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
2015 ◽  
Vol 2015 ◽  
pp. 1-5
Author(s):  
Shuai Chen ◽  
Runwu Zhong ◽  
Xiaohui Liu ◽  
Ahmed Alsaedi

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.


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

2012 ◽  
Vol 433-440 ◽  
pp. 2802-2807
Author(s):  
Ying Hong Han ◽  
Wan Chun Chen

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.


Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Lijun Song ◽  
Zhongxing Duan ◽  
Bo He ◽  
Zhe Li

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.


2021 ◽  
pp. 1-1
Author(s):  
Jing Cai ◽  
Jianhua Cheng ◽  
Jiaxin Liu ◽  
Zhenmin Wang ◽  
Yuehang Xu

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