Accurate Velocity Estimation Research Articles

Adaptive Virtual Waveform Design for Millimeter-Wave Joint Communication–Radar

Joint communication and radar (JCR) waveforms with fully digital baseband generation and processing can now be realized at the millimeter-wave (mmWave) band. Prior work has proposed a mmWave wireless local area network (WLAN)-based JCR that exploits the WLAN preamble for radars. The performance of target velocity estimation, however, was limited. In this paper, we propose a virtual waveform design for an adaptive mmWave JCR. The proposed system transmits a few non-uniformly placed preambles to construct several receive virtual preambles for enhancing velocity estimation accuracy, at the cost of only a small reduction in the communication data rate. We evaluate JCR performance trade-offs using the Cramer-Rao Bound (CRB) metric for radar estimation and a novel distortion minimum mean square error (MMSE) metric for data communication. Additionally, we develop three different MMSE-based optimization problems for the adaptive JCR waveform design. Simulations show that an optimal virtual (non-uniform) waveform achieves a significant performance improvement as compared to a uniform waveform. For a radar CRB constrained optimization, the optimal radar range of operation and the optimal communication distortion MMSE (DMMSE) are improved. For a communication DMMSE constrained optimization with a high DMMSE constraint, the optimal radar CRB is enhanced. For a weighted MMSE average optimization, the advantage of the virtual waveform over the uniform waveform is increased with decreased communication weighting. Comparison of MMSE-based optimization with traditional virtual preamble count-based optimization indicated that the conventional solution converges to the MMSE-based one only for a small number of targets and a high signal-to-noise ratio.

Experimental Comparison of Visual-Aided Odometry Methods for Rail Vehicles

Today, rail vehicle localization is based on infrastructure-side Balises (beacons) together with on-board odometry to determine whether a rail segment is occupied. Such a coarse locking leads to a sub-optimal usage of the rail networks. New railway standards propose the use of moving blocks centered around the rail vehicles to increase the capacity of the network. However, this approach requires accurate and robust position and velocity estimation of all vehicles. In this work, we investigate the applicability, challenges and limitations of current visual and visual-inertial motion estimation frameworks for rail applications. An evaluation against RTK-GPS ground truth is performed on multiple datasets recorded in industrial, sub-urban, and forest environments. Our results show that stereo visual-inertial odometry has a great potential to provide a precise motion estimation because of its complementing sensor modalities and shows superior performance in challenging situations compared to other frameworks.

Vehicle Velocity Estimation Fusion with Kinematic Integral and Empirical Correction on Multi-Timescales

Accurate and reliable vehicle velocity estimation is greatly motivated by the increasing demands of high-precision motion control for autonomous vehicles and the decreasing cost of the required multi-axis IMU sensors. A practical estimation method for the longitudinal and lateral velocities of electric vehicles is proposed. Two reliable driving empirical judgements about the velocities are extracted from the signals of the ordinary onboard vehicle sensors, which correct the integral errors of the corresponding kinematic equations on a long timescale. Meanwhile, the additive biases of the measured accelerations are estimated recursively by comparing the integral of the measured accelerations with the difference of the estimated velocities between the adjacent strong empirical correction instants, which further compensates the kinematic integral error on short timescale. The algorithm is verified by both the CarSim-Simulink co-simulation and the controller-in-the-loop test under the CarMaker-RoadBox environment. The results show that the velocities can be accurately and reliably estimated under a wide range of driving conditions without prior knowledge of the tire-model and other unavailable signals or frequently changeable model parameters. The relative estimation error of the longitudinal velocity and the absolute estimation error of the lateral velocity are kept within 2% and 0.5 km/h, respectively.

MVU Estimate of User Velocity via Gamma Distributed Handover Count in HetNets

In this letter, we propose a handover-count based minimum-variance-unbiased (MVU) estimate of user velocity in heterogeneous-networks (HetNets). Since mobility management is considered as critical challenge in success of HetNets, it is imperative to have accurate estimation of the user velocity. In this context, we consider handover-counts as a measure of velocity and approximate its probability-mass-function by Gamma distribution. Next, we exploit Neyman–Fisher-factorization method to extract the sufficient statistics and then following Rao–Blackwell–Lehmann–Scheffe theorem, we finally derive the MVU estimator. The performance of proposed estimator is verified via numerical analysis in which we observe close match of variance with its corresponding Cramer–Rao-lower-bound.

