scholarly journals Multibody-Based Input and State Observers Using Adaptive Extended Kalman Filter

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5241
Author(s):  
Antonio J. Rodríguez ◽  
Emilio Sanjurjo ◽  
Roland Pastorino ◽  
Miguel Á. Naya
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Adaptive Methods ◽  
Adaptive Method ◽  
Force Estimation ◽  
Adaptive Techniques ◽  
State Observers ◽  
State Estimators ◽  
Noise Covariance ◽  
Adaptive Extended Kalman Filter

The aim of this work is to explore the suitability of adaptive methods for state estimators based on multibody dynamics, which present severe non-linearities. The performance of a Kalman filter relies on the knowledge of the noise covariance matrices, which are difficult to obtain. This challenge can be overcome by the use of adaptive techniques. Based on an error-extended Kalman filter with force estimation (errorEKF-FE), the adaptive method known as maximum likelihood is adjusted to fulfill the multibody requirements. This new filter is called adaptive error-extended Kalman filter (AerrorEKF-FE). In order to present a general approach, the method is tested on two different mechanisms in a simulation environment. In addition, different sensor configurations are also studied. Results show that, in spite of the maneuver conditions and initial statistics, the AerrorEKF-FE provides estimations with accuracy and robustness. The AerrorEKF-FE proves that adaptive techniques can be applied to multibody-based state estimators, increasing, therefore, their fields of application.

Vision-based terrain referenced navigation of aerial vehicles using an adaptive extended Kalman filter

2017 ◽  
Vol 232 (8) ◽  
pp. 1584-1597 ◽  
Author(s):  
Sung-Hoon Mok ◽  
Youngjoo Kim ◽  
Hyochoong Bang
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Least Squares Method ◽  
New Approach ◽  
Terrain Referenced Navigation ◽  
Digital Terrain ◽  
Highly Nonlinear ◽  
Noise Covariance ◽  
Adaptive Extended Kalman Filter ◽  
Filter Divergence

This paper addresses a vision-based terrain referenced navigation of an aircraft. A digital terrain map, in the surroundings of the aircraft, is compared with the camera measurements to estimate the aircraft position. Generally, the measurement equation in the terrain referenced navigation is highly nonlinear due to the sharp changes of terrain. Thus, the conventional extended Kalman filter could lead to unstable navigation solutions. In this paper, a new approach using an adaptive extended Kalman filter is proposed to cope up with the nonlinearity problem. A least squares method is utilized to derive the linearized measurement equations. The Jacobian matrix and sensor noise covariance are modified as a means of smoothing the sharp changes of terrain. Monte Carlo simulations verify that the proposed filter gives the stable navigation solutions, even when there is a large initial error, which is the primary reason for the filter divergence. Moreover, the proposed adaptation barely requires additional computational burden, whereas the high-order filters such as particle filter generally needs higher computational power.

Design of parallel adaptive extended Kalman filter for online estimation of noise covariance

2018 ◽  
Vol 91 (1) ◽  
pp. 112-123 ◽  
Author(s):  
Kai Xiong ◽  
Liangdong Liu
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Position Estimation ◽  
Measurement Noise ◽  
Estimation Accuracy ◽  
Final State ◽  
Content Type ◽  
Noise Covariance ◽  
Adaptive Extended Kalman Filter ◽  
Noise Statistics

Purpose The successful use of the standard extended Kalman filter (EKF) is restricted by the requirement on the statistics information of the measurement noise. The covariance of the measurement noise may deviate from its nominal value in practical environment, and the filtering performance may decline because of the statistical uncertainty. Although the adaptive EKF (AEKF) is available for recursive covariance estimation, it is often less accurate than the EKF with accurate noise statistics. Design/methodology/approach Aiming at this problem, this paper develops a parallel adaptive EKF (PAEKF) by combining the EKF and the AEKF with an adaptive law, such that the final state estimate is dominated by the EKF when the prior noise covariance is accurate, while the AEKF is activated when the actual noise covariance deviates from its nominal value. Findings The PAEKF can reduce the sensitivity of the algorithm to the model uncertainty and ensure the estimation accuracy in the normal case. The simulation results demonstrate that the PAEKF has the advantage of both the AEKF and the EKF. Practical implications The presented algorithm is applicable for spacecraft relative attitude and position estimation. Originality/value The PAEKF is presented for a kind of nonlinear uncertain systems. Stability analysis is provided to show that the error of the estimator is bounded under certain assumptions.

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.

A variable structure adaptive extended Kalman filter for vehicle slip angle estimation

2011 ◽  
Vol 56 (1/2/3/4) ◽  
pp. 161 ◽  
Author(s):  
ang Li ◽  
Jian Song ◽  
Hongzhi Li ◽  
Zhang Xiaolong ◽  
ang Li ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Variable Structure ◽  
Slip Angle ◽  
Angle Estimation ◽  
Adaptive Extended Kalman Filter
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