The Comparison of Attitude Estimation Method Based on Kalman Filter with Considering External Acceleration and Bias Effect

2018 ◽  
Vol 35 (8) ◽  
pp. 745-752
Author(s):  
Taeho Jang ◽  
Yuongshik Kim ◽  
Taesoo Jang
Keyword(s):  
Kalman Filter ◽  
Estimation Method ◽  
Attitude Estimation ◽  
Bias Effect ◽  
External Acceleration

scholarly journals Attitude Estimation Using Kalman Filtering: External Acceleration Compensation Considerations

2016 ◽  
Vol 2016 ◽  
pp. 1-24 ◽  
Author(s):  
Romy Budhi Widodo ◽  
Chikamune Wada
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Attitude Estimation ◽  
Estimation Accuracy ◽  
Compensation Model ◽  
Measurement Noise Covariance ◽  
Noise Covariance ◽  
The One ◽  
The Right ◽  
External Acceleration

Attitude estimation is often inaccurate during highly dynamic motion due to the external acceleration. This paper proposes extended Kalman filter-based attitude estimation using a new algorithm to overcome the external acceleration. This algorithm is based on an external acceleration compensation model to be used as a modifying parameter in adjusting the measurement noise covariance matrix of the extended Kalman filter. The experiment was conducted to verify the estimation accuracy, that is, one-axis and multiple axes sensor movement. Five approaches were used to test the estimation of the attitude: (1) the KF-based model without compensating for external acceleration, (2) the proposed KF-based model which employs the external acceleration compensation model, (3) the two-step KF using weighted-based switching approach, (4) the KF-based model which uses thethreshold-basedapproach, and (5) the KF-based model which uses the threshold-based approach combined with a softened part approach. The proposed algorithm showed high effectiveness during the one-axis test. When the testing conditions employed multiple axes, the estimation accuracy increased using the proposed approach and exhibited external acceleration rejection at the right timing. The proposed algorithm has fewer parameters that need to be set at the expense of the sharpness of signal edge transition.

scholarly journals Linear Kalman Filter for Attitude Estimation from Angular Rate and a Single Vector Measurement

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Shangqiu Shan ◽  
Zhongxi Hou ◽  
Jin Wu
Keyword(s):  
Kalman Filter ◽  
Attitude Determination ◽  
Angular Rate ◽  
Attitude Estimation ◽  
Estimation Accuracy ◽  
Kinematic Equation ◽  
Vector Measurement ◽  
Vector Observation ◽  
Execution Speed ◽  
External Acceleration

In this paper, a new Kalman filtering scheme is designed in order to give the optimal attitude estimation with gyroscopic data and a single vector observation. The quaternion kinematic equation is adopted as the state model while the quaternion of the attitude determination from a strapdown sensor is treated as the measurement. Derivations of the attitude solution from a single vector observation along with its variance analysis are presented. The proposed filter is named as the Single Vector Observation Linear Kalman filter (SVO-LKF). Flexible design of the filter facilitates fast execution speed with respect to other filters with linearization. Simulations and experiments are conducted in the presence of large external acceleration and magnetic distortion. The results show that, compared with representative filtering methods and attitude observers, the SVO-LKF owns the best estimation accuracy and it consumes much less time in the fusion process.

Improvement of Attitude Estimation Using Modified Rodrigues Parameters and Intelligent Controller

2014 ◽  
Vol 556-562 ◽  
pp. 2279-2284
Author(s):  
Qiang Liu ◽  
Fu Yin Gao ◽  
Chang Gang Wang ◽  
Yu Han
Keyword(s):  
Kalman Filter ◽  
Computational Efficiency ◽  
Fuzzy Controller ◽  
Inertial Sensors ◽  
Estimation Method ◽  
Attitude Estimation ◽  
State Variables ◽  
Filter Model ◽  
Modified Rodrigues Parameters ◽  
Filter Tuning

The attitude estimation method of attitude heading reference system (AHRS) using an Extended Kalman Filter (EKF) with a filter tuning algorithm based on fuzzy controller is introduced.The AHRS uses inertial sensors and magnetometers to calculate its attitude. It is known that the attitude update using gyros are prone to diverge and hence the attitude error needs to compensate using accelerometers and magnetometers. In this paper, a Kalman filter model with a state variables represented by Modified Rodrigues Parameters (MRP) is presented to improve the computational efficiency and a model changing algorithm is used to make the filter more robust to acceleration and magnetic disturbances.If the AHRS measures any disturbances which are caused by movement of the vehicle, using fuzzy controller changes the filter gain .Simulation results show, EKF tuned by fuzzy controller is correct method that makes robust to disturbances more properly ,Rodrigues parameters can improve the computational efficiency..

scholarly journals HIGH-PRECISION ATTITUDE ESTIMATION METHOD OF STAR SENSORS AND GYRO BASED ON COMPLEMENTARY FILTER AND UNSCENTED KALMAN FILTER