Moving Target Tracking Using TDOA and FDOA Measurements from Two UAVs with Varying Baseline

This paper considers the problem of tracking a moving target using the time difference of arrival (TDOA) and frequency difference of arrival (FDOA) measurements obtained at two unmanned aerial vehicles (UAVs) with varying baseline. Accumulation time is necessary because the emitter position cannot be estimated at each emission when there are only two UAVs as sensors. And the target position and velocity estimation accuracy often suffer from low convergence in conventional formation flight mode no matter what nonlinear filtering algorithm is used. Based on the analysis of the influence of different flight modes on the positioning performance, a moving target tracking system from two UAVs with varying baseline is proposed. The performance of unscented Kalman filter (UKF) under proposed system is analysed and compared with the Cramer-Rao lower bound (CRLB). Simulation results show that the proposed system can speed up the convergence.

Observer-based estimation of velocity and tire-road friction coefficient for vehicle control systems

A novel nonlinear observer for the estimation of vehicle velocity together with the tire-road friction coefficient is presented in this paper. The modular observer is designed based on a longitudinal tire force estimation approach and a lateral tire friction model. Compared to the state-of-art methods, the proposed observer design provides accurate estimation of the longitudinal velocity, lateral velocity, and the tire-road friction coefficient simultaneously. Particularly, the longitudinal tire forces are first estimated based on a filter observer. Then, according to the calculation of lateral tire forces, the nonlinear observer is proposed to estimate vehicle velocity and the tire-road friction coefficient. Moreover, the stability property of the observer is analyzed using a Lyapunov-based method. Simulation results validate the effectiveness of the proposed method.

Micro-scale wind resource assessment in complex terrain based on CFD coupled measurement from multiple masts

To date, the micro-scale wind resource assessment for complex terrain has always been a challenging mission, due to the flow field complexity caused by the local topography. In this paper, a novel method, combining on-site measurement from multiple masts and computational fluid dynamics (CFD) simulations, is proposed for complex terrain site assessment. It is designed to accomplish the spatial variability reproduction of wind energy distribution, as well as the dynamic wind velocity estimation of any desired positions within the concerned region. CFD simulations are carried out to provide detailed wind fields, which implicitly carry the correlations of physical properties with the concerned space. The on-site measurement data from multiple masts are integrated into the assessment process, to provide dynamical corrections based on the reference solutions obtained from the CFD simulations. A high-resolutional wind resource distribution and accurate wind velocity estimations are achieved. A detailed case study on micro-scale wind resource assessment for a wind farm with complex terrain, located in China is presented for validation.

Integrated GNSS/IMU hub motion estimator for offshore wind turbine blade installation

Abstract Offshore wind turbines (OWTs) have become increasingly popular for their ability to harvest clean offshore wind energy. Bottom-fixed foundations are the most used foundation type. Because of its large diameter, the foundation is sensitive to wave loads. For typical manually assisted blade-mating operations, the decision to perform the mating operation is based on the relative distance and velocity between the blade root center and the hub, and in accordance with the weather window. Hence, monitoring the hub real-time position and velocity is necessary, whether the blade installation is conducted manually or automatically. In this study, we design a hub motion estimation algorithm for the OWT with a bottom-fixed foundation using sensor fusion of a global navigation satellite system (GNSS) and an inertial measurement unit (IMU). Two schemes are proposed based on a moving horizon estimator, a multirate Kalman filter, an online smoother, and a predictor. The moving horizon estimator mitigates the slow GNSS sampling rate relative to the hub dynamics. The multirate Kalman filter estimates the position, velocity, and accelerometer bias with a constant GNSS measurement delay. The online smoothing algorithm filters the delayed estimated trajectory to remove sudden step changes. The predictor compensates the delayed estimate, resulting in real-time monitoring. HAWC2 and MATLAB are used to verify the performance of the estimation algorithms, showing that a sufficiently accurate real-time position and velocity estimate with a high sampling rate is achieved. A sensitivity study compares the accuracy of different algorithms applied in various conditions. By combining both proposed algorithms, a sufficiently accurate estimation can be achieved for a wider scope of practical applications.

Parameters estimation and imaging of moving target for frequency-modulated continuous-wave synthetic-aperture radar system

According to frequency-modulated continuous-wave synthetic-aperture radar, we establish the signal model of a moving target and propose an algorithm for target parameters estimation and imaging. The key procedures of moving target imaging include velocities estimation and range migration correction. Compared with conventional methods, the effect introduced from the along-track velocity is taken into account during the range migration correction to improve the correction accuracy. Moreover, fractional Fourier transform is adopted to estimate the Doppler parameters. To summarize, the proposed method has three advantages. First, by carrying out rough and precise estimation separately, both accurate velocity estimation and better robustness are obtained. Second, this method is available for moving targets with Doppler ambiguity. Third, the third-order phase error derived from moving target is taken into account to improve the azimuth compression result. Finally, a complete flowchart of the proposed method is presented and is verified by both simulation and real data.

