Accurate Velocity Estimation Research Articles

Improving Transcranial Acoustic Targeting: The Limits of CT-Based Velocity Estimates and the Role of MR.

Transcranial magnetic resonance-guided focused ultrasound (tcMRgFUS) enables the noninvasive treatment of the deep brain. This capacity relies on the ability to focus acoustic energy through the in-tact skull, a feat that requires accurate estimates of the acoustic velocity in individual patient skulls. In current practice, these estimates are generated using a pretreatment computed tomography (CT) scan and then registered to a magnetic resonance (MR) dataset on the day of the treatment. Treatment safety and efficacy can be improved by eliminating the need to register the CT data to the MR images and by improving the accuracy of acoustic velocity measurements. In this study, we examine the capacity of MR to supplement or replace CT as a means of estimating velocity in the skull. We find that MR can predict velocity with less but comparable accuracy to CT. We then use micro-CT imaging to better understand the limitations of Hounsfield unit (HU)-based estimates of velocity, demonstrating that the macrostructure of pores in the skull contributes to the acoustic velocity of the bone. We find evidence that detailed T2 measurements provide information about pore macrostructure similar to the information obtained with micro-CT, offering a potential clinical mechanism for improving patient-specific estimates of acoustic velocity in the human skull.

An Adaptive and Accurate Method for Rotational Angular Velocity Estimation of Rotor Targets via Fourier Coefficient Interpolation

As a special micro-motion feature of rotor targets, rotational angular velocity can provide a discriminant basis for target classification and recognition. In this paper, an adaptive and accurate method is proposed for estimating the rotational angular velocity of rotor targets via a Fourier coefficient interpolation algorithm that is based on modified frequency index residue initialization. The negative frequency complex exponential signal component is removed at each iteration to eliminate the estimation bias caused by spectrum superposition and improve the estimation accuracy. The frequency index residue initialization is modified, based on a normalized Fourier spectrum, to improve the estimable range of the rotational angular velocity and reduce the computational complexity of the algorithm. The simulation results show that the estimation performance of the proposed method achieves the Cramér–Rao lower bound and outperforms state-of-the-art methods in terms of the estimable range and estimation accuracy of the rotational angular velocity.

Research on multi-target tracking method based on multi-sensor fusion

Purpose Intelligent and connected vehicle technology is in the ascendant. High-level autonomous driving places more stringent requirements on the accuracy and reliability of environmental perception. Existing research works on multitarget tracking based on multisensor fusion mostly focuses on the vehicle perspective, but limited by the principal defects of the vehicle sensor platform, it is difficult to comprehensively and accurately describe the surrounding environment information. Design/methodology/approach In this paper, a multitarget tracking method based on roadside multisensor fusion is proposed, including a multisensor fusion method based on measurement noise adaptive Kalman filtering, a global nearest neighbor data association method based on adaptive tracking gate, and a Track life cycle management method based on M/N logic rules. Findings Compared with fixed-size tracking gates, the adaptive tracking gates proposed in this paper can comprehensively improve the data association performance in the multitarget tracking process. Compared with single sensor measurement, the proposed method improves the position estimation accuracy by 13.5% and the velocity estimation accuracy by 22.2%. Compared with the control method, the proposed method improves the position estimation accuracy by 23.8% and the velocity estimation accuracy by 8.9%. Originality/value A multisensor fusion method with adaptive Kalman filtering of measurement noise is proposed to realize the adaptive adjustment of measurement noise. A global nearest neighbor data association method based on adaptive tracking gate is proposed to realize the adaptive adjustment of the tracking gate.

