scholarly journals An improved real-time adaptive Kalman filter with recursive noise covariance updating rules

2016 ◽  
Vol 24 ◽  
pp. 524-540 ◽  
Author(s):  
Iyad HASHLAMON ◽  
Kemalettin ERBATUR
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Adaptive Kalman Filter ◽  
Noise Covariance

An adaptive Kalman filter based on variance component estimation for a real-time ZTD solution

2019 ◽  
Vol 54 (1) ◽  
pp. 89-121 ◽  
Author(s):  
Xu Yang ◽  
Guobin Chang ◽  
Qianxin Wang ◽  
Shubi Zhang ◽  
Ya Mao ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Variance Component ◽  
Variance Component Estimation ◽  
Adaptive Kalman Filter ◽  
Component Estimation

The Application of Adaptive Kalman Filter in Traffic Flow Forecasting

2013 ◽  
Vol 680 ◽  
pp. 495-500 ◽  
Author(s):  
Jun Wei Gao ◽  
Zi Wen Leng ◽  
Bin Zhang ◽  
Xin Liu ◽  
Guo Qiang Cai
Keyword(s):  
Kalman Filter ◽  
Traffic Flow ◽  
Kalman Filtering ◽  
Intelligent Transportation System ◽  
Urban Traffic ◽  
Forecasting Model ◽  
Adaptive Kalman Filter ◽  
Forecasting Accuracy ◽  
Traffic Flow Forecasting ◽  
Noise Covariance

The urban traffic usually has the characteristics of time-variation and nonlinearity, real-time and accurate traffic flow forecasting has become an important component of the Intelligent Transportation System (ITS). The paper gives a brief introduction of the basic theory of Kalman filter, and establishes the traffic flow forecasting model on the basis of the adaptive Kalman filter, while the traditional Kalman filtering model has the shortcomings of lower forecasting accuracy and easily running into filtering divergence. The Sage&Husa adaptive filtering algorithm will appropriately estimate and correct the unknown or uncertain noise covariance, so as to improve the dynamic characteristics of the model. The simulation results demonstrate that the adaptive Kalman filtering forecasting model has stronger tracking capability and higher forecasting precision, which is applicable to the traffic flow forecasting.

The Adaptive Kalman Filter Based on Improved Variable Oblivion Factor Least Square Arithmetic

2011 ◽  
Vol 181-182 ◽  
pp. 124-129
Author(s):  
Yu Song
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Transfer Matrix ◽  
Least Square ◽  
Flood Forecast ◽  
Adaptive Kalman Filter ◽  
Dam Reservoir ◽  
State Transfer

Adopting improved variable oblivion factor least square arithmetic to real-time amend Kalman’s state transfer matrix, we put Maple Dam reservoir flood forecast real-time adjustment for example, then apply and compare with other ways. The result shows this arithmetic is preferable.

scholarly journals Multi-Fading Factor and Update Monitoring Strategy Adaptive Kalman Filter Based Variational Bayesian with Inaccurate Time-Varying Process and Measure-Ment Noise Covariance Matrices

2020 ◽  
Author(s):  
Chenghao Shan ◽  
Weidong Zhou ◽  
Yefeng Yang ◽  
Zihao Jiang
Keyword(s):  
Kalman Filter ◽  
Covariance Matrix ◽  
Measurement Noise ◽  
Time Varying ◽  
Monitoring Strategy ◽  
Adaptive Kalman Filter ◽  
Variational Bayesian ◽  
Measurement Noise Covariance ◽  
Noise Covariance ◽  
Fading Factor

Aiming at the problem that the performance of Adaptive Kalman filter estimation will be affected when the statistical characteristics of the process and measurement noise matrix are inaccurate and time-varying in the linear Gaussian state-space model, an algorithm of Multi-fading factor and update monitoring strategy adaptive Kalman filter based variational Bayesian is proposed. Inverse Wishart distribution is selected as the measurement noise model, the system state vector and measurement noise covariance matrix are estimated with the variational Bayesian method. The process noise covariance matrix is estimated by the maximum a posteriori principle, and the update monitoring strategy with adjustment factors is used to maintain the positive semi-definite of the updated matrix. The above optimal estimation results are introduced as time-varying parameters into the multiple fading factors to improve the estimation accuracy of the one-step state predicted covariance matrix. The application of the proposed algorithm in target tracking is simulated. The results show that compared with the current filters, the proposed filtering algorithm has better accuracy and convergence performance, and realizes the simultaneous estimation of inaccurate time-varying process and measurement noise covariance matrices.

Sign in / Sign up

Export Citation Format

Share Document