scholarly journals Velocity estimation error reduction in stenosis areas using a correlation correction method

2016 ◽  
Author(s):  
Luzhen Nie ◽  
Sevan Harput ◽  
David M. J. Cowell ◽  
Steven Freear
Keyword(s):  
Estimation Error ◽  
Correction Method ◽  
Error Reduction ◽  
Velocity Estimation ◽  
Correlation Correction

Truck mass estimation method based on the on-board sensor

2020 ◽  
Vol 234 (10-11) ◽  
pp. 2429-2443
Author(s):  
Beijia Wang ◽  
Hongliang Wang ◽  
Lei Wu ◽  
Liuliu Cai ◽  
Dawei Pi ◽  
...  
Keyword(s):  
Error Correction ◽  
Estimation Error ◽  
Estimation Method ◽  
Estimation Algorithm ◽  
Correction Method ◽  
Mass Estimation ◽  
The Road ◽  
Vehicle Mass ◽  
Error Correction Method ◽  
Road Gradient

Vehicle mass estimation is the key technology to improve vehicle stability. However, the existing mass estimation accuracy is easily affected by the change of road gradient, and there are few studies on the mass estimation method of the light truck. Aiming at this problem, this paper uses sensors to measure road gradient and rear suspension deformation and proposes a sensor-based vehicle mass estimation algorithm. First, factors that affect the mass estimation are analyzed, road gradient error correction method and mass estimation error correction method are established. Besides, the suspension deformation is decoupled from the road gradient. Second, the mass estimation algorithm model was established in Matlab/Simulink platform and compared with the mass estimation iterative algorithm. Finally, the road test was carried out under various conditions, the results show that the proposed mass estimation algorithm is robust, and the accuracy of the mass estimation will not be affected by the sudden change of road gradient.

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

Energies ◽  
2019 ◽  
Vol 12 (7) ◽  
pp. 1242
Author(s):  
Jiangyi Lv ◽  
Hongwen He ◽  
Wei Liu ◽  
Yong Chen ◽  
Fengchun Sun
Keyword(s):  
Autonomous Vehicles ◽  
Estimation Error ◽  
Longitudinal Velocity ◽  
Estimation Method ◽  
Velocity Estimation ◽  
Model Parameters ◽  
Lateral Velocity ◽  
Empirical Correction ◽  
Vehicle Velocity ◽  
Wide Range

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.

scholarly journals Emitter Velocity Estimation Comparison for Frequency Difference of Arrival Measurement Based Single and Multiple Reference Lateration Algorithm

2018 ◽  
Vol 17 (1) ◽  
pp. 9-15
Author(s):  
Abdulmalik Shehu Yaro ◽  
Ahmad Zuri Sha'ameri
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Equilateral Triangle ◽  
Estimation Error ◽  
Velocity Estimation ◽  
Instantaneous Velocity ◽  
Frequency Difference ◽  
Estimation Accuracy ◽  
Estimation Performance ◽  
Multiple Reference

The accuracy at which the instantaneous velocity and position of a non-stationary emitting source estimated using a lateration algorithm depends on several factors such as the lateration algorithm approach, the number and choice of reference receiving station (RS) used in developing the lateration algorithm. In this paper, the use of multiple reference RSs was proposed to improve the velocity estimation accuracy of the frequency difference of arrival (FDOA) based lateration algorithm. The velocity estimation performance of the proposed multiple reference FDOA based lateration algorithm is compared with the conventional approach of using single reference RS at some selected emitter positions using Monte Carlo simulation. Simulation result based on an equilateral triangle RS configuration shows that the use of multiple reference RSs improved the velocity estimation accuracy of the lateration algorithm. Based on the selected emitter positions, a reduction in velocity estimation error of about 0.033  and 1.31  for emitter positions at ranges 0.5 km and 5 km respectively was achieved using the multiple reference lateration algorithm.

Terrain Correlation Correction Method for AUV Seabed Terrain Mapping

2017 ◽  
Vol 70 (5) ◽  
pp. 1062-1078 ◽  
Author(s):  
Ye Li ◽  
Teng Ma ◽  
Rupeng Wang ◽  
Pengyun Chen ◽  
Qiang Zhang
Keyword(s):  
Autonomous Underwater Vehicle ◽  
Likelihood Function ◽  
Correction Method ◽  
Sensor Nodes ◽  
Terrain Mapping ◽  
Correlation Correction ◽  
Front End ◽  
Terrain Elevation ◽  
Elevation Measurement ◽  
Self Consistency

