An innovative transfer alignment method based on federated filter for airborne distributed POS

Measurement ◽  
2016 ◽  
Vol 86 ◽  
pp. 165-181 ◽  
Author(s):  
Xiaolin Gong ◽  
Jianxu Zhang
2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Xixiang Liu ◽  
Xiaosu Xu ◽  
Yiting Liu ◽  
Lihui Wang

Two viewpoints are given: (1) initial alignment of strapdown inertial navigation system (SINS) can be fulfilled with a set of inertial sensor data; (2) estimation time for sensor errors can be shortened by repeated data fusion on the added backward-forward SINS resolution results and the external reference data. Based on the above viewpoints, aiming to estimate gyro bias in a shortened time, a rapid transfer alignment method, without any changes for Kalman filter, is introduced. In this method, inertial sensor data and reference data in one reference data update cycle are stored, and one backward and one forward SINS resolutions are executed. Meanwhile, data fusion is executed when the corresponding resolution ends. With the added backward-forward SINS resolution, in the above mentioned update cycle, the estimating operations for gyro bias are added twice, and the estimation time for it is shortened. In the ship swinging condition, with the “velocity plus yaw” matching, the effectiveness of this method is proved by the simulation.


Measurement ◽  
2021 ◽  
Vol 176 ◽  
pp. 109234
Author(s):  
Jiazhen Lu ◽  
Lili Ye ◽  
Wei Luo ◽  
Jing Dong ◽  
Songlai Han

Optik ◽  
2020 ◽  
Vol 217 ◽  
pp. 164912 ◽  
Author(s):  
Weina Chen ◽  
Zhong Yang ◽  
Shanshan Gu ◽  
Yujuan Tang ◽  
Yizhi Wang

2013 ◽  
Vol 411-414 ◽  
pp. 907-911
Author(s):  
She Sheng Gao ◽  
Yan Zhao ◽  
Wen Hui Wei

This paper presents a fuzzy anti-interference transfer alignment method for airborne strapdown inertial navigation system (SINS). This fuzzy anti-interference transfer alignment method takes the influence of systematic error into account for SINS transfer alignment. The fuzzy rules are constructed and incorporated in the filtering process to estimate the covariance matrices of observation vector and predicted state vector with random weight method. The experimental results demonstrate that the proposed method can resist the interferences caused by the airborne maneuvering process, thus improving the accuracy for SINS transfer alignment.


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