scholarly journals Performance evaluation of GPS / BDS combined single-point positioning based on RTKLIB

2020 ◽  
Vol 165 ◽  
pp. 03009
Author(s):  
Li Yan-yi ◽  
Huang Jin ◽  
Tang Ming-xiu
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Single Point ◽  
Least Square Method ◽  
Least Square ◽  
Filter Method ◽  
Single System ◽  
Positioning Error ◽  
Dual Mode ◽  
Kalman Filter Method

In order to evaluate the performance of GPS / BDS, RTKLIB, an open-source software of GNSS, is used in this paper. In this paper, the least square method, the weighted least square method and the extended Kalman filter method are respectively applied to BDS / GPS single system for data solution. Then, the BDS system and GPS system are used for fusion positioning and the positioning results of the two systems are compared with that of the single system. Through the comparison of experiments, on the premise of using the extended Kalman filter method for positioning, when the GPS signal is not good, BDS data is introduced for dual-mode positioning, the positioning error in e direction is reduced by 36.97%, the positioning error in U direction is reduced by 22.95%, and the spatial positioning error is reduced by 16.01%, which further reflects the advantages of dual-mode positioning in improving a system robustness and reducing the error.

The State of Charge Estimation of Lithium Battery in Electric Vehicle Based on Extended Kalman Filter

2014 ◽  
Vol 953-954 ◽  
pp. 796-799
Author(s):  
Huan Huan Sun ◽  
Jun Bi ◽  
Sai Shao
Keyword(s):  
Kalman Filter ◽  
Electric Vehicle ◽  
Extended Kalman Filter ◽  
Nonlinear Problems ◽  
Least Square Method ◽  
State Equation ◽  
State Of Charge ◽  
Least Square ◽  
Accurate Estimation ◽  
Battery State Of Charge

Accurate estimation of battery state of charge (SOC) is important to ensure operation of electric vehicle. Since a nonlinear feature exists in battery system and extended kalman filter algorithm performs well in solving nonlinear problems, the paper proposes an EKF-based method for estimating SOC. In order to obtain the accurate estimation of SOC, this paper is based on composite battery model that is a combination of three battery models. The parameters are identified using the least square method. Then a state equation and an output equation are identified. All experimental data are collected from operating EV in Beijing. The results of the experiment show  that the relative error of estimation of state of charge is reasonable, which proves this method has good estimation performance.

Adaptive Modeling of Laser Powder Deposition Process for Control and Monitoring Application

2005 ◽  
Author(s):  
Mohammad Durali ◽  
Alireza Fathi ◽  
Amir Khajepour ◽  
Ehsan Toyserkani
Keyword(s):  
Kalman Filter ◽  
System Identification ◽  
Least Square Method ◽  
Deposition Process ◽  
Least Square ◽  
Filter Method ◽  
Recursive Least Square ◽  
Noise Elimination ◽  
Laser Powder Deposition ◽  
Powder Deposition

Laser Powder Deposition technique is an advanced production method with many applications. Despite this fact, reliable and accurate control schemes have not yet fully developed for this method. This article presents method for in time identification of the process for modeling and adaptation of proper control strategy. ARMAX structure is chosen for system model. Recursive least square method and Kalman Filter methods are adopted for system identification, and their performance are compared. Experimental data was used for system identification, and proper filtering schemes are devised here for noise elimination and increased estimation results. It was concluded that although both methods yield efficient performance and accurate results, Kalman Filter method gives better results in parameter estimations. The comparison of the results shows that this method can be used very efficiently in control and monitoring of Laser Powder Deposition process.

Investigation on adaptive method of torque characteristic in dual clutch transmission during launch phase

2020 ◽  
Vol 234 (3) ◽  
pp. 568-584
Author(s):  
Hongtao Hao ◽  
Tongli Lu ◽  
Jianwu Zhang ◽  
Wenjie Ding
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Sliding Friction ◽  
Least Square Method ◽  
Adaptive Method ◽  
Least Square ◽  
Dual Clutch Transmission ◽  
Engine Torque ◽  
Simulation And Experiment ◽  
Application Potential

To reduce sliding friction work and clutch judder, an adaptive method of torque characteristic in dual clutch transmission during launch phase is presented in this paper. The proposed approach provides a tool to identify the change of torque characteristic and adapt it in real time. Firstly, to reduce the influence of the error between the nominal engine torque and the actual engine torque, the estimator based on the extended Kalman filter is designed to estimate the transmitted torque of the dual-clutch transmission during launch phase. Furthermore, the torque characteristic adaptive method is presented by a combination of the estimator with the improved Least Square Method. Then, based on the established driveline model of the dual-clutch transmission, the torque characteristic adaptive method during launch phase is validated by MATLAB/Simulink. Finally, in order to further evaluate the application potential of the adaptive method, the experiments are conducted on a production vehicle equipped with the wet dual-clutch transmission. The simulation and experiment results show that the proposed algorithms work well.

Application of Improved Extended Kalman Filter for Adaptive Model of Aircraft Engine

2014 ◽  
Vol 709 ◽  
pp. 180-185
Author(s):  
Gu Ting Zhou ◽  
San Mai Su
Keyword(s):  
Kalman Filter ◽  
Fault Diagnosis ◽  
Extended Kalman Filter ◽  
Performance Monitoring ◽  
Aircraft Engine ◽  
Filter Method ◽  
Adaptive Model ◽  
Nominal Model ◽  
Simulation Results ◽  
Kalman Filter Method

Adaptive model is the basis of engine fault diagnosis, performance monitoring, engine control, etc. This paper presents an improved kalman filter method which uses engine measurable parameters deviation to estimate the degradation parameters to correct the nominal model, and the acquisition and application of multiple kalman filter gain matrices in the whole flight envelope is analyzed. Simulation is carried out taking a civil engine as simulation object, the simulation results show that the method used in this paper to establish unmeasured parameters adaptive model can get the engine parameters better.

scholarly journals A Progressive Extended Kalman Filter Method for Freeway Traffic State Estimation Integrating Multisource Data

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Yingshun Liu ◽  
Shanglu He ◽  
Bin Ran ◽  
Yang Cheng
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Extended Kalman Filter ◽  
State Space Model ◽  
Heterogeneous Data ◽  
Filter Method ◽  
Multiple Sources ◽  
Freeway Traffic ◽  
Traffic Conditions ◽  
Kalman Filter Method

Variable techniques have been used to collect traffic data and estimate traffic conditions. In most cases, more than one technology is available. A legitimate need for research and application is how to use the heterogeneous data from multiple sources and provide reliable and consistent results. This paper aims to integrate the traffic features extracted from the wireless communication records and the measurements from the microwave sensors for the state estimation. A state-space model and a Progressive Extended Kalman Filter (PEKF) method are proposed. The results from the field test exhibit that the proposed method efficiently fuses the heterogeneous multisource data and adaptively tracks the variation of traffic conditions. The proposed method is satisfactory and promising for future development and implementation.

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