Importance of initial state covariance matrix for the parameter estimation using an adaptive extended Kalman filter

1998 ◽  
Author(s):  
R. Gemson ◽  
M. Anathasayanam
Keyword(s):  
Parameter Estimation ◽  
Kalman Filter ◽  
Covariance Matrix ◽  
Extended Kalman Filter ◽  
Initial State ◽  
Adaptive Extended Kalman Filter

scholarly journals Calculation Algorithm of Tire-Road Friction Coefficient Based on Limited-Memory Adaptive Extended Kalman Filter

2019 ◽  
Vol 2019 ◽  
pp. 1-14
Author(s):  
Bin Huang ◽  
Xiang Fu ◽  
Sen Wu ◽  
Song Huang
Keyword(s):  
Kalman Filter ◽  
Friction Coefficient ◽  
Covariance Matrix ◽  
Extended Kalman Filter ◽  
Estimation Accuracy ◽  
Limited Memory ◽  
Road Friction ◽  
Adaptive Extended Kalman Filter ◽  
Road Friction Coefficient ◽  
Real Vehicle

In this paper, a limited-memory adaptive extended Kalman Filter (LM-AEKF) to estimate tire-road friction coefficient is proposed. By combining extended Kalman filter (EKF) with the limited-memory filter, this algorithm can reduce the effects of old measurement data on filtering and improve the estimation accuracy. Self-adaptive regulatory factors were introduced to weigh covariance matrix of evaluated error. Meanwhile, measured noise covariance matrix was adjusted dynamically by fuzzy inference to accurately track the breaking status of system. Therefore, problems, including large filter error and divergence caused by incorrect model, can be solved. Joint simulation was conducted for the proposed algorithm with Carsim and Matlab/Simulink. Under the different road conditions, real-vehicle road tests were conducted in various working conditions for contrast with traditional EKF results. Simulation and real-vehicle road tests show that this algorithm can enhance the filter stability, improve the estimation accuracy of algorithm, and increase algorithm robustness.

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.

scholarly journals A DCM Based Attitude Estimation Algorithm for Low-Cost MEMS IMUs

2015 ◽  
Vol 2015 ◽  
pp. 1-18 ◽  
Author(s):  
Heikki Hyyti ◽  
Arto Visala
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Low Cost ◽  
Direction Cosine ◽  
Estimation Algorithm ◽  
Calibration Method ◽  
Attitude Estimation ◽  
Mems Sensors ◽  
Acceleration Sensors ◽  
Adaptive Extended Kalman Filter

An attitude estimation algorithm is developed using an adaptive extended Kalman filter for low-cost microelectromechanical-system (MEMS) triaxial accelerometers and gyroscopes, that is, inertial measurement units (IMUs). Although these MEMS sensors are relatively cheap, they give more inaccurate measurements than conventional high-quality gyroscopes and accelerometers. To be able to use these low-cost MEMS sensors with precision in all situations, a novel attitude estimation algorithm is proposed for fusing triaxial gyroscope and accelerometer measurements. An extended Kalman filter is implemented to estimate attitude in direction cosine matrix (DCM) formation and to calibrate gyroscope biases online. We use a variable measurement covariance for acceleration measurements to ensure robustness against temporary nongravitational accelerations, which usually induce errors when estimating attitude with ordinary algorithms. The proposed algorithm enables accurate gyroscope online calibration by using only a triaxial gyroscope and accelerometer. It outperforms comparable state-of-the-art algorithms in those cases when there are either biases in the gyroscope measurements or large temporary nongravitational accelerations present. A low-cost, temperature-based calibration method is also discussed for initially calibrating gyroscope and acceleration sensors. An open source implementation of the algorithm is also available.

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