Improved state estimation of a robot end-effector using laser tracker and inertial sensor fusion

An increased focus on automation in the aerospace industry has led to a demand for flexible manipulators that can achieve high trajectory following accuracy for tasks like machining, joining, and painting. These tasks require an accurate, real-time state estimation of the robot’s end-effector. Currently, the most accurate methods of estimating the end-effector state involve using high-end position and orientation sensors such as laser trackers and camera-based metrology systems and tend to focus on accurately estimating the end-effector position and orientation. This paper investigates the potential benefits and practical considerations in supplementing the data from these kinds of sensors with acceleration and angular velocity measurements obtained from an inertial measurement unit (IMU) to improve the overall robot end-effector state estimation including its velocity and angular acceleration. The sensor fusion is achieved using a Kalman Filter for motions with no rotation and using a Particle Filter for motions with rotation. Simulation results indicate that up to 95% improvement in velocity estimation accuracy, and up to 45% improvement in angular acceleration estimation accuracy can be achieved by fusing IMU data with laser tracker data. In addition, the paper analyzes the thresholds of sensor accuracy beyond which fusion no longer provides significant benefits. Finally, hardware experiments are conducted using a fusion of robot encoder data with IMU measurements. These experiments indicate up to 40% improvement in velocity estimation accuracy from the sensor fusion.

Simulation of flow redistribution in 7 × 7 ballooned fuel bundle experiments using DRACCAR code

IRSN has developed DRACCAR code in order to model loss of coolant accidents (LOCA) in a light water nuclear reactor. The physics during a hypothetical loss of coolant accident involves several phenomena such as heat and mass transfers, fluid dynamics, mechanics, and chemistry. Even if a simulation requires modeling these conjugated processes, it is important to validate them separately first—for example, fluid dynamics calculations of the flow redistribution after the clad ballooning that can occur during a LOCA. In this paper, we present DRACCAR simulations of the fluid dynamics in two 7 × 7 ballooned bundles with different blockage, two ballooned lengths and with two different flow rates, one laminar and the other turbulent. DRACCAR results for the axial velocities are compared to experimental data obtained by MRI techniques on the MASCARA facility. The present results show that, in most of the sub-channels studied, DRACCAR is capable of predicting correctly the velocity profiles for both bundles and flow rates. In general, DRACCAR provided fairly accurate estimates of the axial velocity, especially for the intact and less-blocked sub-channels. For the most blocked subchannel, DRACCAR is able to estimate the majority of the axial velocity in a margin of ±15% with respect to the MASCARA values, except for the most blocked bundle at laminar flow where only 20% of the calculated velocities were found within this margin.

Analysis and Uncertainty Quantification of a Hybrid Laminar Flow Control System

This study aims to quantify suction velocities for hybrid laminar flow control of a vertical fin by taking data and model uncertainties into account. Because suction velocities cannot be measured directly, they are calculated using the Darcy–Forchheimer model, which needs the pressure distribution and porosity characteristics as input. Uncertainties in the pressure distribution are characterized using a novel data fusion method incorporating wind-tunnel and numerical data. Furthermore, geometrical uncertainties of the airfoil that alter the pressure distribution are considered. Two methods are proposed to model the porosity characteristics of the surface: one of which uses a local representation, and the other a global representation. In addition, the modeling error of the Darcy–Forchheimer model is estimated. These input uncertainties are quantified and propagated by Monte Carlo sampling through the Darcy–Forchheimer model. It reveals that local information on the porosity characteristics is essential for accurate suction velocity estimates for this specific application. A sensitivity study shows that the most influential contribution to the variance of suction velocities is the uncertainty in the porosity characteristics, followed by uncertainties in the pressure distribution. The model error and uncertainties in the reference pressure are shown to have a negligible influence.

Influence of the Field of View on Shear Wave Velocity Estimation

Abstract Tissue elasticity contains important information for physicians in diagnosis and treatment, and, e.g., can help in tumor detection because tumors are stiffer than healthy tissue. Ultrasound shear wave elastography imaging (US-SWEI) can be used to estimate tissue stiffness by measuring the velocity of induced shear waves. Commonly, a linear US probe is used to track shear waves at a high imaging frequency in 2D. Realtime US-SWEI is limited by the required time for data processing. Hence, reducing the imaging field of view (FOV) is beneficial as it decreases the size of the acquired data and thereby the acquisition, transfer and processing time. However, a decrease in the FOV has the disadvantage that shear waves are tracked over a smaller distance and thus, also fewer sampling points are available for velocity estimation. This trade-off between a smaller FOV and thus, a smaller data size, and the accuracy of shear wave velocity estimation is investigated in this work. For this purpose, shear waves were tracked with a linear US probe in gelatin phantoms with four different stiffness values. During data processing, we reduced the FOV virtually from 38.1mm to 2.1mm. It was found that a reduction of the FOV to 4.5mm leads to an overestimation of up to five times larger shear wave velocities but still allows to distinguish between phantoms of different stiffness. However, not all estimated velocity values could be clearly assigned to the correct stiffness value. The smallest studied FOV of 2.1mm was not sufficient for distinguishing between the phantoms anymore.

