scholarly journals A Comparison Between Numerical Differentiation and Kalman Filtering for a Leo Satellite Velocity Determination

2013 ◽  
Vol 48 (3) ◽  
pp. 103-110 ◽  
Author(s):  
M.A. Sharifi ◽  
M.R. Seif ◽  
M.A. Hadi
Keyword(s):  
Kalman Filtering ◽  
Velocity Vector ◽  
Numerical Differentiation ◽  
Filter Method ◽  
Newton Interpolation ◽  
Satellite Velocity ◽  
Kalman Filter Method ◽  
Range Rate ◽  
Noise Amplification ◽  
Leo Satellite

Abstract The kinematic orbit is a time series of position vectors generally obtained from GPS observations. Velocity vector is required for satellite gravimetry application. It cannot directly be observed and should be numerically determined from position vectors. Numerical differentiation is usually employed for a satellite’s velocity, and acceleration determination. However, noise amplification is the single obstacle to the numerical differentiation. As an alternative, velocity vector is considered as a part of the state vector and is determined using the Kalman filter method. In this study, velocity vector is computed using the numerical differentiation (e.g., 9-point Newton interpolation scheme) and Kalman filtering for the GRACE twin satellites. The numerical results show that Kalman filtering yields more accurate results than numerical differentiation when they are compared with the intersatellite range-rate measurements.

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.

Fault diagnose of the rolling bearings vibration signals based on Kalman filter method

2021 ◽  
Author(s):  
Qingpeng Han ◽  
Xinhang Shen ◽  
Bin Wu ◽  
Rui Zhu ◽  
Daolei Wang ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Filter Method ◽  
Fault Diagnose ◽  
Rolling Bearings ◽  
Vibration Signals ◽  
Kalman Filter Method

scholarly journals Two unbiased converted measurement Kalman filtering algorithms with range rate

2018 ◽  
Vol 12 (11) ◽  
pp. 1217-1224 ◽  
Author(s):  
Hongqiang Liu ◽  
Zhongliang Zhou ◽  
Lei Yu ◽  
Chunguang Lu
Keyword(s):  
Kalman Filtering ◽  
Filtering Algorithms ◽  
Range Rate

An Implicit Algorithm of Solving Nonlinear Filtering Problems

2014 ◽  
Vol 16 (2) ◽  
pp. 382-402
Author(s):  
Feng Bao ◽  
Yanzhao Cao ◽  
Xiaoying Han
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
Nonlinear Filtering ◽  
State Equation ◽  
Nonlinear Filter ◽  
Filter Method ◽  
Simulation Methods ◽  
Current State ◽  
Numerical Simulation Methods ◽  
Kalman Filter Method

AbstractNonlinear filter problems arise in many applications such as communications and signal processing. Commonly used numerical simulation methods include Kalman filter method, particle filter method, etc. In this paper a novel numerical algorithm is constructed based on samples of the current state obtained by solving the state equation implicitly. Numerical experiments demonstrate that our algorithm is more accurate than the Kalman filter and more stable than the particle filter.

An Improved Adaptive Kalman Filter for Gravity Anomaly Distortion Correction

2011 ◽  
Vol 121-126 ◽  
pp. 1421-1425
Author(s):  
Li Ye Zhao ◽  
Hong Sheng Li
Keyword(s):  
Kalman Filter ◽  
Gravity Anomaly ◽  
Distortion Correction ◽  
The Self ◽  
Filter Method ◽  
Current Time ◽  
Gain Matrix ◽  
Gravity Signal ◽  
Kalman Filter Method ◽  
Correlation Sequences

Combined with the system state equation and the measurement equation, a new method of cascade Kalman filter is proposed and applied to the correction of gravity anomaly distortion. In the signal processing procedure, according to the self-correlation sequences of the measurement gravity signal, the relation of the gain matrix K and the self-correlation sequences could be obtain, and the gravity signal at current time can be calculated by the gain matrix K. Emulations and experiments indicate that both the cascade Kalman filter method and the single inverse Kalman filter method are effective in alleviating the distortion of the gravity anomaly signal, but the performance of the cascade Kalman filter method is better than that of single inverse Kalman filter method.

