scholarly journals Study on a Twice Transfer Alignment Based on Dual Model

2015 ◽  
Vol 2015 ◽  
pp. 1-5
Author(s):  
Shuai Chen ◽  
Runwu Zhong ◽  
Xiaohui Liu ◽  
Ahmed Alsaedi
Keyword(s):  
Kalman Filter ◽  
Convergence Time ◽  
Alignment Algorithm ◽  
Dual Model ◽  
Matching Method ◽  
Error Models ◽  
Transfer Alignment ◽  
Nonlinear Error ◽  
Dual Models ◽  
Rapid Transfer

This paper proposes a twice rapid transfer alignment algorithm based on dual models in order to solve the problems such as long convergence time, poor accuracy, and heavy computation burden resulting from the traditional nonlinear error models. The quaternion matching method based on quaternion error model along with the extended Kalman filter (EKF) is applied to deal with the large misalignment in the first phase. Then in the second transfer alignment phase, velocity plus attitude matching method as well as classical Kalman filter is adopted. The simulation and the results of vehicle tests demonstrate that this method combines the advantages of both nonlinear and linear error models with the guarantee of accuracy and fastness.

scholarly journals Rapid Transfer Alignment of MEMS SINS Based on Adaptive Incremental Kalman Filter

Sensors ◽  
2017 ◽  
Vol 17 (12) ◽  
pp. 152 ◽  
Author(s):  
Hairong Chu ◽  
Tingting Sun ◽  
Baiqiang Zhang ◽  
Hongwei Zhang ◽  
Yang Chen
Keyword(s):  
Kalman Filter ◽  
Transfer Alignment ◽  
Rapid Transfer

scholarly journals A Unified Nonsingular Rapid Transfer Alignment Solution for Tactical Weapon Based on Matrix Kalman Filter

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 78700-78709 ◽  
Author(s):  
Xiao Cui ◽  
Gongmin Yan ◽  
Qiangwen Fu ◽  
Qi Zhou ◽  
Zhenbo Liu
Keyword(s):  
Kalman Filter ◽  
Transfer Alignment ◽  
Rapid Transfer

Comparison of Three Rapid Transfer Alignment Algorithms

2012 ◽  
Vol 433-440 ◽  
pp. 3483-3488 ◽  
Author(s):  
Hong De Dai ◽  
Shao Lei Zhou ◽  
Qin Jiu Xu ◽  
Guang Bin Wu ◽  
Xiao Nan Wu
Keyword(s):  
Rapid Response ◽  
The Other ◽  
Alignment Algorithm ◽  
Transfer Alignment ◽  
Matching Methods ◽  
Lever Arm ◽  
Alignment Algorithms ◽  
The Times ◽  
Rapid Transfer ◽  
American Scientist

Speed of transfer alignment affects the ability of rapid response of the weapons equipped on the ship or the plane, so rapid transfer alignment has been widely researched for the last two decades. The “velocity plus attitude” matching rapid transfer alignment algorithm which is presented by the American scientist Kain, who started the times of rapid transfer alignment, then lots of researches have been done based on Kain’s work, every one has its own advantages, and of course disadvantages. Three rapid transfer alignment algorithms called “velocity plus attitude”, “velocity plus rate” and “attitude plus rate” matching rapid transfer alignment, have been compared from the aspect of accuracy and speed in this paper, while the flexure of the vehicle and the lever arm errors occurred respectively. Simulations have been done for the comparison of these three rapid transfer alignment algorithms, results show that while there is no flexure nor lever arm error, the three methods perform the same in accuracy and speed, the “velocity plus attitude” is a little slower than the other two methods in the estimation of the misalignment, the flexure influence the “velocity plus rate” and “attitude plus rate” matching methods much more than the “velocity plus attitude” method, but the latter greatly affected by the lever arm error. The results of this paper can help engineers to chose better rapid transfer alignment algorithm in their work.

Analysis of Three Velocity Plus Attitude Matching Methods for Transfer Alignment

2012 ◽  
Vol 433-440 ◽  
pp. 2802-2807
Author(s):  
Ying Hong Han ◽  
Wan Chun Chen
Keyword(s):  
Kalman Filter ◽  
Inertial Navigation ◽  
Navigation Systems ◽  
Misalignment Angle ◽  
Inertial Navigation Systems ◽  
Excellent Performance ◽  
Transfer Alignment ◽  
Matching Methods ◽  
Moving Base

For inertial navigation systems (INS) on moving base, transfer alignment is widely applied to initialize it. Three velocity plus attitude matching methods are compared. And Kalman filter is employed to evaluate the misalignment angle. Simulations under the same conditions show which scheme has excellent performance in precision and rapidness of estimations.

scholarly journals Application of Federal Kalman Filter with Neural Networks in the Velocity and Attitude Matching of Transfer Alignment

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Lijun Song ◽  
Zhongxing Duan ◽  
Bo He ◽  
Zhe Li
Keyword(s):  
Neural Networks ◽  
Kalman Filter ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Estimation Error ◽  
Inertial Navigation ◽  
Transfer Alignment ◽  
The Neural Networks ◽  
Initial Attitude ◽  
Noise Statistics

The centralized Kalman filter is always applied in the velocity and attitude matching of Transfer Alignment (TA). But the centralized Kalman has many disadvantages, such as large amount of calculation, poor real-time performance, and low reliability. In the paper, the federal Kalman filter (FKF) based on neural networks is used in the velocity and attitude matching of TA, the Kalman filter is adjusted by the neural networks in the two subfilters, the federal filter is used to fuse the information of the two subfilters, and the global suboptimal state estimation is obtained. The result of simulation shows that the federal Kalman filter based on neural networks is better in estimating the initial attitude misalignment angle of inertial navigation system (INS) when the system dynamic model and noise statistics characteristics of inertial navigation system are unclear, and the estimation error is smaller and the accuracy is higher.

scholarly journals Vibration Error Correction for the FOGs-Based Measurement in a Drilling System Using an Extended Kalman Filter

2021 ◽  
Vol 11 (14) ◽  
pp. 6514
Author(s):  
Lu Wang ◽  
Yuanbiao Hu ◽  
Tao Wang ◽  
Baolin Liu
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Measurement Accuracy ◽  
Original Data ◽  
Calibration Method ◽  
Error Model ◽  
Nonlinear Error ◽  
Measurement While Drilling ◽  
Drilling System ◽  
Vibration Error

Fiber-optic gyroscopes (FOGs)-based Measurement While Drilling system (MWD) is a newly developed instrument to survey the borehole trajectory continuously and in real time. However, because of the strong vibration while drilling, the measurement accuracy of FOG-based MWD deteriorates. It is urgent to improve the measurement accuracy while drilling. Therefore, this paper proposes an innovative scheme for the vibration error of the FOG-based MWD. Firstly, the nonlinear error models for the FOGs and ACCs are established. Secondly, a 36-order Extended Kalman Filter (EKF) combined with a calibration method based on 24-position is designed to identify the coefficients in the error model. Moreover, in order to obtain a higher accurate error model, an iterative calibration method has been suggested to suppress calibration residuals. Finally, vibration experiments simulating the drilling vibration in the laboratory is implemented. Compared to the original data, compensated the linear error items, the error of 3D borehole trajectory can only be reduced by a ratio from 10% to 34%. While compensating for the nonlinear error items of the FOG-based MWD, the error of 3D borehole trajectory can be reduced by a ratio from 44.13% to 97.22%. In conclusion, compensation of the nonlinear error of FOG-based MWD could improve the trajectory survey accuracy under vibration.

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