Soft Measurement Technology for Tractor-Semi Trailer

2014 ◽  
Vol 494-495 ◽  
pp. 821-824
Author(s):  
Chao Yi Wei ◽  
Xu Guang Li ◽  
Mei Zhi Xie ◽  
Feng Yan Yi
Keyword(s):  
Covariance Matrix ◽  
Estimation Accuracy ◽  
Slip Angle ◽  
Measurement Technology ◽  
Noise Covariance Matrix ◽  
Measurement Noise Covariance ◽  
Noise Covariance ◽  
Set Up ◽  
Three Degree Of Freedom ◽  
Vehicle Dynamic Model

A three degree of freedom vehicle dynamic model of a tractor semi trailer is established and a simulation mode is set up with the Matlab/simulink software. Then, design a Kalman state estimator, using easily measured parameters to estimate the tractor side slip angle, trailer yaw rate those are difficult measured or measuring in high cost; the simulation show that: the estimated value coming from estimator agree well with the simulation measurements, and can improve the estimation accuracy by adjusting the process noise covariance matrix and measurement noise covariance matrix.

A New Adaptive Square-Root Unscented Kalman Filter for Nonlinear Systems

2013 ◽  
Vol 300-301 ◽  
pp. 623-626 ◽  
Author(s):  
Yong Zhou ◽  
Yu Feng Zhang ◽  
Ju Zhong Zhang
Keyword(s):  
Kalman Filter ◽  
Nonlinear Systems ◽  
Covariance Matrix ◽  
Unscented Kalman Filter ◽  
Square Root ◽  
Noise Covariance Matrix ◽  
Measurement Noise Covariance ◽  
Noise Covariance ◽  
The Stability ◽  
Stability And Accuracy

This paper describes a new adaptive filtering approach for nonlinear systems with additive noise. Based on Square-Root Unscented Kalman Filter (SRUKF), the traditional Maybeck’s estimator is modified and extended to the nonlinear systems, the estimation of square root of the process noise covariance matrix Q or measurement noise covariance matrix R is obtained straightforwardly. Then the positive semi-definiteness of Q or R is guaranteed, some shortcomings of traditional Maybeck’s algorithm are overcome, so the stability and accuracy of the filter is improved greatly.

Kalman Filter and Complementary Filter in Attitude Estimation of APM

2014 ◽  
Vol 577 ◽  
pp. 794-797 ◽  
Author(s):  
Feng Lin ◽  
Xi Lan Miao ◽  
Xiao Guang Qu
Keyword(s):  
Kalman Filter ◽  
Covariance Matrix ◽  
Attitude Estimation ◽  
Measurement Noise ◽  
Experimental Results ◽  
Extend Kalman Filter ◽  
Noise Covariance Matrix ◽  
Complementary Filter ◽  
Measurement Noise Covariance ◽  
Noise Covariance

This paper presents the results of a quaternion based extend Kalman filter (EKF) and complementary filter for ArduPilotMega (APM) attitude estimation. In addition, a new method to get the measurement noise covariance matrix R is proposed. Experimental results show that the two algorithms can meet the requirements, but the complementary filter can yield better performance than EKF.

scholarly journals A New Variational Bayesian Adaptive Extended Kalman Filter for Cooperative Navigation

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2538 ◽  
Author(s):  
Chengjiao Sun ◽  
Yonggang Zhang ◽  
Guoqing Wang ◽  
Wei Gao
Keyword(s):  
Kalman Filter ◽  
Covariance Matrix ◽  
Extended Kalman Filter ◽  
Measurement Noise ◽  
Noise Covariance Matrix ◽  
Cooperative Navigation ◽  
Error Covariance ◽  
Measurement Noise Covariance ◽  
Noise Covariance ◽  
Adaptive Extended Kalman Filter

To solve the problem of unknown state noises and uncertain measurement noises inherent in underwater cooperative navigation, a new Variational Bayesian (VB)-based Adaptive Extended Kalman Filter (VBAEKF) for master–slave Autonomous Underwater Vehicles (AUV) is proposed in this paper. The Inverse Wishart (IW) distribution is used to model the predicted error covariance and measurement noise covariance matrix. The state, together with the predicted error covariance and measurement noise covariance matrix, can be adaptively estimated based on VB approximation. The performance of the proposed algorithm is demonstrated through a lake trial, which shows the advantage of the proposed algorithm.

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