A Novel Attitude Estimation Algorithm Based on EKF-LSTM Fusion Model

2021 ◽  
pp. 94-104
Author(s):  
Yufan Zhuo ◽  
Fuchun Sun ◽  
Zhenkun Wen ◽  
Huisi Wu ◽  
Haiming Huang
Keyword(s):  
Estimation Algorithm ◽  
Attitude Estimation ◽  
Fusion Model

Reliable attitude estimation algorithm considering atypical observation

2021 ◽  
Vol 65 (10) ◽  
Author(s):  
Xiao-Ting Guo ◽  
Chong Shen ◽  
Jun Tang ◽  
Jie Li ◽  
Jun Liu
Keyword(s):  
Estimation Algorithm ◽  
Attitude Estimation

High-precision roll attitude estimation of decoupled canards relative to the projectile body using bipolar hall-effect sensors

2021 ◽  
pp. 1-9
Author(s):  
Tingting Yin ◽  
Zhong Yang ◽  
Youlong Wu ◽  
Fangxiu Jia
Keyword(s):  
Hall Effect ◽  
Real Time ◽  
High Precision ◽  
Solution Method ◽  
Estimation Method ◽  
Estimation Algorithm ◽  
Attitude Estimation ◽  
Fuzzy Algorithms ◽  
Positioning Method ◽  
Hall Effect Sensors

The high-precision roll attitude estimation of the decoupled canards relative to the projectile body based on the bipolar hall-effect sensors is proposed. Firstly, the basis engineering positioning method based on the edge detection is introduced. Secondly, the simplified dynamic relative roll model is established where the feature parameters are identified by fuzzy algorithms, while the high-precision real-time relative roll attitude estimation algorithm is proposed. Finally, the trajectory simulations and grounded experiments have been conducted to evaluate the advantages of the proposed method. The positioning error is compared with the engineering solution method, and it is proved that the proposed estimation method has the advantages of the high accuracy and good real-time performance.

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.

Highly Efficient Attitude-Estimation Algorithm for Star Trackers Using Optimal Image Matching

2013 ◽  
Vol 36 (6) ◽  
pp. 1672-1680 ◽  
Author(s):  
Tjorven Delabie ◽  
Joris De Schutter ◽  
Bart Vandenbussche
Keyword(s):  
Image Matching ◽  
Estimation Algorithm ◽  
Attitude Estimation ◽  
Highly Efficient

scholarly journals Evaluation of Murrell’s EKF-Based Attitude Estimation Algorithm for Exploiting Multiple Attitude Sensor Configurations

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6450
Author(s):  
Sharanabasaweshwara Asundi ◽  
Norman Fitz-Coy ◽  
Haniph Latchman
Keyword(s):  
Magnetic Field ◽  
Time Derivative ◽  
Estimation Algorithm ◽  
Field Vector ◽  
Attitude Estimation ◽  
Magnetic Field Vector ◽  
Sun Sensor ◽  
Satellite Attitude ◽  
Multiple Sensor ◽  
Sensor Suite

Pico- and nano-satellites, due to their form factor and size, are limited in accommodating multiple or redundant attitude sensors. For such satellites, Murrell’s implementation of the extended Kalman filter (EKF) can be exploited to accommodate multiple sensor configurations from a set of non redundant attitude sensors. The paper describes such an implementation involving a sun sensor suite and a magnetometer as attitude sensors. The implementation exploits Murrell’s EKF to enable three sensor configurations, which can be operationally commanded, for satellite attitude estimation. Among the three attitude estimation schemes, (i) sun sensor suite and magnetometer, (ii) magnetic field vector and its time derivative and (iii) magnetic field vector, it is shown that the third configuration is better suited for attitude estimation in terms of precision and accuracy, but can consume more time to converge than the other two.

Attitude Estimation Algorithm of Optronics Mast Systems (OMS) Based on Fuzzy Weighted Method

2010 ◽  
Vol 139-141 ◽  
pp. 2266-2270
Author(s):  
Xiao Yao Zhou ◽  
Zhi Yong Zhang ◽  
Lian Chao Zhang ◽  
Da Peng Fan
Keyword(s):  
Attitude Determination ◽  
State Space Model ◽  
Mathematic Model ◽  
Estimation Algorithm ◽  
Attitude Estimation ◽  
Body Motion ◽  
Continuum State ◽  
Reasoning System ◽  
Value Determination

A fuzzy weighted attitude estimation algorithm is proposed to settle the problem of attitude determination for Optronics Mast Systems (OMS) by combining gyros and inclinometers. Firstly, Euler representation is employed to descript attitude kinematics, and then nonlinear continuum state space model is derived, based on analysis of mathematic model of gyros and inclinometer. Secondly, EKF is used to linear and discrete the models, and a weighted measurement update equation of attitude and gyro drifts is developed under kalman Filter framework. Lastly, according to the relations between the weighted value and body motion, a weighted value determination method is proposed based on fuzzy reasoning system. The experience results show that the accuracy of attitude estimation algorithm is 0.0380. The algorithm also solves the problem of vibration disturber of inclinometer and measure error of gyros in long term.

scholarly journals Single axis FOG aided attitude estimation algorithm for mobile robots

Mechatronics ◽  
2015 ◽  
Vol 30 ◽  
pp. 158-173 ◽  
Author(s):  
B. Allotta ◽  
R. Costanzi ◽  
F. Fanelli ◽  
N. Monni ◽  
A. Ridolfi
Keyword(s):  
Mobile Robots ◽  
Estimation Algorithm ◽  
Attitude Estimation

scholarly journals A Fast Weakly-Coupled Double-Layer ESKF Attitude Estimation Algorithm and Application

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1465
Author(s):  
Yue Yang ◽  
Xiaoxiong Liu ◽  
Weiguo Zhang ◽  
Xuhang Liu ◽  
Yicong Guo
Keyword(s):  
Real Time ◽  
Double Layer ◽  
Short Range ◽  
Estimation Algorithm ◽  
Attitude Estimation ◽  
Sensor Data ◽  
Test Environment ◽  
Attitude Error ◽  
Weakly Coupled ◽  
Solution Accuracy

Aimed at the problem of small unmanned aerial vehicle (UAV) attitude solution accuracy and real-time performance in short-range navigation flight, in this paper, we propose a fast weakly-coupled double-layer error-state Kalman filter (DL-ESKF) attitude estimation algorithm. Considering the application of short-range navigation, we designed an improved attitude error model for low-cost gyroscope/accelerometer/magnetometer devices. In addition, we reasonably simplified certain factors that affect the attitude solution to reduce the filtering calculation burden. For the data coupling phenomenon caused by the different sampling frequencies of the attitude sensor data in the filtering process, we designed a new attitude algorithm combined with the ESKF and hierarchical filter. The first layer of filters used an accelerometer and the second layer used a magnetometer to correct the attitude error. We also built an offline and real-time test platform to verify the performance of the proposed algorithm in a simulation and flight test environment compared with the classic attitude algorithms. The experimental results demonstrated that the proposed algorithm not only improved the attitude solution accuracy and stability but also reduced the filter running time.

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