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Xiang Xu   Dr.  Graduate Student or Post Graduate 
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Xiang Xu published an article in August 2017.
Top co-authors
Yao Li

5 shared publications

Key Laboratory of Micro-Inertial Instrument and Advanced Navigation Technology, Ministry of Education, Southeast University, Nanjing 210096, China;;; School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China

Tao Zhang

2 shared publications

Jinwu Tong

1 shared publications

2
Publications
3
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0
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11
Citations
Publication Record
Distribution of Articles published per year 
(2017)
Total number of journals
published in
 
2
 
Publications
Article 3 Reads 0 Citations Spacecraft attitude estimation based on matrix Kalman filter and recursive cubature Kalman filter Tao Zhang, Xiang Xu, Zhicheng Wang Published: 08 August 2017
Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, doi: 10.1177/0954410017723359
DOI See at publisher website
Article 0 Reads 11 Citations A Kalman Filter for SINS Self-Alignment Based on Vector Observation Xiang Xu, Xiaosu Xu, Tao Zhang, Yao Li, Jinwu Tong Published: 29 January 2017
Sensors, doi: 10.3390/s17020264
DOI See at publisher website ABS Show/hide abstract
In this paper, a self-alignment method for strapdown inertial navigation systems based on the q-method is studied. In addition, an improved method based on integrating gravitational apparent motion to form apparent velocity is designed, which can reduce the random noises of the observation vectors. For further analysis, a novel self-alignment method using a Kalman filter based on adaptive filter technology is proposed, which transforms the self-alignment procedure into an attitude estimation using the observation vectors. In the proposed method, a linear psuedo-measurement equation is adopted by employing the transfer method between the quaternion and the observation vectors. Analysis and simulation indicate that the accuracy of the self-alignment is improved. Meanwhile, to improve the convergence rate of the proposed method, a new method based on parameter recognition and a reconstruction algorithm for apparent gravitation is devised, which can reduce the influence of the random noises of the observation vectors. Simulations and turntable tests are carried out, and the results indicate that the proposed method can acquire sound alignment results with lower standard variances, and can obtain higher alignment accuracy and a faster convergence rate.
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