scholarly journals Attitude Estimation Using Kalman Filtering: External Acceleration Compensation Considerations

2016 ◽  
Vol 2016 ◽  
pp. 1-24 ◽  
Author(s):  
Romy Budhi Widodo ◽  
Chikamune Wada
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Attitude Estimation ◽  
Estimation Accuracy ◽  
Compensation Model ◽  
Measurement Noise Covariance ◽  
Noise Covariance ◽  
The One ◽  
The Right ◽  
External Acceleration

Attitude estimation is often inaccurate during highly dynamic motion due to the external acceleration. This paper proposes extended Kalman filter-based attitude estimation using a new algorithm to overcome the external acceleration. This algorithm is based on an external acceleration compensation model to be used as a modifying parameter in adjusting the measurement noise covariance matrix of the extended Kalman filter. The experiment was conducted to verify the estimation accuracy, that is, one-axis and multiple axes sensor movement. Five approaches were used to test the estimation of the attitude: (1) the KF-based model without compensating for external acceleration, (2) the proposed KF-based model which employs the external acceleration compensation model, (3) the two-step KF using weighted-based switching approach, (4) the KF-based model which uses thethreshold-basedapproach, and (5) the KF-based model which uses the threshold-based approach combined with a softened part approach. The proposed algorithm showed high effectiveness during the one-axis test. When the testing conditions employed multiple axes, the estimation accuracy increased using the proposed approach and exhibited external acceleration rejection at the right timing. The proposed algorithm has fewer parameters that need to be set at the expense of the sharpness of signal edge transition.

scholarly journals Linear Kalman Filter for Attitude Estimation from Angular Rate and a Single Vector Measurement

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Shangqiu Shan ◽  
Zhongxi Hou ◽  
Jin Wu
Keyword(s):  
Kalman Filter ◽  
Attitude Determination ◽  
Angular Rate ◽  
Attitude Estimation ◽  
Estimation Accuracy ◽  
Kinematic Equation ◽  
Vector Measurement ◽  
Vector Observation ◽  
Execution Speed ◽  
External Acceleration

In this paper, a new Kalman filtering scheme is designed in order to give the optimal attitude estimation with gyroscopic data and a single vector observation. The quaternion kinematic equation is adopted as the state model while the quaternion of the attitude determination from a strapdown sensor is treated as the measurement. Derivations of the attitude solution from a single vector observation along with its variance analysis are presented. The proposed filter is named as the Single Vector Observation Linear Kalman filter (SVO-LKF). Flexible design of the filter facilitates fast execution speed with respect to other filters with linearization. Simulations and experiments are conducted in the presence of large external acceleration and magnetic distortion. The results show that, compared with representative filtering methods and attitude observers, the SVO-LKF owns the best estimation accuracy and it consumes much less time in the fusion process.

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 Position and Speed Estimation of PMSM Based on Extended Kalman Filter Tuned by Biogeography-Based-Optimization

2021 ◽  
Vol 54 (4) ◽  
pp. 559-568
Author(s):  
Samia Allaoui ◽  
Yahia Laamari ◽  
Kheireddine Chafaa ◽  
Salah Saad
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Great Influence ◽  
Correct Selection ◽  
State Variables ◽  
Measurement Noise Covariance ◽  
Noise Covariance ◽  
Trial And Error Method ◽  
Error Method ◽  
Selection Of

In a sensorless control of PMSM based on Extended Kalman Filter (EKF), the correct selection of system and measurement noise covariance has a great influence on the estimation performances of the filter. In fact, it is extremely difficult to find their optimal values by trial and error method. Therefore, the main contribution of this work is to prove the efficiency of Biogeography-Based-Optimization (BBO) technique to obtain the optimal noise covariance matrices Q and R. The BBO and EKF combination gives a BBO-EKF algorithm, which allows to estimate all the state variables of PMSM drive particularly, the rotor position and speed. In this paper, three evolutionary algorithms namely Particle Swarm Optimization (PSO), genetic algorithms (GAs) and BBO are used to get the best Q and R of EKF. Simulations tests performed in Matlab Simulink environment show excellent performance of BBO-EKF compared to GAs-EKF and PSO-EKF approaches either in resolution or in convergence speed.

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 Attitude Estimation Algorithm of Portable Mobile Robot Based on Complementary Filter

Micromachines ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 1373
Author(s):  
Mei Liu ◽  
Yuanli Cai ◽  
Lihao Zhang ◽  
Yiqun Wang
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Computational Cost ◽  
Attitude Estimation ◽  
Estimation Accuracy ◽  
Measurement Unit ◽  
Attitude Motion ◽  
Extended Kalman Filtering ◽  
Filtering Algorithm ◽  
Complementary Filter

In robot inertial navigation systems, to deal with the problems of drift and noise in the gyroscope and accelerometer and the high computational cost when using extended Kalman filter (EKF) and particle filter (PF), a complementary filtering algorithm is utilized. By combining the Inertial Measurement Unit (IMU) multi-sensor signals, the attitude data are corrected, and the high-precision attitude angles are obtained. In this paper, the quaternion algorithm is used to describe the attitude motion, and the process of attitude estimation is analyzed in detail. Moreover, the models of the sensor and system are given. Ultimately, the attitude angles are estimated by using the quaternion extended Kalman filter, linear complementary filter, and Mahony complementary filter, respectively. The experimental results show that the Mahony complementary filtering algorithm has less computational cost than the extended Kalman filtering algorithm, while the attitude estimation accuracy of these two algorithms is similar, which reveals that Mahony complementary filtering is more suitable for low-cost embedded systems.

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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