An Improved Particle Filtering Algorithm Using Different Correlation Coefficients for Nonlinear System State Estimation

Big Data ◽  
2019 ◽  
Vol 7 (2) ◽  
pp. 114-120
Author(s):  
Qingxu Meng ◽  
Kaicheng Li ◽  
Chen Zhao
Keyword(s):  
Nonlinear System ◽  
State Estimation ◽  
Particle Filtering ◽  
Correlation Coefficients ◽  
System State ◽  
Filtering Algorithm

scholarly journals Stability Control of Energy Saving Electric Vehicle Using Dynamic Nonlinear System State Estimation

Fractals ◽  
2021 ◽  
Author(s):  
Liancheng Zhang ◽  
Weiping Ai ◽  
Zhibin Liu ◽  
Long Zhang ◽  
Xinmiao Teng
Keyword(s):  
Nonlinear System ◽  
State Estimation ◽  
Energy Saving ◽  
Electric Vehicle ◽  
Stability Control ◽  
System State

Carrier Aircraft Urgent Takeoff In-Flight Alignment Extended Set-Membership Estimation

2014 ◽  
Vol 945-949 ◽  
pp. 2772-2779
Author(s):  
Yi Yao ◽  
Shuang Pan ◽  
Yu Qiang Wu
Keyword(s):  
Time Delay ◽  
Nonlinear System ◽  
State Estimation ◽  
Robust Estimation ◽  
Inertial Navigation System ◽  
Estimation Algorithm ◽  
System State ◽  
Nonlinear Error ◽  
Set Membership ◽  
Measurement Delay

This paper considers the nolinear system robust estimation problem with measurement time delay. The purpose is to provide algorithm for inertial navigation system nonlinear error model in transmittion delay case. Considering that innovation regroup method can treat the measurement delay problem better with no addition of system state dimension, and decrease the system computation, therefore, that algorithm is combined with extended set-membership estimation and deduce the distrete nonlinear system extended set-membership estimation algorithm with measurement delay, realize nonlinear system varying state estimation and the treatment of measurement delay. At last, apply the algorithm to carrier aircraft in-flight alignment and validate the effecitiveness.

scholarly journals Research on the method of information system risk state estimation based on clustering particle filter

Open Physics ◽  
2017 ◽  
Vol 15 (1) ◽  
pp. 240-246 ◽  
Author(s):  
Jia Cui ◽  
Bei Hong ◽  
Xuepeng Jiang ◽  
Qinghua Chen
Keyword(s):  
Risk Assessment ◽  
Information System ◽  
State Estimation ◽  
Particle Filtering ◽  
Dynamic Assessment ◽  
Assessment Method ◽  
Management Control ◽  
Limited Information ◽  
Filtering Algorithm ◽  
System Risk

Abstract With the purpose of reinforcing correlation analysis of risk assessment threat factors, a dynamic assessment method of safety risks based on particle filtering is proposed, which takes threat analysis as the core. Based on the risk assessment standards, the method selects threat indicates, applies a particle filtering algorithm to calculate influencing weight of threat indications, and confirms information system risk levels by combining with state estimation theory. In order to improve the calculating efficiency of the particle filtering algorithm, the k-means cluster algorithm is introduced to the particle filtering algorithm. By clustering all particles, the author regards centroid as the representative to operate, so as to reduce calculated amount. The empirical experience indicates that the method can embody the relation of mutual dependence and influence in risk elements reasonably. Under the circumstance of limited information, it provides the scientific basis on fabricating a risk management control strategy.

Sign in / Sign up

Export Citation Format

Share Document