the NIR [Fe ii] 1.644 μm line also indicates moderately high progenitor white dwarf central density and potentially high magnetic fields.

A Probability-Constraint-Based Method for Robust Wind Velocity Estimation in Lidar Doppler Spectrograms With Low Signal-to-Noise Ratio

A Probability-Constraint-Based Method for Robust Wind Velocity Estimation in Lidar Doppler Spectrograms With Low Signal-to-Noise Ratio