scholarly journals Low-Cost Monitoring System of Sensors for Evaluating Dynamic Solicitations of Semitrailer Structure

2016 ◽  
Vol 2016 ◽  
pp. 1-15 ◽  
Author(s):  
Pablo Luque ◽  
Daniel A. Mántaras ◽  
Aida Rodríguez ◽  
Hugo Malón ◽  
Luis Castejón ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Monitoring System ◽  
Extended Kalman Filter ◽  
Numerical Models ◽  
Low Cost ◽  
Three Dimensional ◽  
Cost Benefit ◽  
Simulation Software ◽  
Measurement Unit ◽  
Definition Of

Analysis of the fatigue life of a semitrailer structure necessitates identification of the loads and dynamic solicitations in the structure. These forces can be introduced in computer simulation software (multibody + finite element) for analysing the response of different design solutions to them. These numerical models must be validated and some parameters need to be measured directly in a field test with real vehicles under various driving conditions. In this study, a low-cost monitoring system is developed for application to a real fleet of semitrailers. According to the definition of the numerical model, the guidance of a virtual vehicle is defined by the three-dimensional kinematics of the kingpin. For characterisation of these movements, a monitoring system having a low-cost inertial measurement unit (IMU) and global positioning system (GPS) antennas is developed with different configurations to enable analysis of the best cost-benefit (result accuracy) solution, and an extended Kalman filter (EKF) that characterises the kinematic guidance of the kingpin is proposed. A semitrailer was equipped with the experimental low-cost monitoring system and high-precision sensors (IMU, GPS) in order to validate the results obtained by the experimental low-cost monitoring system and the inertial-extended Kalman filter developed. The validated system has applicability in the low-cost monitoring of a fleet of real vehicles.

scholarly journals Performance Enhancement of Pedestrian Navigation Systems Based on Low-Cost Foot-Mounted MEMS-IMU/Ultrasonic Sensor

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 364 ◽  
Author(s):  
Ming Xia ◽  
Chundi Xiu ◽  
Dongkai Yang ◽  
Li Wang
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Navigation System ◽  
Low Cost ◽  
Ultrasonic Sensor ◽  
Measurement Unit ◽  
Positioning Error ◽  
Process Noise ◽  
Pedestrian Navigation ◽  
Mems Imu

The pedestrian navigation system (PNS) based on inertial navigation system-extended Kalman filter-zero velocity update (INS-EKF-ZUPT or IEZ) is widely used in complex environments without external infrastructure owing to its characteristics of autonomy and continuity. IEZ, however, suffers from performance degradation caused by the dynamic change of process noise statistics and heading estimation errors. The main goal of this study is to effectively improve the accuracy and robustness of pedestrian localization based on the integration of the low-cost foot-mounted microelectromechanical system inertial measurement unit (MEMS-IMU) and ultrasonic sensor. The proposed solution has two main components: (1) the fuzzy inference system (FIS) is exploited to generate the adaptive factor for extended Kalman filter (EKF) after addressing the mismatch between statistical sample covariance of innovation and the theoretical one, and the fuzzy adaptive EKF (FAEKF) based on the MEMS-IMU/ultrasonic sensor for pedestrians was proposed. Accordingly, the adaptive factor is applied to correct process noise covariance that accurately reflects previous state estimations. (2) A straight motion heading update (SMHU) algorithm is developed to detect whether a straight walk happens and to revise errors in heading if the ultrasonic sensor detects the distance between the foot and reflection point of the wall. The experimental results show that horizontal positioning error is less than 2% of the total travelled distance (TTD) in different environments, which is the same order of positioning error compared with other works using high-end MEMS-IMU. It is concluded that the proposed approach can achieve high performance for PNS in terms of accuracy and robustness.

scholarly journals An Augmented Reality Geo-Registration Method for Ground Target Localization from a Low-Cost UAV Platform

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3739
Author(s):  
Xiang Ren ◽  
Min Sun ◽  
Cheng Jiang ◽  
Lei Liu ◽  
Wei Huang
Keyword(s):  
Kalman Filter ◽  
Augmented Reality ◽  
Extended Kalman Filter ◽  
Low Cost ◽  
Video Stream ◽  
Target Localization ◽  
Measurement Unit ◽  
Gps Data ◽  
Registration Method ◽  
Rtk Gps

