scholarly journals Moving Horizon Estimation for Cooperative Localisation with Communication Delay

2014 ◽  
Vol 68 (3) ◽  
pp. 493-510 ◽  
Author(s):  
Wei Gao ◽  
Jian Yang ◽  
Ju Liu ◽  
Hongyang Shi ◽  
Bo Xu
Keyword(s):  
Kalman Filter ◽  
Time Delay ◽  
Extended Kalman Filter ◽  
Acoustic Communication ◽  
Underwater Vehicle ◽  
Random Time ◽  
Computation Method ◽  
Moving Horizon Estimation ◽  
Underwater Environment ◽  
Simulation Results

Cooperative Localisation (CL) technology is required in some situations for Multiple Unmanned Underwater Vehicle (MUUVs) missions. During the CL process, the Relative Localisation Information (RLI) of the master UUV is transmitted to slave UUVs via acoustic communication. In the underwater environment, the RLI is subject to a random time delay. Considering the time delay characteristic of the RLI during the acoustic communication, a Moving Horizon Estimation (MHE) method with a Delayed Extended Kalman Filter (DEKF)-based arrival cost update law is presented in this paper to obtain an accurate and reliable estimation of present location. Additionally, an effective computation method for the MHE method is employed, in which the “Lower Upper” (LU) factorization is used to compute the solution of the Karush-Kuhn-Tucker (KKT) system. At the end of this paper, simulation results are presented to prove the superiority and practicality of the proposed MHE algorithm.

Gas pipeline leakage detection in the presence of parameter uncertainty using robust extended Kalman filter

2021 ◽  
pp. 014233122198911
Author(s):  
Mohadese Jahanian ◽  
Amin Ramezani ◽  
Ali Moarefianpour ◽  
Mahdi Aliari Shouredeli
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Parameter Uncertainty ◽  
Oil And Gas ◽  
Estimation Error ◽  
Real Data ◽  
Gas Pipeline ◽  
Pipeline System ◽  
Parameter Uncertainties ◽  
Simulation Results

One of the most significant systems that can be expressed by partial differential equations (PDEs) is the transmission pipeline system. To avoid the accidents that originated from oil and gas pipeline leakage, the exact location and quantity of leakage are required to be recognized. The designed goal is a leakage diagnosis based on the system model and the use of real data provided by transmission line systems. Nonlinear equations of the system have been extracted employing continuity and momentum equations. In this paper, the extended Kalman filter (EKF) is used to detect and locate the leakage and to attenuate the negative effects of measurement and process noises. Besides, a robust extended Kalman filter (REKF) is applied to compensate for the effect of parameter uncertainty. The quantity and the location of the occurred leakage are estimated along the pipeline. Simulation results show that REKF has better estimations of the leak and its location as compared with that of EKF. This filter is robust against process noise, measurement noise, parameter uncertainties, and guarantees a higher limit for the covariance of state estimation error as well. It is remarkable that simulation results are evaluated by OLGA software.

Real-Time Signal Denoising Algorithm in Wheel Force Transducer

2014 ◽  
Vol 615 ◽  
pp. 244-247
Author(s):  
Dong Wang ◽  
Guo Yu Lin ◽  
Wei Gong Zhang
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Extended Kalman Filter ◽  
Unscented Kalman Filter ◽  
Force Transducer ◽  
Time Signal ◽  
Signal Denoising ◽  
Observation Function ◽  
Simulation Results ◽  
Wheel Force Transducer

The wheel force transducer (WFT) is used to measure dynamic wheel loads. Unlike other force sensors, WFT is rotating with the wheel. For this reason, the outputs and the inputs of the transducer are nonlinearly related, and traditional Kalman Filter is not suitable. In this paper, a new real-time filter algorithm utilizing Quadrature Kalman Filter (QKF) is proposed to solve this problem. In Quadrature Kalman Filter, Singer model is introduced to track the wheel force, and the observation function is established for WFT. The simulation results illustrate that the new filter outperforms the traditional Unscented Kalman Filter (UKF) and Extended Kalman Filter (EKF).

A New Friction Estimation Approach for Precision Positioner

2007 ◽  
Author(s):  
Wen Wang ◽  
Xinxin Li ◽  
Zichen Chen
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Taylor Series ◽  
Estimation Method ◽  
Estimation Accuracy ◽  
Accurate Estimation ◽  
Low Velocity ◽  
Friction Estimation ◽  
Simulation Results ◽  
Long Stroke

Precision positioner has been significantly developed as the rapid growth of MEMS and IC industries. As for short-stroke position, the loss of friction can be avoided by using flexible hinges. Long-stroke postioner, however, in which moved-to-be mass always stands on the guide-way part, a main source of friction, makes friction unavoidable. Friction estimation is based on certain filters, such as Extended Kalman filter (EKF). However, estimation accuracy of Kalman filter, especially at low-velocity movement, is not very well. To solve this problem, the paper proposes an estimation method based on DD2 to make an accurate estimation. And the result shows this method is promising in real-time friction estimation. After background introduction, in section 2, the relation of EKF and Taylor series and EKF implementation are reviewed and its limitations are noted as well. A briefly introduction to DD2 is given in Section 3 and friction estimation case comparing the simulation results of DD2 estimation with that of EKF described in Section 4, respectively. At last, conclusions are summarized.

