An improved Kalman filter for attitude determination of multi-rotor UAVs based on low-cost MEMS sensors

2016 ◽  
Author(s):  
Xiang Zhang ◽  
Zhong Yang ◽  
Tianyi Zhang ◽  
Yangyang Shen
Keyword(s):  
Kalman Filter ◽  
Attitude Determination ◽  
Low Cost ◽  
Mems Sensors

scholarly journals Attitude Determination Method by Fusing Single Antenna GPS and Low Cost MEMS Sensors Using Intelligent Kalman Filter Algorithm

2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Lei Wang ◽  
Bo Song ◽  
Xueshuai Han ◽  
Yongping Hao
Keyword(s):  
Kalman Filter ◽  
Attitude Determination ◽  
Fuzzy Controller ◽  
Low Cost ◽  
Transition Process ◽  
Measurement Unit ◽  
Accurate Estimation ◽  
Mems Sensors ◽  
Fusion Algorithm ◽  
Single Antenna

For meeting the demands of cost and size for micronavigation system, a combined attitude determination approach with sensor fusion algorithm and intelligent Kalman filter (IKF) on low cost Micro-Electro-Mechanical System (MEMS) gyroscope, accelerometer, and magnetometer and single antenna Global Positioning System (GPS) is proposed. The effective calibration method is performed to compensate the effect of errors in low cost MEMS Inertial Measurement Unit (IMU). The different control strategies fusing the MEMS multisensors are designed. The yaw angle fusing gyroscope, accelerometer, and magnetometer algorithm is estimated accurately under GPS failure and unavailable sideslip situations. For resolving robust control and characters of the uncertain noise statistics influence, the high gain scale of IKF is adjusted by fuzzy controller in the transition process and steady state to achieve faster convergence and accurate estimation. The experiments comparing different MEMS sensors and fusion algorithms are implemented to verify the validity of the proposed approach.

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.

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.

Research on Data Fusion Technology of Body Posture Detection Based on Kalman Filter

2014 ◽  
Vol 668-669 ◽  
pp. 1003-1006 ◽  
Author(s):  
Xian Wei Wang ◽  
Fu Cheng Cao
Keyword(s):  
Kalman Filter ◽  
Attitude Determination ◽  
Inertial Sensors ◽  
Low Cost ◽  
Micro Electro Mechanical System ◽  
Angle Measurement ◽  
The Body ◽  
Body Posture ◽  
Posture Estimation ◽  
The Impact

This paper discusses the body posture detection problem using low cost Micro-Electro-Mechanical System (MEMS) inertial sensors, for which a complementary sensor fusion solution is proposed. Considering the impact from the noise and bias drifts, through Kalman filter to complete the multi-sensor information fusion, achieved an accurate attitude determination. The experimental results show that, after using Kalman filtering algorithm to fuse acceleration sensor and signal gyroscope, it can effectively eliminate the accumulative error and significantly better dynamic characteristics of attitude angle measurement, Improving the reliability and accuracy of body posture estimation.

scholarly journals Adaptive UAV Attitude Estimation Employing Unscented Kalman Filter, FOAM and Low-Cost MEMS Sensors

Sensors ◽  
2012 ◽  
Vol 12 (7) ◽  
pp. 9566-9585 ◽  
Author(s):  
Héctor García de Marina ◽  
Felipe Espinosa ◽  
Carlos Santos
Keyword(s):  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Low Cost ◽  
Attitude Estimation ◽  
Mems Sensors

scholarly journals A Dual-Linear Kalman Filter for Real-Time Orientation Determination System Using Low-Cost MEMS Sensors

Sensors ◽  
2016 ◽  
Vol 16 (2) ◽  
pp. 264 ◽  
Author(s):  
Shengzhi Zhang ◽  
Shuai Yu ◽  
Chaojun Liu ◽  
Xuebing Yuan ◽  
Sheng Liu
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Low Cost ◽  
Time Orientation ◽  
Mems Sensors ◽  
Determination System ◽  
Orientation Determination

Multiple model Kalman filter for attitude determination of precision pointing spacecraft

2011 ◽  
Vol 68 (7-8) ◽  
pp. 843-852 ◽  
Author(s):  
Kai Xiong ◽  
Tang Liang ◽  
Lei Yongjun
Keyword(s):  
Kalman Filter ◽  
Attitude Determination ◽  
Multiple Model
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