This paper presents an augmented reality-based method for geo-registering videos from low-cost multi-rotor Unmanned Aerial Vehicles (UAVs). The goal of the proposed method is to conduct an accurate geo-registration and target localization on a UAV video stream. The geo-registration of video stream requires accurate attitude data. However, the Inertial Measurement Unit (IMU) sensors on most low-cost UAVs are not capable of being directly used for geo-registering the video. The magnetic compasses on UAVs are more vulnerable to the interferences in the working environment than the accelerometers. Thus the camera yaw error is the main sources of the registration error. In this research, to enhance the low accuracy attitude data from the onboard IMU, an extended Kalman Filter (EKF) model is used to merge Real Time Kinematic Global Positioning System (RTK GPS) data with the IMU data. In the merge process, the high accuracy RTK GPS data can be used to promote the accuracy and stability of the 3-axis body attitude data. A method of target localization based on the geo-registration model is proposed to determine the coordinates of the ground targets in the video. The proposed method uses a modified extended Kalman Filter to combine the data from RTK GPS and the IMU to improve the accuracy of the geo-registration and the localization result of the ground targets. The localization results are compared to the reference point coordinates from satellite image. The comparison indicates that the proposed method can provide practical geo-registration and target localization results.

scholarly journals A DCM Based Attitude Estimation Algorithm for Low-Cost MEMS IMUs

2015 ◽  
Vol 2015 ◽  
pp. 1-18 ◽  
Author(s):  
Heikki Hyyti ◽  
Arto Visala
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Low Cost ◽  
Direction Cosine ◽  
Estimation Algorithm ◽  
Calibration Method ◽  
Attitude Estimation ◽  
Mems Sensors ◽  
Acceleration Sensors ◽  
Adaptive Extended Kalman Filter

An attitude estimation algorithm is developed using an adaptive extended Kalman filter for low-cost microelectromechanical-system (MEMS) triaxial accelerometers and gyroscopes, that is, inertial measurement units (IMUs). Although these MEMS sensors are relatively cheap, they give more inaccurate measurements than conventional high-quality gyroscopes and accelerometers. To be able to use these low-cost MEMS sensors with precision in all situations, a novel attitude estimation algorithm is proposed for fusing triaxial gyroscope and accelerometer measurements. An extended Kalman filter is implemented to estimate attitude in direction cosine matrix (DCM) formation and to calibrate gyroscope biases online. We use a variable measurement covariance for acceleration measurements to ensure robustness against temporary nongravitational accelerations, which usually induce errors when estimating attitude with ordinary algorithms. The proposed algorithm enables accurate gyroscope online calibration by using only a triaxial gyroscope and accelerometer. It outperforms comparable state-of-the-art algorithms in those cases when there are either biases in the gyroscope measurements or large temporary nongravitational accelerations present. A low-cost, temperature-based calibration method is also discussed for initially calibrating gyroscope and acceleration sensors. An open source implementation of the algorithm is also available.

scholarly journals Integration of GPS, Monocular Vision, and High Definition (HD) Map for Accurate Vehicle Localization

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3270 ◽  
Author(s):  
Hao Cai ◽  
Zhaozheng Hu ◽  
Gang Huang ◽  
Dunyao Zhu ◽  
Xiaocong Su
Keyword(s):  
Kalman Filter ◽  
Low Cost ◽  
Kinematic Model ◽  
Intelligent Vehicles ◽  
Monocular Vision ◽  
Measurement Unit ◽  
High Definition ◽  
Position Information ◽  
Vehicle Localization ◽  
Lateral Distance

Self-localization is a crucial task for intelligent vehicles. Existing localization methods usually require high-cost IMU (Inertial Measurement Unit) or expensive LiDAR sensors (e.g., Velodyne HDL-64E). In this paper, we propose a low-cost yet accurate localization solution by using a custom-level GPS receiver and a low-cost camera with the support of HD map. Unlike existing HD map-based methods, which usually requires unique landmarks within the sensed range, the proposed method utilizes common lane lines for vehicle localization by using Kalman filter to fuse the GPS, monocular vision, and HD map for more accurate vehicle localization. In the Kalman filter framework, the observations consist of two parts. One is the raw GPS coordinate. The other is the lateral distance between the vehicle and the lane, which is computed from the monocular camera. The HD map plays the role of providing reference position information and correlating the local lateral distance from the vision and the GPS coordinates so as to formulate a linear Kalman filter. In the prediction step, we propose using a data-driven motion model rather than a Kinematic model, which is more adaptive and flexible. The proposed method has been tested with both simulation data and real data collected in the field. The results demonstrate that the localization errors from the proposed method are less than half or even one-third of the original GPS positioning errors by using low cost sensors with HD map support. Experimental results also demonstrate that the integration of the proposed method into existing ones can greatly enhance the localization results.

