A novel gyrocompass alignment method under large azimuth misalignment angle

2015 ◽  
Vol 20 (5) ◽  
pp. 558-564
Author(s):  
Feng-mei Wei ◽  
Jian-pei Zhang ◽  
Jing Yang ◽  
Shu-qiang Jiang ◽  
Song-lin Xie
2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Yong-Gang Zhang ◽  
Yu-Long Huang ◽  
Zhe-Min Wu ◽  
Ning Li

A new moving state marine initial alignment method of strap-down inertial navigation system (SINS) is proposed based on high-degree cubature Kalman filter (CKF), which can capture higher order Taylor expansion terms of nonlinear alignment model than the existing third-degree CKF, unscented Kalman filter and central difference Kalman filter, and improve the accuracy of initial alignment under large heading misalignment angle condition. Simulation results show the efficiency and advantage of the proposed initial alignment method as compared with existing initial alignment methods for the moving state SINS initial alignment with large heading misalignment angle.


Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3896 ◽  
Author(s):  
Kang Gao ◽  
Shunqing Ren ◽  
Guoxing Yi ◽  
Jiapeng Zhong ◽  
Zhenhuan Wang

For a land-vehicle strapdown inertial navigation system (SINS), the problem of initial alignment with large misalignment angle in-motion needs to be solved urgently. This paper proposes an improved ACKF/KF initial alignment method for SINS aided by odometer. The SINS error equation with large misalignment angle is established first in the form of an Euler angle. The odometer/gyroscope dead reckoning (DR) error equation is deduced, which makes the observation equation linear when the position is taken as the observation of the Kalman filter. Then, based on the cubature Kalman filter, the Sage-Husa adaptive filter and the characteristics of the observation equation, an improved ACKF/KF method is proposed, which can accomplish initial alignment well in the case of unknown measurement noise. Computer simulation results show that the performance of the proposed ACKF/KF algorithm is superior to EKF, CKF and AEKF method in accuracy and stability, and the vehicle test validates its advantages.


2021 ◽  
Vol 1846 (1) ◽  
pp. 012075
Author(s):  
Chen Yang ◽  
Yuanwen Cai ◽  
Chaojun Xin ◽  
Meiling Shi

Measurement ◽  
2021 ◽  
pp. 109250
Author(s):  
Hongliang Zhang ◽  
Yilan Zhou ◽  
Tengchao Huang ◽  
Lei Wang

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Nae-Chyun Chen ◽  
Brad Solomon ◽  
Taher Mun ◽  
Sheila Iyer ◽  
Ben Langmead

AbstractMost sequencing data analyses start by aligning sequencing reads to a linear reference genome, but failure to account for genetic variation leads to reference bias and confounding of results downstream. Other approaches replace the linear reference with structures like graphs that can include genetic variation, incurring major computational overhead. We propose the reference flow alignment method that uses multiple population reference genomes to improve alignment accuracy and reduce reference bias. Compared to the graph aligner vg, reference flow achieves a similar level of accuracy and bias avoidance but with 14% of the memory footprint and 5.5 times the speed.


Sign in / Sign up

Export Citation Format

Share Document