Rethinking Doppler Effect for Accurate Velocity Estimation With Commodity WiFi Devices

Enabling pervasive WiFi devices with non-contact sensing capability is an important topic in the field of integrated sensing and communication. Doppler effect has been widely exploited to estimate targets’ velocity from wireless signals. However, the separation of signal sources and receivers complicates the relationship between Doppler frequency shift (DFS) and target velocity in WiFi-based non-contact sensing systems. In contrast to existing works that rely on either approximated relations or coarse-grained information such as whether a target is moving toward or away from WiFi transceivers, this paper investigates rigorously the dependency of velocity estimation accuracy on target locations and headings in WiFi sensing systems. The theoretical insights allow us to derive a closed-form solution and understand the fundamental limitation of velocity estimation. To optimize velocity estimation performance, we devise a receiving device selection scheme that dynamically chooses the optimal set of receivers among multiple available WiFi devices. A prototype real-time target tracking system has been implemented using commodity WiFi devices. Extensive experimental results show that the proposed system outperforms state-of-the-art approaches in velocity estimation and tracking, and is able to achieve <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$9.38cm/s$ </tex-math></inline-formula> , 13.42°, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$31.08cm$ </tex-math></inline-formula> median errors in speed, heading and location estimation amongst experiments conducted in three indoor environments with three device placements and eight human subjects over 15 trajectories.

Quantitative investigations of a schlieren velocimetry technique by a schlieren motion estimation (SME) method

A schlieren motion estimation (SME) method is a physical-based schlieren image velocimetry involving both schlieren luminance characteristic equation and continuity equation. This seedless velocimetry technique is useful for flow situations where the seeding particles are difficult to supply or identify. In this study, we conducted two quantitative investigations: (1) the sensitivity in an estimation accuracy with the user-defined weight parameters in the SME method and (2) the performances of the SME method for a laminar–turbulent transition flow in which a velocity field would be difficult to be estimated by a standard schlieren image velocimetry technique driven by a block-matching algorithm. The experimental results showed that a weight parameter α between governing equation and a constraint condition is sensitive to a velocity estimation accuracy. Additionally, α is strongly related to an image contrast gradient appearing in a schlieren image, and the strong image contrast gradient leads to the better weight parameter α in a wide range. Regarding the result of another quantitative investigation, it is found that the SME method is applicable to the laminar–turbulent transition flow because the turbulent structures in different sizes can be simultaneously captured by the SME method.

A Doppler enhanced TDCP algorithm based on terrain adaptive and robust Kalman filter using a stand-alone receiver

AbstractTime-differenced carrier phase (TDCP) is a commonly used method of precise velocimetry, but when the receiver is in a dynamic or complex observation environment, the estimation accuracy is reduced. Doppler velocimetry aims at estimating instantaneous velocity, and the accuracy is restricted by the accuracy of measurement. However, in such unfavourable cases, the Doppler measurement is more reliable than the carrier phase measurement. This paper derives the relationship between Doppler observation and TDCP observation, then proposes a Doppler enhanced TDCP algorithm, for the purpose of improving the velocity estimation accuracy in dynamic and complex observation environments. In addition, considering the error caused by the constant speed state update model in the robust Kalman filter (RKF), this paper designs a terrain adaptive and robust Kalman filter (TARKF). After three experimental tests, the improved TDCP algorithm can significantly increase the speed measurement accuracy to sub-metre per second, and the accuracy can be further improved after using TARKF.