Comparison of Attitude Determination Methodologies for Implementation with 9DOF, Low Cost Inertial Measurement Unit for Autonomous Aerial Vehicles

2013 ◽  
Vol 3 (2) ◽  
pp. 1-15 ◽  
Author(s):  
Man Ho Choi ◽  
Robert Porter ◽  
Bijan Shirinzadeh
Keyword(s):  
Kalman Filter ◽  
Gradient Descent ◽  
Attitude Determination ◽  
Low Cost ◽  
Euler Angle ◽  
Measurement Unit ◽  
Descent Algorithm ◽  
Complementary Filter ◽  
Gradient Descent Algorithm ◽  
Reference Input

The performances of three attitude determination algorithms are compared in this paper. The three methods are the Complementary Filter, a Quaternion-based Kalman Filter and a Quaternion-based Gradient Descent Algorithm. An analysis of their performance based on an experimental investigation was undertaken. This paper shows that the Complementary Filter requires the least computational power; Quaternion-based Kalman Filter has the best noise filtering ability; and the Quaternion-based Gradient Descent Algorithm produced estimates with the highest accuracy. As many attitude determination methodologies make use of the quaternion rotation representation, the attitude quaternion to Euler angle singularity property has been investigated. Experiments conducted show that when Y-rotation approach the singularity position (±90°), the X-rotation drifts away from the reference input. This paper proposes the use of an imaginary set of sensor measurements to replace the original sensor measurements as the Y-rotation approaches the singularity. The proposed methodology for overcoming the conversion singularity has been experimentally verified.

scholarly journals Angular velocity fusion of the microelectromechanical system inertial measurement unit array based on extended Kalman filter with correlated system noises

2020 ◽  
pp. 002029402091770
Author(s):  
Li Xing ◽  
Xiaowei Tu ◽  
Weixing Qian ◽  
Yang Jin ◽  
Pei Qi
Keyword(s):  
Kalman Filter ◽  
Angular Velocity ◽  
Extended Kalman Filter ◽  
Inertial Measurement Unit ◽  
Angular Rate ◽  
The State ◽  
Measurement Unit ◽  
Fusion Method ◽  
Microelectromechanical System ◽  
Inertial Measurement

The paper proposes an angular velocity fusion method of the microelectromechanical system inertial measurement unit array based on the extended Kalman filter with correlated system noises. In the proposed method, an adaptive model of the angular velocity is built according to the motion characteristics of the vehicles and it is regarded as the state equation to estimate the angular velocity. The signal model of gyroscopes and accelerometers in the microelectromechanical system inertial measurement unit array is used as the measurement equation to fuse and estimate the angular velocity. Due to the correlation of the state and measurement noises in the presented fusion model, the traditional extended Kalman filter equations are optimized, so as to accurately and reliably estimate the angular velocity. By simulating angular rates in different motion modes, such as constant and change-in-time angular rates, it is verified that the proposed method can reliably estimate angular rates, even when the angular rate has been out of the microelectromechanical system gyroscope measurement range. And results show that, compared with the traditional angular rate fusion method of microelectromechanical system inertial measurement unit array, it can estimate angular rates more accurately. Moreover, in the kinematic vehicle experiments, the performance advantage of the proposed method is also verified and the angular rate estimation accuracy can be increased by about 1.5 times compared to the traditional method.

Simulations of Experiments on Isothermal Containment Atmosphere Mixing Caused by Vertical Injection

2020 ◽  
Author(s):  
Rok Krpan ◽  
Iztok Tiselj ◽  
Ivo Kljenak
Keyword(s):  
Best Practice ◽  
Molecular Diffusion ◽  
Numerical Models ◽  
Three Dimensional ◽  
Momentum Equation ◽  
Correction Function ◽  
Best Practice Guidelines ◽  
Air Jet ◽  
Calculation Results ◽  
Definition Of

Abstract An experiment performed in SPARC experimental facility, was simulated with the computational fluid dynamics code OpenFOAM. The experiment took place in two phases. In the first phase, a helium-air layer was generated, which was then eroded with a vertical air jet injected in the vessel axis during the second experimental phase. A three-dimensional and a quasi-two-dimensional numerical models of a cylindrical vessel were developed and mesh convergence studies were performed. The mixing process was simulated as a single-phase flow with common momentum equation. Included gas species mass fractions are considered as passive scalars and are calculated using the transport equation. However, term describing the molecular diffusion cannot be neglected in our case and had to be added to diffusion equation implemented in default OpenFOAM solver. The k-ε turbulence model with additional buoyancy term implemented in OpenFOAM was used for turbulence modelling. Despite improvement of the physical model and following the Best Practice Guidelines, the results obtained with OpenFOAM CFD code still, at some locations, differ substantially from the experimental results. A modified definition of low Reynolds number eddy viscosity correction function is proposed, which significantly improves the agreement between measurements and calculation results.

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