Low-cost attitude determination system using an extended Kalman filter (EKF) algorithm

2016 ◽  
Author(s):  
Fernando M. Esteves ◽  
Georges Nehmetallah ◽  
Jandro L. Abot
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Attitude Determination ◽  
Low Cost ◽  
Determination System

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.

Influence analysis of measurement errors in satellite attitude determination based on extended Kalman filter

2012 ◽  
Vol 116 (1178) ◽  
pp. 373-389
Author(s):  
Y. Jiao ◽  
J. Wang ◽  
X. Pan ◽  
H. Zhou
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Measurement Errors ◽  
Attitude Determination ◽  
Previous Analysis ◽  
Measurement Model ◽  
Experimental Results ◽  
Influence Analysis ◽  
Satellite Attitude ◽  
Structural Error

Abstract The satellite attitude determination approach based on the Extended Kalman Filter (EKF) has been widely used in many real applications. However, the accuracy of this method largely depends on the fitness of measurement model. We aim to analyse the influence of measurement errors to the accuracy of EKF based attitude determination approach in this paper. The measurement errors, which are divided into structural error and nonstructural error by their influences, are analysed in principle. In the setting of the combination of star sensors and gyros, according to the property of innovation, we employ the technique of correlation test to analyse the influences of different kinds of measurement errors. Experimental results demonstrate the effectiveness of our previous analysis.

Comparison of Attitude Determination Methodologies for Implementation with 9DOF, Low Cost Inertial Measurement Unit for Autonomous Aerial Vehicles

2013 ◽  
Vol 3 (2) ◽  
pp. 1-15 ◽  
Author(s):  
Man Ho Choi ◽  
Robert Porter ◽  
Bijan Shirinzadeh
Keyword(s):  
Kalman Filter ◽  
Gradient Descent ◽  
Attitude Determination ◽  
Low Cost ◽  
Euler Angle ◽  
Measurement Unit ◽  
Descent Algorithm ◽  
Complementary Filter ◽  
Gradient Descent Algorithm ◽  
Reference Input

The performances of three attitude determination algorithms are compared in this paper. The three methods are the Complementary Filter, a Quaternion-based Kalman Filter and a Quaternion-based Gradient Descent Algorithm. An analysis of their performance based on an experimental investigation was undertaken. This paper shows that the Complementary Filter requires the least computational power; Quaternion-based Kalman Filter has the best noise filtering ability; and the Quaternion-based Gradient Descent Algorithm produced estimates with the highest accuracy. As many attitude determination methodologies make use of the quaternion rotation representation, the attitude quaternion to Euler angle singularity property has been investigated. Experiments conducted show that when Y-rotation approach the singularity position (±90°), the X-rotation drifts away from the reference input. This paper proposes the use of an imaginary set of sensor measurements to replace the original sensor measurements as the Y-rotation approaches the singularity. The proposed methodology for overcoming the conversion singularity has been experimentally verified.

scholarly journals Performance Enhancement of Pedestrian Navigation Systems Based on Low-Cost Foot-Mounted MEMS-IMU/Ultrasonic Sensor

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 364 ◽  
Author(s):  
Ming Xia ◽  
Chundi Xiu ◽  
Dongkai Yang ◽  
Li Wang
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Navigation System ◽  
Low Cost ◽  
Ultrasonic Sensor ◽  
Measurement Unit ◽  
Positioning Error ◽  
Process Noise ◽  
Pedestrian Navigation ◽  
Mems Imu

The pedestrian navigation system (PNS) based on inertial navigation system-extended Kalman filter-zero velocity update (INS-EKF-ZUPT or IEZ) is widely used in complex environments without external infrastructure owing to its characteristics of autonomy and continuity. IEZ, however, suffers from performance degradation caused by the dynamic change of process noise statistics and heading estimation errors. The main goal of this study is to effectively improve the accuracy and robustness of pedestrian localization based on the integration of the low-cost foot-mounted microelectromechanical system inertial measurement unit (MEMS-IMU) and ultrasonic sensor. The proposed solution has two main components: (1) the fuzzy inference system (FIS) is exploited to generate the adaptive factor for extended Kalman filter (EKF) after addressing the mismatch between statistical sample covariance of innovation and the theoretical one, and the fuzzy adaptive EKF (FAEKF) based on the MEMS-IMU/ultrasonic sensor for pedestrians was proposed. Accordingly, the adaptive factor is applied to correct process noise covariance that accurately reflects previous state estimations. (2) A straight motion heading update (SMHU) algorithm is developed to detect whether a straight walk happens and to revise errors in heading if the ultrasonic sensor detects the distance between the foot and reflection point of the wall. The experimental results show that horizontal positioning error is less than 2% of the total travelled distance (TTD) in different environments, which is the same order of positioning error compared with other works using high-end MEMS-IMU. It is concluded that the proposed approach can achieve high performance for PNS in terms of accuracy and robustness.

A Novel GNSS Attitude Determination Method Based on Primary Baseline Switching for A Multi-Antenna Platform

2020 ◽  
Vol 12 (5) ◽  
pp. 747
Author(s):  
Peng Zhang ◽  
Yinzhi Zhao ◽  
Huan Lin ◽  
Jingui Zou ◽  
Xinzhe Wang ◽  
...  
Keyword(s):  
Attitude Determination ◽  
Low Cost ◽  
Satellite System ◽  
Poor Quality ◽  
Integrated Navigation ◽  
Determination Method ◽  
Integrated Navigation System ◽  
Determination System ◽  
Vehicle Test ◽  
Global Navigation Satellite

The global navigation satellite system (GNSS)-based attitude determination system has attracted more and more attention with the advantages of having simplified algorithms, a low price and errors that do not accumulate over time. However, GNSS signals may have poor quality or lose lock in some epochs with the influence of signal fading and the multipath effect. When the direct attitude determination method is applied, the primary baseline may not be available (ambiguity is not fixed), leading to the inability of attitude determination. With the gradual popularization of low-cost receivers, making full use of spatial redundancy information of multiple antennas brings new ideas to the GNSS-based attitude determination method. In this paper, an attitude angle conversion algorithm, selecting an arbitrary baseline as the primary baseline, is derived. A multi-antenna attitude determination method based on primary baseline switching is proposed, which is performed on a self-designed embedded software and hardware platform. The proposed method can increase the valid epoch proportion and attitude information. In the land vehicle test, reference results output from a high-accuracy integrated navigation system were used to evaluate the accuracy and reliability. The results indicate that the proposed method is correct and feasible. The valid epoch proportion is increased by 16.2%, which can effectively improve the availability of attitude determination. The RMS of the heading, pitch and roll angles are 0.52°, 1.25° and 1.16°.

scholarly journals Experimentally Validated Extended Kalman Filter for UAV State Estimation Using Low-Cost Sensors

2018 ◽  
Vol 51 (15) ◽  
pp. 43-48 ◽  
Author(s):  
S.P.H. Driessen ◽  
N.H.J. Janssen ◽  
L. Wang ◽  
J.L. Palmer ◽  
H. Nijmeijer
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Extended Kalman Filter ◽  
Low Cost
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