The Dangerous Rock Mass Deformation Analysis with Application of Kalman Filter Method Based on Material Properties and AR Model

2013 ◽  
Vol 738 ◽  
pp. 109-112
Author(s):  
Fu Min Lu ◽  
Ting Yao Jiang
Keyword(s):  
Kalman Filter ◽  
Rock Mass ◽  
Ar Model ◽  
Deformation Analysis ◽  
Filter Method ◽  
The Status ◽  
Rock Mass Deformation ◽  
Kalman Filter Method ◽  
Mass Deformation ◽  
Dangerous Rock Mass

Considering the material property of the rock to the dangerous rock mass,the paper Looks the model parameter of AR( 1) model as the status vector, and uses Kalman filter method to analysis the deformation of the dangerous rock mass. The result shows that the method can improve the accuracy of fitting and forecasting of the model.

Optimization of Stock Portfolios Using Goal Programming Based on the Kalman-Filter Method

2021 ◽  
Vol 7 (1) ◽  
pp. 20-30
Author(s):  
Fauziyah ◽  
Evita Purnaningrum
Keyword(s):  
Kalman Filter ◽  
Goal Programming ◽  
Stock Prices ◽  
Stock Price ◽  
Filter Method ◽  
Stock Portfolios ◽  
Stock Investment ◽  
Kalman Filter Method ◽  
Return Difference ◽  
Stock Portfolio

Long-term stock investment development is carried out by means of portfolio optimization. Selection of stocks for portfolios is not only based on high-value stock prices but also takes into account their fluctuations. Estimation of future stock price fluctuations has an indirect impact on future portfolio formation. This research has implemented the Kalman filter method to obtain the best estimation results from various stock prices with a high degree of accuracy. The results are then used to form a stock portfolio on the basis of Goal Programming. This study has compared the optimization results with the real value of stock prices. The results obtained, Kalman filter-based Goal Programming is more effective for predicting future portfolios compared to the Goal Programming method with a return difference of Rp. 178,039,848. This suggests that optimization with the Kalman Filter-based Objective Programming can be used as a tool to determine future stock portfolios.

scholarly journals Active oscillation control of electric vehicles with two-speed transmission considering nonlinear backlash

2019 ◽  
Vol 234 (1) ◽  
pp. 116-133 ◽  
Author(s):  
Jianwu Zhang ◽  
Benben Chai ◽  
Xingyang Lu
Keyword(s):  
Control Algorithm ◽  
Estimation Method ◽  
Hierarchical Architecture ◽  
Filter Method ◽  
Torsional Angle ◽  
Traction Motor ◽  
Oscillation Control ◽  
Electric Drive System ◽  
Vehicle Mass ◽  
Kalman Filter Method

The nonlinear backlash can influence the driveability of the electric driveline due to the lack of the traditional clutches and torsional damper devices. During the stand-start, regenerative braking and gear shift process of the electric drive system equipped with two-speed transmission, the request of transient traction motor change will transverse the powertrain backlash, which will excite the driveline oscillations coupled to the first rigid mode of the entire powertrain, referred as shuffle or shunt. These vibrations are transmitted to the chassis, causing deterioration in passenger comfort. In this paper, the nonlinear driveline model including the traction motor, half shaft, driveline backlash, and car wheel are developed. Then, dual extended Kalman filter method is employed to estimate vehicle mass and half shaft torsional angle of the proposed driveline. Based on the estimated information, a hierarchical architecture of the active oscillation compensation is proposed to alleviate the vibration in both contact and backlash mode. The experimental results show that the estimation method is effective and the vehicle shuffle can be significantly decreased by the developed control algorithm.

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