Multiple Model Tracking for Hypersonic Gliding Vehicles With Aerodynamic Modeling and Analysis

The multiple model tracking methods for hypersonic gliding vehicles (HGVs), which improves the design of tracking model-set is proposed in this paper. Different from the traditional tracking method with dynamic pressure models, the proposed method refines the model-set by modeling and analyzing the specific aerodynamic characteristics of the HGVs. Based on the aerodynamic model of the vehicle, the novel tracking models are established and the multiple model tracking methods are adopted. The uniform model-sets are designed by determining the representative points from the constrained characteristic parameters to ensure the stability of the tracking system. Moreover, the model-sets are then augmented by the real-time updates of the motion estimations to further improve the tracking accuracy. The simulation studies indicate that the proposed tracking method outperforms the method with dynamic pressure models in both position and velocity estimations, the position estimation accuracy is increased by about 23%-53% and the velocity estimation accuracy is increased by about 42%-56% in the given examples.

Constant false alarm rate detection of slow targets in polarimetric along‐track interferometric synthetic aperture radar imagery

To improve the detection performance for ground moving targets with small radar-cross-section and low velocity in Along-Track Interferometric Synthetic Aperture Radar (AT-INSAR) system, this study explores new methods to elevate the target-to-clutter ratio by using full Polarimetric AT-INSAR (AT-POLINSAR). Three novel detectors that can achieve Constant False Alarm Rate (CFAR) detection are put forward for AT-POLINSAR at first, namely, the Suboptimal Interferometric Coherence Detector, the Generalized Polarimetric Whitening Filter and the Dual Detector. Secondly, the analytical expressions of their false-alarm rates are derived under the Gaussian distribution assumption of the six-dimensional polarimetric interferometric vector, then the estimation methods of distribution parameters and the calculating methods of detection thresholds as well as the specific implementation schemes of CFAR detection are given. Finally, the proposed detectors are compared with single-polarimetric AT-INSAR detectors and full polarimetric log-likelihood-ratio test through both the Monte Carlo detection experiments and the scene detection experiments of simulated AT-POLINSAR imagery, where the results indicate that new methods have rather higher CFAR detection performance and velocity estimation accuracy than current single-polarimetric detectors.

Doppler chirplet transform for the velocity estimation of a fast moving acoustic source of discrete tones.

An extended chirplet transform method termed as Doppler chirplet transform is proposed to estimate the velocity of a discrete tone source in uniform linear motion. This method directly uses the relation of the observed instantaneous frequency to the source velocity as the kernel of the chirplet transform. It is tested on a set of 30-s truck noise recordings and also on simulated data from a statistical perspective. The results show that the Doppler chirplet transform significantly reduces the run time that the polynomial chirplet transform [Xu, Yang, and Yu, J. Acoust. Soc. Am. 137(4), EL320-EL326 (2015)] costs to produce similarly accurate estimates of the source velocity.

Sparsity-promoting method to estimate the dispersion curve of surface-wave group velocity

Group velocity is an important characteristic of surface wave that is defined as the velocity of an envelope of frequencies. Although many studies have shown the promises of analyzing the group velocity to obtain subsurface S-wave velocity, the estimation of the group velocity is not straightforward due to the uncertainties of selecting an optimum envelope of frequencies. Conventional transformations or filtering algorithms used to define an optimum envelope usually give reasonable results just for a narrow frequency or velocity range. We introduced a new approach for the estimation of the group velocity using the sparse S transform (SST) and sparse linear Radon transform (SLRT). In SST, the width of the Gaussian window is optimally calculated by energy concentration to eliminate energy smearing in the time-frequency (TF) domain, and then the sparsity is applied to enhance the TF resolution. Compared with conventional methods for the estimation of the group velocity based on the generalized S transforms, SST does not require any adjustment to the Gaussian window and yields accurate estimates of the group velocity. We apply SST to each seismic trace of a seismic shot record to obtain a 3D cube of frequency, time, and offset. For any frequency, we obtain a common frequency gather of time and offset to which we apply SLRT to obtain the group velocity of the surface wave. Our approach is robust at calculating high-resolution distinguishable dispersion curves of the group velocity in particular when data are extremely sparse.