Seismic surface wave focal spot imaging: numerical resolution experiments

SUMMARY Numerical experiments of seismic wave propagation in a laterally homogeneous layered medium explore subsurface imaging at subwavelength distances for dense seismic arrays. We choose a time-reversal approach to simulate fundamental mode Rayleigh surface wavefields that are equivalent to the cross-correlation results of three-component ambient seismic field records. We demonstrate that the synthesized 2-D spatial autocorrelation fields in the time domain support local or so-called focal spot imaging. Systematic tests involving clean isotropic surface wavefields but also interfering body wave components and anisotropic incidence assess the accuracy of the phase velocity and dispersion estimates obtained from focal spot properties. The results suggest that data collected within half a wavelength around the origin is usually sufficient to constrain the used Bessel functions models. Generally, the cleaner the surface wavefield the smaller the fitting distances that can be used to accurately estimate the local Rayleigh wave speed. Using models based on isotropic surface wave propagation we find that phase velocity estimates from vertical–radial component data are less biased by P-wave energy compared to estimates obtained from vertical–vertical component data, that even strong anisotropic surface wave incidence yields phase velocity estimates with an accuracy of 1 per cent or better, and that dispersion can be studied in the presence of noise. Estimates using a model to resolve potential medium anisotropy are significantly biased by anisotropic surface wave incidence. The overall accurate results obtained from near-field measurements using isotropic medium assumptions imply that dense array seismic Rayleigh wave focal spot imaging can increase the depth sensitivity compared to ambient noise surface wave tomography. The analogy to elastography focal spot medical imaging implies that a high station density and clean surface wavefields support subwavelength resolution of lateral medium variations.

Meteoroid orbit determination from HPLA radar data

High-power large-aperture radars have revolutionized meteor science by allowing highly accurate position and velocity estimates to be made from meteor head echoes. This paper describes a new open-source software, MODA, for determining the heliocentric orbital parameters of these meteoroids. We compare MODA with other current methods, both analytical and numerical. We describe our modeling of third-body perturbations and atmospheric drag, as well as solar radiation pressure, which is not taken into account in other works. We verify MODA against results from the literature and use it to compute the orbits for two small particles observed by ALTAIR in 2008.

Improve the accuracy of vehicle velocity estimation based on low-cost GPS/IMU by GPS course angle

Vehicle velocity is an important parameter for vehicle dynamics control. Global Positioning System (GPS) and inertial measurement unit (IMU) information can be used for velocity estimation by taking advantage of each other through fusion. However, if GPS velocity is used as the only measurement like classic GPS/INS, it will cause a large velocity estimation error or even divergence. This paper, from the perspective of system observability, proves that the GPS course angle (GCA) plays an important role in vehicle velocity estimation based on low-cost GPS/IMU. Firstly, construct the velocity estimation algorithm in the vehicle coordinate system directly. Then, according to the characteristics of different working conditions, the observability of the system without GCA is fully analyzed from the Lie derivative-based observability proof and the observability basic definition. The results show that the weak observability of the yaw angle is the most serious threat in vehicle velocity estimation. After fusing the GCA, the yaw angle potential divergence can be suppressed and the estimation accuracy of velocity will be improved. Even though roll and pitch are still unobservable under some conditions after augmenting GCA, the error propagation analysis shows that their estimation errors have little effect on velocity estimation, so the accurate estimation of velocity can be guaranteed. Finally, the indispensable of GCA in velocity estimation based on low-cost GPS/IMU is verified by Carsim-Simulink joint simulations and vehicle tests.

