Advanced GPS-based Attitude Estimation Scheme for Various IMU Failure Scenarios of Low-Cost UAV

2017 ◽  
Author(s):  
Heekwon No ◽  
Changdon Kee ◽  
Am Cho ◽  
Byungwoon Park
Keyword(s):  
Low Cost ◽  
Attitude Estimation ◽  
Estimation Scheme

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.

scholarly journals A Fast Calibration and Compensation Method for Magnetometers in Strap-Down Spinning Projectiles

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4157 ◽  
Author(s):  
Dafeng Long ◽  
Xiaoming Zhang ◽  
Xiaohui Wei ◽  
Zhongliang Luo ◽  
Jianzhong Cao
Keyword(s):  
Low Cost ◽  
Experimental System ◽  
Calibration Method ◽  
Attitude Estimation ◽  
Attitude Measurement ◽  
Angle Error ◽  
Estimation Errors ◽  
Zero Bias ◽  
Launch Site ◽  
Spinning Projectile

Attitude measurement is an essential technology in projectile trajectory correction. Magnetometers have been used for projectile attitude measurement systems as they are small in size, lightweight, and low cost. However, magnetometers are seriously disturbed by the artillery magnetic field during launch. Moreover, the error parameters of the magnetometers, which are calibrated in advance, usually change after extended storage. The changed parameters have negative effects on attitude estimation of the projectile. To improve the accuracy of attitude estimation, the magnetometers should be calibrated again before launch or during flight. This paper presents a fast calibration method specific for a spinning projectile. At the launch site, the tri-axial magnetometer is calibrated, the parameters of magnetometer are quickly obtained by optimal ellipsoid fitting based on a least squares criterion. Then, the calibration parameters are used to compensate for magnetometer outputs during flight. The numerical simulation results show that the proposed calibration method can effectively determine zero bias, scale factors, and alignment angle errors. Finally, a semi-physical experimental system was designed to further verify the performance of the calibration method. The results show that pitch angle error reduces from 3.52° to 0.58° after calibration. The roll angle error is reduced from 2.59° to 0.65°. Simulations and experimental results indicate that the accuracy of magnetometer in strap-down spinning projectile has been greatly enhanced, and the attitude estimation errors are reduced after calibration.

Attitude estimation Algorithms using low cost IMU

2015 ◽  
Vol 8 (11) ◽  
pp. 113-126 ◽  
Author(s):  
Dung Duong Quoc ◽  
Jinwei Sun ◽  
Van Nhu Le
Keyword(s):  
Low Cost ◽  
Attitude Estimation ◽  
Estimation Algorithms

Fast Complementary Filter for Attitude Estimation Using Low-Cost MARG Sensors

2016 ◽  
Vol 16 (18) ◽  
pp. 6997-7007 ◽  
Author(s):  
Jin Wu ◽  
Zebo Zhou ◽  
Jingjun Chen ◽  
Hassen Fourati ◽  
Rui Li
Keyword(s):  
Low Cost ◽  
Attitude Estimation ◽  
Complementary Filter

Least square estimation-based adaptive complimentary filter for attitude estimation

2018 ◽  
Vol 41 (1) ◽  
pp. 235-245 ◽  
Author(s):  
Parag Narkhede ◽  
Alex Noel Joseph Raj ◽  
Vipan Kumar ◽  
Vinod Karar ◽  
Shashi Poddar
Keyword(s):  
Inertial Sensors ◽  
Low Cost ◽  
Attitude Estimation ◽  
Unmanned Vehicles ◽  
Least Square ◽  
Least Square Estimation ◽  
Micro Electro Mechanical Systems ◽  
Moving Vehicle ◽  
Complementary Filter ◽  
Filter Structure

Attitude estimation is one of the core fundamentals for navigation of unmanned vehicles and other robotic systems. With the advent of low cost and low accuracy micro-electro-mechanical systems (MEMS) based inertial sensors, these devices are used ubiquitously for all such commercial grade systems that need motion information. However, these sensors suffer from time-varying bias and noise parameters, which need to be compensated during system state estimation. Complementary filtering is one of such techniques that is used here for estimating attitude of a moving vehicle. However, the complementary filter structure is dependent on user fed gain parameters, KP and KI and needs a mechanism by which they can be obtained automatically. In this paper, an attempt has been made towards addressing this issue by applying least square estimation technique on the error obtained between estimated and measured attitude angles. The proposed algorithm simplifies the design of nonlinear complementary filter by computing the filter gains automatically. The experimental investigation has been carried out over several datasets, confirming the advantage of obtaining gain parameters automatically for the complementary filtering structure.

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