Optical Tracking Velocimetry (OTV): Leveraging Optical Flow and Trajectory-Based Filtering for Surface Streamflow Observations

Nonintrusive image-based methods have the potential to advance hydrological streamflow observations by providing spatially distributed data at high temporal resolution. Due to their simplicity, correlation-based approaches have until recent been preferred to alternative image-based approaches, such as optical flow, for camera-based surface flow velocity estimate. In this work, we introduce a novel optical flow scheme, optical tracking velocimetry (OTV), that entails automated feature detection, tracking through the differential sparse Lucas-Kanade algorithm, and then a posteriori filtering to retain only realistic trajectories that pertain to the transit of actual objects in the field of view. The method requires minimal input on the flow direction and camera orientation. Tested on two image data sets collected in diverse natural conditions, the approach proved suitable for rapid and accurate surface flow velocity estimations. Five different feature detectors were compared and the features from accelerated segment test (FAST) resulted in the best balance between the number of features identified and successfully tracked as well as computational efficiency. OTV was relatively insensitive to reduced image resolution but was impacted by acquisition frequencies lower than 7–8 Hz. Compared to traditional correlation-based techniques, OTV was less affected by noise and surface seeding. In addition, the scheme is foreseen to be applicable to real-time gauge-cam implementations.

Efficient and accurate estimation of water surface velocity in STIV

In shallow flow conditions, turbulence effects appear on a water surface as a form of irregularity of surface shape composed of a large number of fluctuating ripples. The intensity of such a fluctuation increases with the Froude number and also with the Reynolds number as can be observed in flooding river flow. In such a flow condition, surface irregularities are viewed as surface features or textures moving with the flow. Although there has been a discussion in terms of the traceability of surface features, the advection speed of surface features agrees well with the surface velocity from a practical point of view. Based on the assumption about the traceability of surface features, image-based techniques have been developed in the past decades. The space–time image velocimetry (STIV) is one of those techniques developed by Fujita et al. (Int J River Basin Man 5(2):105–114, 2007), with success of measuring river surface velocity distributions without seeding the flow. However, there is still some room for improvement in determining accurate surface velocity from a space–time image (STI) used in STIV. For that purpose, a novel technique was developed that utilizes the two dimensional auto-correlation function of the image intensity in an STI together with quality indices of STI. The performance of the new technique was verified using synthetic images as well as its application to the measurement of snowmelt flood.

Non-destructive characterization of surfaces and thin coatings using a large-bandwidth interdigital transducer.

This paper deals with non-destructive testing of thin layer structures using Rayleigh-type waves over a broad frequency range (25-125 MHz). The dispersion phenomenon was used to characterize a layer-on-substrate-type sample comprising a thin layer of platinum 100 nm thick on a silicon substrate. The originality of this paper lies in the investigation of different ways of generating surface acoustic waves (SAWs) with large bandwidth interdigital transducers (IDTs) as well as the development of a measuring device to accurately estimate the SAW phase velocity. In particular, this study focuses on comparing the performance (in terms of SAW amplitude and bandwidth) of different excitations imposed on IDTs. The three types of excitations are burst, impulse, and chirp. The interest of chirp excitation compared to the other two types was clearly demonstrated in terms of the SAW bandwidth and amplitude of displacement. With these IDT transducers, measurements could be performed over a wide frequency band (20-125 MHz), and consequently, dispersion curves could be obtained over a wide frequency band with a range of velocity variations in the order of 100 m/s. Under these conditions, an extremely accurate estimate of the phase velocity as a function of the frequency could be obtained using a Slant Stack transformation. Finally, from these experimental dispersion curves and theoretical dispersion curves, an accurate estimate of the thickness of the layer could be obtained by inversion. This estimated thickness was then confirmed using profilometer measurements.

Rapid time-stamped analysis of filament motility.

The in vitro motility assay is a valuable tool to understand motor protein mechanics, but existing algorithms are not optimized for accurate time resolution. We propose an algorithm that combines trace detection with a time-stamped analysis. By tracking filament ends, we minimize data loss from overlapping and crossing filaments. A movement trace formed by each filament end is created by time-stamping when the filament either first (filament tip) or last (filament tail) occupies a pixel. A frame number vs. distance curve is generated from this trace, which is segmented into regions by slope to detect stop-and-go movement. We show, using generated mock motility videos, accurate detection of velocity and motile fraction changes for velocities < 0.05 pixels per frame, without manual trace dropping and regardless of filament crossings. Compared with established algorithms we show greatly improved accuracy in velocity and motile fraction estimation, with greatly reduced user effort. We tested two actual motility experiments: (1) adenosine triphosphate (ATP) added to skeletal myosin in rigor; (2) myosin light chain phosphatase (MLCP) added to phasic smooth muscle myosin. Our algorithm revealed previously undetectable features: (1) rapid increase in motile fraction paralleled by a slow increase in velocity as ATP concentration increases; (2) simultaneous reductions in velocity and motile fraction as MLCP diffuses into the motility chamber at very low velocities. Our algorithm surpasses existing algorithms in the resolution of time dependent changes in motile fraction and velocity at a wide range of filament lengths and velocities, with minimal user input and CPU time.

