A Novel GPS Based Real Time Orbit Determination Using Adaptive Extended Kalman Filter

2018 ◽  
Vol E101.A (1) ◽  
pp. 287-292
Author(s):  
Yang XIAO ◽  
Limin LI ◽  
Jiachao CHANG ◽  
Kang WU ◽  
Guang LIANG ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Extended Kalman Filter ◽  
Orbit Determination ◽  
Adaptive Extended Kalman Filter

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.

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.

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.

Real-Time Signal Denoising Algorithm in Wheel Force Transducer

2014 ◽  
Vol 615 ◽  
pp. 244-247
Author(s):  
Dong Wang ◽  
Guo Yu Lin ◽  
Wei Gong Zhang
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Extended Kalman Filter ◽  
Unscented Kalman Filter ◽  
Force Transducer ◽  
Time Signal ◽  
Signal Denoising ◽  
Observation Function ◽  
Simulation Results ◽  
Wheel Force Transducer

The wheel force transducer (WFT) is used to measure dynamic wheel loads. Unlike other force sensors, WFT is rotating with the wheel. For this reason, the outputs and the inputs of the transducer are nonlinearly related, and traditional Kalman Filter is not suitable. In this paper, a new real-time filter algorithm utilizing Quadrature Kalman Filter (QKF) is proposed to solve this problem. In Quadrature Kalman Filter, Singer model is introduced to track the wheel force, and the observation function is established for WFT. The simulation results illustrate that the new filter outperforms the traditional Unscented Kalman Filter (UKF) and Extended Kalman Filter (EKF).

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