2017 ◽  
Vol XLII-3/W1 ◽  
pp. 49-53 ◽  
Author(s):  
C. Guo ◽  
X. Tong ◽  
S. Liu ◽  
S. Liu ◽  
X. Lu ◽  
...  
Keyword(s):  
Kalman Filter ◽  
High Frequency ◽  
Attitude Determination ◽  
Unscented Kalman Filter ◽  
Estimation Method ◽  
Attitude Estimation ◽  
Star Tracker ◽  
Attitude Motion ◽  
Complementary Filter ◽  
Gyro Drift

Determining the attitude of satellite at the time of imaging then establishing the mathematical relationship between image points and ground points is essential in high-resolution remote sensing image mapping. Star tracker is insensitive to the high frequency attitude variation due to the measure noise and satellite jitter, but the low frequency attitude motion can be determined with high accuracy. Gyro, as a short-term reference to the satellite’s attitude, is sensitive to high frequency attitude change, but due to the existence of gyro drift and integral error, the attitude determination error increases with time. Based on the opposite noise frequency characteristics of two kinds of attitude sensors, this paper proposes an on-orbit attitude estimation method of star sensors and gyro based on Complementary Filter (CF) and Unscented Kalman Filter (UKF). In this study, the principle and implementation of the proposed method are described. First, gyro attitude quaternions are acquired based on the attitude kinematics equation. An attitude information fusion method is then introduced, which applies high-pass filtering and low-pass filtering to the gyro and star tracker, respectively. Second, the attitude fusion data based on CF are introduced as the observed values of UKF system in the process of measurement updating. The accuracy and effectiveness of the method are validated based on the simulated sensors attitude data. The obtained results indicate that the proposed method can suppress the gyro drift and measure noise of attitude sensors, improving the accuracy of the attitude determination significantly, comparing with the simulated on-orbit attitude and the attitude estimation results of the UKF defined by the same simulation parameters.

Spacecraft attitude estimation based on matrix Kalman filter and recursive cubature Kalman filter

2017 ◽  
Vol 232 (16) ◽  
pp. 3024-3033
Author(s):  
Tao Zhang ◽  
Xiang Xu ◽  
Zhicheng Wang
Keyword(s):  
Kalman Filter ◽  
Recursive Algorithm ◽  
Estimation Method ◽  
Attitude Estimation ◽  
Previous Method ◽  
Spacecraft Attitude ◽  
Original Matrix ◽  
Cubature Kalman Filter ◽  
The Matrix ◽  
Gyro Drift

An interlaced matrix Kalman filter, which is based on vector observations and gyro measurements, is proposed for spacecraft attitude estimation in this paper. It combines the matrix Kalman filter and cubature Kalman filter to estimate spacecraft attitude and gyro drift bias, respectively. The defects of the original matrix Kalman filter, which could only estimate the attitude parameters of spacecraft, are addressed by the proposed interlaced matrix Kalman filter. In addition, the dimensions of cubature Kalman filter for conventional attitude estimation method are reduced by the designed recursive algorithm. It is noted that the two filters are not independent with each other. Firstly, the attitude quaternion of spacecraft is estimated by the modified matrix Kalman filter. Then, the estimated quaternion is input for the recursive cubature Kalman filter, which is used to estimate the gyro drift bias. Finally, the estimated gyro drift bias is compensated for the measurements of the gyros. Therefore, the precision of the estimated attitude of spacecraft is improved by the interacting process of the modified matrix Kalman filter and recursive cubature Kalman filter. A simulation test is designed to verify the advantage of the proposed method by comparing with the previous method, and the results indicate that the proposed algorithm has better performance on convergence rate and stability.

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 An Initial Value Estimation Method for the Kalman and Extended Kalman Filters in Underground Metal Detection

2019 ◽  
Vol 9 (19) ◽  
pp. 4113 ◽  
Author(s):  
Yadong Wan ◽  
Zhen Wang ◽  
Peng Wang ◽  
Zhiyang Liu ◽  
Na Li ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Estimation Method ◽  
Detection Methods ◽  
Polarizability Tensor ◽  
Filter Method ◽  
Excellent Performance ◽  
Initial Value ◽  
Initial State ◽  
Metal Detection ◽  
Value Estimation

As an underground metal detection technology, the electromagnetic induction (EMI) method is widely used in many cases. Therefore, the EMI detection algorithms with excellent performance are worth studying. One of the EMI detection methods in the underground metal detection is the filter method, which first obtains the secondary magnetic field data and then uses the Kalman filter (KF) and the extended Kalman filter (EKF) to estimate the parameters of metal targets. However, the traditional KF methods used in the underground metal detection have an unsatisfactory performance of the convergence as the algorithms are given a random or a fixed initial value. Here, an initial state estimation algorithm for the underground metal detection is proposed. The initial state of the target’s horizontal position is estimated by the first order central moments of the secondary field strength map. In addition, the initial state of the target’s depth is estimated by the full width at half maximum (FWHM) method. In addition, the initial state of the magnetic polarizability tensor is estimated by the least squares method. Then, these initial states are used as the initial values for KF and EKF. Finally, the position, posture and polarizability of the target are recursively calculated. A simulation platform for the underground metal detection is built in this paper. The simulation results show that the initial value estimation method proposed for the filtering algorithm has an excellent performance in the underground metal detection.

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