Analytical model test of methods to find the geometry and velocity of magnetic structures

It is important to determine the dimensionality and velocity information in the study of spatial magnetic structures. Many data analysis theories/techniques are based on the assumption of one or two dimensions. For example, the Grad-Shafranov (GS) reconstruction method assumes a dimensionality of two or less. The Minimum Direction Derivative (MDD) method provides an indication of the dimensionality. For the structure velocity, the components in each dimensionality can be calculated by Spatio-Temporal Difference analysis (STD). In order to improve the convenience of use of MDD method, a new parameter D m quantifying the dimensionality based on MDD eigenvalues is introduced in this paper. The influences of noise /turbulence, separation distance and tetrahedron configuration on MDD and the evaluation of D m are systematically tested using two analytical models for magnetic structures, representing a magnetic mirror and magnetic flux rope. We tested and gave the threshold values of three quality indicators for MDD results using the flux rope model. We also show that the error induced by turbulence is comparable to that of random noise when the turbulence scales are less than the spacecraft separation. Besides, the accuracy of STD velocity estimation will also be influenced by turbulence for cases with excessively high data time resolution. By using D m, we show that an ideal model of a mirror-like structure can be divided into one dimension (1-D) and three dimension (3-D) regions. This restricts the applicability of the GS method in mirror-like structures. For example, in a given reconstruction range, the GS error increased from less than 7% to more than 15% by using the data along trajectories in 1-D and 3-D regions as predicated by D m. Thus, it is important to estimate the structure dimensionality, which can be further used to estimate the reliability of the GS reconstruction map.

A high-accuracy autonomous navigation scheme for the Mars rover

High-accuracy, autonomous and reliable navigation systems are important foundations for Mars rovers to achieve exploration missions successfully. Based on the motion characteristics and working environment of the rover, the paper shows a high-accuracy strapdown inertial navigation system/visual navigation system/celestial navigation system (SINS/VNS/CNS) integrated navigation scheme suitable for the rover with long-time and long-distance motion. According to the feature point positions in the camera frame at adjacent time obtained by the binocular visual odometry, its velocity in the camera frame and the attitude are calculated, then a subsystem model of SINS/VNS integrated navigation is established. In addition, using the star vectors measured by the large field-of-view star sensor, a high-accuracy attitude matrix of the rover in the inertial frame can be obtained, and a SINS/CNS subsystem model is established. Furthermore, in order to make full use of the complementary advantages of the two subsystems in attitude and position estimation, an interacting multiple model filter is developed. By updating the model probabilities of the subsystems separately in real time, the filter can output accurate navigation information of the rover. Simulation results show that the proposed navigation scheme can significantly improve the estimation accuracy of attitude, position and velocity simultaneously.

The accurate estimation of GPR migration velocity and comparison of imaging methods

In order to improve the resolution of GPR profiles from the perspective of data processing, the migration technique is applied to forward and measured GPR profiles. Given the shortcomings of the conventional GPR migration velocity determination method, such as strong randomness, uncontrollable error and massive consumption of workforce and material resources, therefore, a new method for determining GPR migration velocity based on cross-correlation analysis and least square fitting has been proposed. According to the determined velocity, three migration algorithms (Kirchhoff, FK and finite difference) can be used to process the forward and measured GPR profiles (pipes detection), the migration effect evaluated from image display, the information entropy, the operational efficiency and the accuracy of the determined velocity also reflected from migration results. At the same time, the radius inversion is realized under the coordinate points picked up by the cross-correlation analysis and fitted equations. In addition, a series of studies show that the migration velocity determined by the method proposed in this paper can be used not only to migrate GPR profiles, but to converge the hyperbola in profiles completely, which indicates the velocity is relatively accurate. By analyzing the effect of the GPR profiles migrated by three algorithms, it can be found that the FK algorithm has a strong applicability to the forward and measured profiles of GPR and that the radius inversion errors of forward and measured profiles can be controlled below 6% and 12% respectively.