Re-fusion of Inertial Attitude Measurement Sensors Based on Phase-lag-free LPF and Kalman Filter

2021 ◽  
Author(s):  
Yawen Kong ◽  
Dapeng Tian ◽  
Fuchao Wang ◽  
Meiyu Wang ◽  
Jihong Xiu
Keyword(s):  
Kalman Filter ◽  
Phase Lag ◽  
Attitude Measurement

Using MEMS Accelerometers to Improve Automobile Handwheel State Estimation for Force Feedback

2004 ◽  
Author(s):  
Joshua P. Switkes ◽  
Ian A. Coe ◽  
J. Christian Gerdes
Keyword(s):  
Kalman Filter ◽  
Force Feedback ◽  
Low Frequency ◽  
Phase Lag ◽  
Position Sensor ◽  
Noisy Signal ◽  
Low Resolution ◽  
Moving Vehicle ◽  
Mems Accelerometers ◽  
Smooth Acceleration

This paper explores the possibility of combining solid state accelerometers with a low resolution position sensor to provide clean estimates of automobile handwheel position, velocity and acceleration for use in force feedback. Typically determining the acceleration and velocity of the handwheel requires differentiating a position sensor such as an encoder or potentiometer. Unless expensive high-resolution sensors are used, this differentiation leads to a noisy signal, requiring significant filtering which leads to significant phase lag. With a direct measurement of acceleration, we circumvent many of the problems associated with differentiation and filtering. This work uses a Kalman filter to combine a pair of MEMS accelerometers with a low-resolution potentiometer to estimate handwheel states. This measurement scheme is effective in this application because of the low frequency nature of the force feedback and because of the structural robustness of the handwheel system. Initial in-vehicle experimental results show the setup can provide smooth acceleration and velocity signals in a moving vehicle.

Research on Non-Gyro Projectile’s Attitude Measurement Based on Improved Unscented Kalman Filter

2011 ◽  
Vol 480-481 ◽  
pp. 1111-1116
Author(s):  
Zhao Hua Liu ◽  
Jia Bin Chen ◽  
Yong Wang ◽  
Chun Lei Song ◽  
Jun Wang ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Low Cost ◽  
State Equation ◽  
Measurement Unit ◽  
Attitude Measurement ◽  
Good Precision ◽  
Measurement Equation ◽  
Novel Method ◽  
Linear State

Considering the control requirements of guided projectile, a novel method is studied that using three low-cost MEMS accelerometers as inertial measurement unit to construct state equation and measurement equation of system, using improved unscented Kalman filter (IUKF) to estimate the state of system. For the system characteristic of linear state equation and nonlinear measurement equation, the improved UKF nonlinear filter algorithm which combines KF and UKF was proposed. At the same time, utilizing minimal skew simplex sampling to reduce the number of sigma points, computational efficiency is enhanced. The simulation experimental results show that using IUKF algorithm can obtains good precision of estimation.

scholarly journals Multi-Sensor Combined Measurement While Drilling Based on the Improved Adaptive Fading Square Root Unscented Kalman Filter

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 1897
Author(s):  
Yi Yang ◽  
Fei Li ◽  
Yi Gao ◽  
Yanhui Mao
Keyword(s):  
Kalman Filter ◽  
Covariance Matrix ◽  
Unscented Kalman Filter ◽  
Computational Method ◽  
Attitude Measurement ◽  
Square Root ◽  
Drilling Tool ◽  
Measurement While Drilling ◽  
Fading Factor ◽  
Filter Divergence

In the process of the attitude measurement for a steering drilling system, the measurement of the attitude parameters may be uncertain and unpredictable due to the influence of server vibration on bits. In order to eliminate the interference caused by vibration on the measurement and quickly obtain the accurate attitude parameters of the steering drilling tool, a new method for multi-sensor dynamic attitude combined measurement is presented. Firstly, by using a triaxial accelerometer and triaxial magnetometer measurement system, the nonlinear model based on the quaternion is established. Then, an improved adaptive fading square root unscented Kalman filter is proposed for eliminating the vibration disturbance signal. In this algorithm, the square root of the state covariance matrix is used to replace the covariance matrix in the classical unscented Kalman filter (UKF) to avoid the filter divergence caused by the negative definite state covariance matrix. The fading factor is introduced into UKF to adjust the filter gain in real-time and improve the adaptive ability of the algorithm to mutation state. Finally, the computational method of the fading factor is optimized to ensure the self-adaptability of the algorithm and reduce the computational complexity. The results of the laboratory test and the field-drilling data show that the proposed method can filter out the interference noise in the attitude measurement sensor effectively, improve the solution accuracy of attitude parameters of drilling tools in the case of abrupt changes in the measuring environment, and thus ensuring the dynamic stability of the well trajectory.

scholarly journals Square-Root Cubature Kalman Filter Based on H∞ Filter for Attitude Measurement of High-Spinning Aircraft

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Ping-an Zhang ◽  
Wei Wang ◽  
Min Gao ◽  
Yi Wang
Keyword(s):  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Euler Angle ◽  
Positive Semidefinite ◽  
Measurement Model ◽  
The State ◽  
Measurement Noise ◽  
Attitude Measurement ◽  
Square Root ◽  
Cubature Kalman Filter

A novel H∞ filter called square-root cubature H∞ Kalman filter is proposed for attitude measurement of high-spinning aircraft. In this method, a combined measurement model of three-axis geomagnetic sensor and gyroscope is used, and the Euler angle algorithm model is used to reduce the state dimension and linearize the state equation, which can reduce the amount of calculation. Simultaneously, the method can be applied to the case of measurement noise uncertainty. By continuously modifying the error limiting parameters to update the measurement noise estimation, the filtering accuracy and robustness can be improved. The square-root forms enjoy a consistently improved numerical stability because all the resulting covariance matrices by QR decomposition are guaranteed to stay positive semidefinite. The algorithm is applied to the simulation experiment of attitude measurement with the combination of geomagnetic sensor and gyroscope and compared with the results of Unscented Kalman filter, cubature Kalman filter, square root cubature Kalman filter, and singular value decomposition cubature Kalman filter, which proves the effectiveness and superiority of the algorithm.

Error Correction Capability of GPS/INS Integrated Navigation System for Guided Rockets

2013 ◽  
Vol 325-326 ◽  
pp. 1053-1057
Author(s):  
Wei Wei Bian ◽  
Liang Ming Wang ◽  
Chuan Bing Ding ◽  
Yang Zhong
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Measurement Information ◽  
Attitude Measurement ◽  
Integrated Navigation ◽  
Integrated Navigation System ◽  
Error Correction Capability ◽  
Trajectory Simulation ◽  
Simulation Results ◽  
Navigation Method

In order to improve the guidance accuracy of long-range rockets, a GPS/INS integrated navigation method with combination of position, velocity and attitude was applied. The GPS/INS integrated navigation system taking the position and velocity from INS and attitude from GPS as observables was studied. The error model of system was established and the Kalman filter was designed. A 6-DOF trajectory simulation was put forward and the correction capability of the INS measurement error by using GPS attitude measurement information was analyzed. The simulation results verify the feasibility and effectiveness of the integrated navigation method.

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