Automotive Battery Prognostics Using Dual Extended Kalman Filter

2009 ◽  
Author(s):  
Matteo Rubagotti ◽  
Simona Onori ◽  
Giorgio Rizzoni
Keyword(s):  
Experimental Data ◽  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Remaining Useful Life ◽  
State Of Charge ◽  
Future Evolution ◽  
On Line ◽  
Automotive Batteries ◽  
Useful Life ◽  
Simulation Results

This paper proposes a strategy for estimating the remaining useful life of automotive batteries based on dual Extended Kalman Filter. A nonlinear model of the battery is exploited for the on-line estimation of the State of Charge, and this information is used to evaluate the actual capacity and predict its future evolution, from which an estimate of the remaining useful life is obtained with suitable margins of uncertainty. Simulation results using experimental data from lead-acid batteries show the effectiveness of the approach.

Autonomous Aerial Refueling for UAVs Based on GPS/MV

2012 ◽  
Vol 433-440 ◽  
pp. 4087-4094 ◽  
Author(s):  
Long Wang ◽  
Xin Min Dong ◽  
Jun Guo ◽  
Hai Yan Jia
Keyword(s):  
Kalman Filter ◽  
Machine Vision ◽  
Extended Kalman Filter ◽  
Reference Trajectory ◽  
Sensor Failure ◽  
Aerial Refueling ◽  
Control Laws ◽  
Measurement Models ◽  
Simulation Results ◽  
Docking Control

According to the UAV autonomous aerial refueling based on GPS/Machine Vision integration, the restrictions on the sensors during docking are analyzed. An adaptive Federal Kalman Filter (AFKF) is proposed, which is based on extended Kalman filter arithmetic, after modeling the sensors measurement models. Reference trajectory of docking is planed using cubic interpolators and docking control laws are designed with LQR. Simulation results show that the controller ensure the stabilized tracking and docking, and the AFKF outputs is continuous and stabilized during sensor failure comparing to centralize Kalman filter.

scholarly journals A hypothesis of state covariance decorrelation effects to partial observability SLAM

2019 ◽  
Vol 14 (2) ◽  
pp. 588 ◽  
Author(s):  
H Ahmad ◽  
N.A Othman ◽  
M M Saari ◽  
M S Ramli ◽  
M M Mazlan ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Matrix Inversion ◽  
Matrix Norm ◽  
Main Element ◽  
Partial Observability ◽  
Localization And Mapping ◽  
The Matrix ◽  
Simulation Results ◽  
Partially Observable

<span>This paper analyze the performance of partial observability in simultaneous localization and mapping(SLAM) problem. The study focuses mainly on the effect of having a decorrelation technique known as Covariance Inflation to the estimation. The matrix inversion will be the main element to be investigated through two conditions with respect to some defined environment namely as unstable partially observable SLAM and partially observable SLAM via matrix norm analysis. For assessment purposes, the Extended Kalman Filter estimation is referred as the estimator to understand how the conditions can influence the results. The simulation results depicted that, the matrix norm is able to determine the efficiency of estimation and is proportional to the uncertainties of the system.</span>

scholarly journals Improvement of position and orientation of Unmanned Arial Vehicle (UAV) with INS/ GPS

2018 ◽  
Vol 7 (2.7) ◽  
pp. 642
Author(s):  
V Appala Raju ◽  
P Vasundhara ◽  
V ChandraKanth Reddy ◽  
A Sai Aiswarya
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Extended Kalman Filter ◽  
Navigation System ◽  
Reference System ◽  
Inertial Navigation System ◽  
Inertial Navigation ◽  
State Estimate ◽  
Simulation Results ◽  
Position And Orientation

This paper deals with the methods performing state estimation .that is position and orientation of Unmanned Arial Vehicle (UAV) using GPS, gyro, accelerometers and magnetometer sensors. Various methods are designed for position and orientation measurements of UAV. In this paper we proposed extended kalman filter based inertial navigation system using quaternions and 3D magnetometer. Initially we load UAV truth data from a file ,generate noisy UAV sensor measurements and perform UAV state estimation and display UAV state estimate results with proposed method compares with previously exited method extended  kalman filter based altitude and heading reference system using quaternion and 3D magnetometer simulation .Results shows that EKF-INS method gives better position and orientation of UAV.  

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.

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