Multi-Layer Fault-Tolerant Robust Filter for Integrated Navigation in Launch Inertial Coordinate System

As to an aerospace vehicle, the flight span is large and the flight environment is complex. More than that, the existing navigation algorithms cannot meet the needs to provide accurate navigation parameters for aerospace vehicles, which results in the decline of navigation accuracy. This paper proposes a multi-layer, fault-tolerant robust filtering algorithm of aerospace vehicle in the launch inertial coordinate system to address this problem. Firstly, the launch inertial coordinate system is used as the reference coordinate system for navigation calculation, and the state equation and measurement equation of the navigation system are established in this coordinate system to improve the modeling accuracy of the navigation system. On this basis, a multi-layer, fault-tolerant robust filtering algorithm is designed to estimate and compensate the unknown input in the state equation in real time and adjust the noise variance matrix in the measurement equation adaptively. Simulation results show that the errors about the integrated navigation system output parameters are reduced, through this algorithm, which improves the attitude, velocity and position estimation accuracy of the integrated navigation system. In addition, the algorithm enhances the fault tolerance and robustness of the filtering algorithm.

Real-Time Event-Based Unsupervised Feature Consolidation and Tracking for Space Situational Awareness.

Earth orbit is a limited natural resource that hosts a vast range of vital space-based systems that support the international community's national, commercial and defence interests. This resource is rapidly becoming depleted with over-crowding in high demand orbital slots and a growing presence of space debris. We propose the Fast Iterative Extraction of Salient targets for Tracking Asynchronously (FIESTA) algorithm as a robust, real-time and reactive approach to optical Space Situational Awareness (SSA) using Event-Based Cameras (EBCs) to detect, localize, and track Resident Space Objects (RSOs) accurately and timely. We address the challenges of the asynchronous nature and high temporal resolution output of the EBC accurately, unsupervised and with few tune-able parameters using concepts established in the neuromorphic and conventional tracking literature. We show this algorithm is capable of highly accurate in-frame RSO velocity estimation and average sub-pixel localization in a simulated test environment to distinguish the capabilities of the EBC and optical setup from the proposed tracking system. This work is a fundamental step toward accurate end-to-end real-time optical event-based SSA, and developing the foundation for robust closed-form tracking evaluated using standardized tracking metrics.

Airspeed-Aided State Estimation Algorithm of Small Fixed-Wing UAVs in GNSS-Denied Environments.

Aimed at improving the navigation accuracy of the fixed-wing UAVs in GNSS-denied environments, this paper proposes an algorithm of nongravitational acceleration estimation based on airspeed and IMU sensors, which use a differential tracker (TD) model to further supplement the effect of linear acceleration for UAVs under dynamic flight. We further establish the mapping relationship between vehicle nongravitational acceleration and the vehicle attitude misalignment angle and transform it into the attitude angle rate deviation through the nonlinear complementary filtering model for real-time compensation. It can improve attitude estimation precision significantly for vehicles in dynamic conditions. Furthermore, a lightweight complementary filter is used to improve the accuracy of vehicle velocity estimation based on airspeed, and a barometer is fused on the height channel to achieve the accurate tracking of height and the lift rate. The algorithm is actually deployed on low-cost fixed-wing UAVs and is compared with ACF, EKF, and NCF by using real flight data. The position error within 30 s (about 600 m flying) in the horizontal channel flight is less than 30 m, the error within 90 s (about 1800 m flying) is less than 50 m, and the average error of the height channel is 0.5 m. The simulation and experimental tests show that this algorithm can provide UAVs with good attitude, speed, and position calculation accuracy under UAV maneuvering environments.

Coordinated control of path tracking and energy optimization for in-wheel motor drive electric buses with velocity estimation

Accurate velocity estimation plays a significant role in improving path tracking performance. Meanwhile, for in-wheel motor drive electric buses (IWMEBs), how to tradeoff the energy optimization and torque distribution in path tracking is also a challenging task. To address the above issues, a hierarchical control framework is proposed in this paper for IWMEBs based on nonlinear model predictive control (NMPC), under which a modular nonlinear observer is designed to achieve accurate velocity estimate. The framework can be divided into two layers. In the upper layer, a modular nonlinear observer is designed to estimate the longitudinal and lateral velocities, considering the dependence of path tracking control on vehicle states. By replacing the tire model with the estimated longitudinal and lateral forces, the errors caused by the inaccurate parameters and modeling can be reduced. In the lower layer, a coordinated control method is proposed to realize the accurate path tracking while minimizing the energy consumption based on NMPC. By integrating tracking error, energy consumption and dynamic demand of IWMEB into an objective function, the coordinated control of path tracking and energy optimization is accomplished. The comparison simulation is implemented via Trucksim-Simulink joint simulation, whose results demonstrate that the proposed hierarchical control framework is effective and its performance is satisfactory.