A method is proposed for improving the accuracy and self-consistency of bathymetric maps built using an Autonomous Underwater Vehicle (AUV) to create precise prior maps for Terrain-Aided Navigation (TAN), when the Global Positioning System (GPS) or another precise location method is unavailable. This method consists of front-end and back-end. For the front-end, the AUV predicts the measurement of the bathymetry system through Terrain Elevation Measurement Extrapolation Estimation (TEMEE) and calculates the likelihood function using real measurements. After the final Inertial Navigation System (INS) error is obtained by communicating with sensor nodes, the process enters the back-end. A Terrain Correlation Correction Method (TCCM) and an Improved Terrain Correlation Correction Method (ITCCM) are proposed to solve the gradual distribution of the final INS error to each point on a path, and the accuracy of ITCCM was confirmed experimentally. Finally, a TAN simulation experiment was conducted to prove the importance and necessity of map correction using ITCCM. ITCCM was proven to be an effective and important method for correcting maps built using an AUV.

scholarly journals Efficiency of a Digital Particle Image Velocimetry (DPIV) Method for Monitoring the Surface Velocity of Hyper-Concentrated Flows

Geosciences ◽  
2018 ◽  
Vol 8 (10) ◽  
pp. 383 ◽  
Author(s):  
Donatella Termini ◽  
Alice Di Leonardo
Keyword(s):  
Particle Image Velocimetry ◽  
Digital Image ◽  
Particle Image ◽  
Estimation Error ◽  
Digital Particle Image Velocimetry ◽  
Velocity Estimation ◽  
Surface Velocity ◽  
Image Velocimetry ◽  
Image Technique ◽  
Interrogation Area

Digital particle image velocimetry records high resolution images and allows the identification of the position of points in different time instants. This paper explores the efficiency of the digital image-technique for remote monitoring of surface velocity and discharge measurement in hyper-concentrated flow by the way of laboratory experiment. One of the challenges in the application of the image-technique is the evaluation of the error in estimating surface velocity. The error quantification is complex because it depends on many factors characterizing either the experimental conditions or/and the processing algorithm. In the present work, attention is devoted to the estimation error due either to the acquisition time or to the size of the sub-images (interrogation areas) to be correlated. The analysis is conducted with the aid of data collected in a scale laboratory flume constructed at the Hydraulic laboratory of the Department of Civil, Environmental, Aerospace and of Materials Engineering (DICAM)—University of Palermo (Italy) and the image processing is carried out by the help of the PivLab algorithm in Matlab. The obtained results confirm that the number of frames used in processing procedure strongly affects the values of surface velocity; the estimation error decreases as the number of frames increases. The size of the interrogation area also exerts an important role in the flow velocity estimation. For the examined case, a reduction of the size of the interrogation area of one half compared to its original size has allowed us to obtain low values of the velocity estimation error. Results also demonstrate the ability of the digital image-technique to estimate the discharge at given cross-sections. The values of the discharge estimated by applying the digital image-technique downstream of the inflow sections by using the aforementioned size of the interrogation area compares well with those measured.

scholarly journals SBAS-Aided GPS Positioning with an Extended Ionosphere Map at the Boundaries of WAAS Service Area

2021 ◽  
Vol 13 (1) ◽  
pp. 151
Author(s):  
Mingyu Kim ◽  
Jeongrae Kim
Keyword(s):  
Satellite Orbit ◽  
Correction Method ◽  
Error Reduction ◽  
Service Area ◽  
Ionospheric Delay ◽  
Correlation Property ◽  
Low Latitude ◽  
Positioning Accuracy ◽  
Extrapolation Algorithm ◽  
The Mean

Space-based augmentation system (SBAS) provides correction information for improving the global navigation satellite system (GNSS) positioning accuracy in real-time, which includes satellite orbit/clock and ionospheric delay corrections. At SBAS service area boundaries, the correction is not fully available to GNSS users and only a partial correction is available, mostly satellite orbit/clock information. By using the geospatial correlation property of the ionosphere delay information, the ionosphere correction coverage can be extended by a spatial extrapolation algorithm. This paper proposes extending SBAS ionosphere correction coverage by using a biharmonic spline extrapolation algorithm. The wide area augmentation system (WAAS) ionosphere map is extended and its ionospheric delay error is compared with the GPS Klobuchar model. The mean ionosphere error reduction at low latitude is 52.3%. The positioning accuracy of the extended ionosphere correction method is compared with the accuracy of the conventional SBAS positioning method when only a partial set of SBAS corrections are available. The mean positioning error reduction is 44.8%, and the positioning accuracy improvement is significant at low latitude.

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