Real-Time and High-Performance Attitude and Heading Reference System Based on MIMU/Magnetometers

2009 ◽  
Vol 60-61 ◽  
pp. 219-223 ◽  
Author(s):  
Wei Qin ◽  
Wei Zheng Yuan ◽  
Hong Long Chang ◽  
Liang Xue ◽  
Guang Min Yuan
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Extended Kalman Filter ◽  
Reference System ◽  
High Performance ◽  
Dynamic Mode ◽  
Stationary Mode ◽  
Real Time Measurement ◽  
Data Refresh ◽  
Adaptive Extended Kalman Filter

In the paper, an attitude and heading reference system based on MIMU/magnetometers with moderate accuracy is presented. To meet the requirements of the real-time measurement, a master/slave CPU structure is proposed in order to improve the data refresh rate effectively. In the algorithm part, an adaptive extended Kalman filter equation is applied in the system, where the filter equation uses three tilt angles of attitude and three bias errors for the gyroscopes as state vectors, the measurements of three accelerometers and magnetometers are used to drive the state update. When the system is in dynamic mode, the measured values of the accelerometers consist of the gravity vector and the dynamic accelerations, an adaptive extended Kalman filter tunes its gain automatically based on the system dynamics sensed by the accelerometers to yield optimal performance. The experiment result shows that the attitude and heading angle errors are within 0.2 deg and 0.5 deg respectively in stationary mode, and the result can reflect the attitude angles reasonably in dynamic mode.

Hardware Implementation Optimization of Extended Kalman Filter for the Estimation of State of Charge of Li-Ion Battery

2015 ◽  
Vol 713-715 ◽  
pp. 1094-1098
Author(s):  
Hong Ce Zhang ◽  
Pu Shen Wang ◽  
Jiang Jiang ◽  
Hong Fei Cao
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Extended Kalman Filter ◽  
High Performance ◽  
Hardware Implementation ◽  
State Of Charge ◽  
Li Ion Batteries ◽  
Soc Estimation ◽  
Li Ion ◽  
Time Restriction

Extended Kalman Filter (EKF) is widely studied in the field of State of Charge (SOC) estimation of Li-ion batteries, however, in applications like Electric Vehicles (EV), there are usually a large number of individual battery cells. In order to meet the demand of real-time computation, MCU of high performance is essential. In this paper, we proposed a hardware structure to implement EKF which is economical in area and power consumption and could be easily integrated in a larger design and at the same time could satisfy the real-time restriction.

scholarly journals An Improved Adaptive Extended Kalman Filter Algorithm of SINS/GPS Loosely-Coupled Integrated Navigation System

2018 ◽  
Vol 7 (4.27) ◽  
pp. 87
Author(s):  
Yuyan Wang ◽  
Xiuyun Meng ◽  
Jilu Liu
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Extended Kalman Filter ◽  
Navigation System ◽  
Measurement Noise ◽  
Integrated Navigation ◽  
Integrated Navigation System ◽  
Loosely Coupled ◽  
Kalman Filter Algorithm ◽  
Adaptive Extended Kalman Filter

The Kalman Filter algorithm usually cannot estimate noise statistics in real-time, in order to deal with this issue, a new kind of improved Adaptive Extended Kalman Filter algorithm is proposed. Based on residual sequence, this algorithm mainly improves the adaptive estimator of the filter algorithm, which can estimate measurement noise in real-time. Furthermore, this new filter algorithm is applied to a SINS/GPS loosely-coupled integrated navigation system, which can automatically adjust the covariance matrix of measurement noise as noise varies in the system. Finally, the original Extended Kalman Filter and the improved Adaptive Extended Kalman Filter are applied respectively to simulate for the SINS/GPS loosely-coupled model. Tests demonstrate that, the improved Adaptive Extended Kalman Filter reduces both position error and velocity error compared with the original Extended Kalman Filter.  

scholarly journals A Novel Fuzzy-Adaptive Extended Kalman Filter for Real-Time Attitude Estimation of Mobile Robots

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 803 ◽  
Author(s):  
Ákos Odry ◽  
Istvan Kecskes ◽  
Peter Sarcevic ◽  
Zoltan Vizvari ◽  
Attila Toth ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Extended Kalman Filter ◽  
Attitude Estimation ◽  
Sensor Data ◽  
External Disturbances ◽  
Filter Performance ◽  
Filter Structure ◽  
Adaptive Extended Kalman Filter ◽  
Fuzzy Adaptive

This paper proposes a novel fuzzy-adaptive extended Kalman filter (FAEKF) for the real-time attitude estimation of agile mobile platforms equipped with magnetic, angular rate, and gravity (MARG) sensor arrays. The filter structure employs both a quaternion-based EKF and an adaptive extension, in which novel measurement methods are used to calculate the magnitudes of system vibrations, external accelerations, and magnetic distortions. These magnitudes, as external disturbances, are incorporated into a sophisticated fuzzy inference machine, which executes fuzzy IF-THEN rules-based adaption laws to consistently modify the noise covariance matrices of the filter, thereby providing accurate and robust attitude results. A six-degrees of freedom (6 DOF) test bench is designed for filter performance evaluation, which executes various dynamic behaviors and enables measurement of the true attitude angles (ground truth) along with the raw MARG sensor data. The tuning of filter parameters is performed with numerical optimization based on the collected measurements from the test environment. A comprehensive analysis highlights that the proposed adaptive strategy significantly improves the attitude estimation quality. Moreover, the filter structure successfully rejects the effects of both slow and fast external perturbations. The FAEKF can be applied to any mobile system in which attitude estimation is necessary for localization and external disturbances greatly influence the filter accuracy.

A New State Estimator of PMSM Using Adaptive Extended Kalman Filter

2012 ◽  
Vol 430-432 ◽  
pp. 772-780
Author(s):  
Xin Zhong Ding ◽  
Cheng Rui Zhang ◽  
Le Hua Yu ◽  
Hu Xiu Li
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
High Performance ◽  
Permanent Magnet Synchronous Motor ◽  
Noisy Environment ◽  
State Estimator ◽  
Inertia Moment ◽  
Noise Simulation ◽  
Adaptive Extended Kalman Filter ◽  
Model Reference Adaptive

This paper presents a new permanent magnet synchronous motor (PMSM) drive technique using adaptive state estimator for high-performance motion control to estimate the instantaneous speed, position and disturbance load torque. In the proposed algorithm, the model reference adaptive control (MRAC) method is incorporated to identify the variations of inertia moment, and the identified inertia is used to adapt the extended Kalman filter (EKF), which is an optimal state estimator to provide good estimation performance for the rotor speed, rotor position and disturbance torque with low precision quadrature encoder in a random noisy environment. In addition, the disturbance–rejection ability and the robustness to variations of the mechanical parameters are discussed and it is verified that the system is robust to the modeling error and system noise. Simulation and experimental results confirm the validity of the proposed estimation technique.

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