Characterizing and correcting phase biases in short-term, multilooked interferograms

Interferometric Synthetic Aperture Radar (InSAR) is widely used to measure deformation of the Earth's surface over large areas and long time periods. A common strategy to overcome coherence loss in long-term interferograms is to use multiple multilooked shorter interferograms, which can cover the same time period but maintain coherence. However, it has recently been shown that using this strategy can introduce a bias (also referred to as a “fading signal”) in the interferometric phase. We isolate the signature of the phase bias by constructing “daisy chain” sums of short-term interferograms of different length covering identical 1-year time intervals. This shows that the shorter interferograms are more affected by this phenomenon and the degree of the effect depends on ground cover types; cropland and forested pixels have significantly larger bias than urban pixels and the bias for cropland mimics subsidence throughout the year, whereas forests mimics subsidence in the spring and heave in the autumn. We, propose a method for correcting the phase bias, based on the assumption, borne out by our observations, that the bias in an interferogram is linearly related to the sum of the bias in shorter interferograms spanning the same time. We tested the algorithm over a study area in western Turkey by comparing average velocities against results from a phase linking approach, which estimates the single primary phases from all the interferometric pairs, and has been shown to be almost insensitive to the phase bias. Our corrected velocities agree well with those from a phase linking approach. Our approach can be applied to global compilations of short-term interferograms and provides accurate long-term velocity estimation without a requirement for coherence in long-term interferograms.

A SLAM System with Direct Velocity Estimation for Mechanical and Solid-State LiDARs

Simultaneous localization and mapping (SLAM) is essential for intelligent robots operating in unknown environments. However, existing algorithms are typically developed for specific types of solid-state LiDARs, leading to weak feature representation abilities for new sensors. Moreover, LiDAR-based SLAM methods are limited by distortions caused by LiDAR ego motion. To address the above issues, this paper presents a versatile and velocity-aware LiDAR-based odometry and mapping (VLOM) system. A spherical projection-based feature extraction module is utilized to process the raw point cloud generated by various LiDARs, hence avoiding the time-consuming adaptation of various irregular scan patterns. The extracted features are grouped into higher-level clusters to filter out smaller objects and reduce false matching during feature association. Furthermore, bundle adjustment is adopted to jointly estimate the poses and velocities for multiple scans, effectively improving the velocity estimation accuracy and compensating for point cloud distortions. Experiments on publicly available datasets demonstrate the superiority of VLOM over other state-of-the-art LiDAR-based SLAM systems in terms of accuracy and robustness. Additionally, the satisfactory performance of VLOM on RS-LiDAR-M1, a newly released solid-state LiDAR, shows its applicability to a wide range of LiDARs.

LSTM을 이용한 항법시스템의 확장칼만필터 공분산 튜닝

Noise covariances in the EKF (Extended Kalman Filter) act as tuning parameters due to modeling errors, such as the error generated in the process of linearizing the nonlinear system equation. Filter designers traditionally prefer the trial-and-error method. However, because the noise covariances require retuning in concert with changes in trajectories or changes in system equations, human tuning is very time consuming. Accordingly, this study proposes an automation method for real-time parameter tuning using an LSTM (Long Short-Term Memory) neural network. The proposed method consists of two stages: 1) generating residual and state error covariances from an EKF using fixed-noise covariances and 2) producing a state estimate from an EKF using tuned time-varying noise covariances obtained from an LSTM neural network. This scheme is applied to navigation systems to calculate accurate velocity, attitude, and IMU bias estimates under challenging maneuvering situations. Using simulation, we showed that the proposed method outperforms the conventional EKF of navigation systems in suppressing estimation error in different trajectory scenarios.