基于信息复用和算法融合的初始对准方法

doi:10.12305/j.issn.1001-506X.2021.05.19

Abstract

Abstract:

Through theoretical analysis of the error characteristics of the multi-vector attitude determination method and the observability of the optimal estimation initial alignment, it is concluded that the accuracy of the two methods under a static base is equivalent. This conclusion is verified through experiments, and it is found that when the alignment time is short, the multi-vector attitude determination method has large fluctuations. Therefore, in order to improve the stability of the algorithm, all data in the alignment phase is used for multi-vector attitude determination. And the optimal estimation alignment is extended to the entire alignment stage to improve the alignment accuracy. Combining the characteristics of the two algorithms with the same limit accuracy but different anti-jamming capabilities, a vehicle-mounted alignment scheme with algorithm fusion is designed. The vehicle experiments show that the new scheme improves the accuracy and robustness of the initial alignment under interference conditions, and the standard deviation of the azimuth angle is reduced from 1.48′ in the traditional scheme to 0.72′.

Key words: strapdown inertial navigation system (SINS), initial alignment, information reuse, algorithm fusion

CLC Number:

V249.3

Zhihao XU, Zhaofa ZHOU, Zhenjun CHANG, Qi GUO. Initial alignment method based on information reuse and algorithm fusion[J]. Systems Engineering and Electronics, 2021, 43(5): 1310-1315.

Figures/Tables 5

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Table 1

References 30

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ψ	多矢量	估计1	估计2	融合算法
1	9.650 7	9.655 9	9.652 7	9.651 7
2	9.638 4	9.625 7	9.631 3	9.634 9
3	9.667 9	9.698 2	9.676 1	9.672 0
4	9.650 0	9.678 4	9.658 5	9.654 2
5	9.647 4	9.649 8	9.645 9	9.646 7
6	9.639 1	9.642 0	9.639 0	9.639 1
7	9.641 1	9.640 7	9.644 1	9.642 6
8	9.632 6	9.616 0	9.629 4	9.631 0
9	9.633 4	9.629 8	9.638 7	9.636 0
10	9.640 3	9.645 5	9.643 1	9.641 7
均值	9.644 1	9.648 2	9.645 9	9.645 0
1σ (′)	0.630 0	1.480 0	0.830 0	0.720 0

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Initial alignment method based on information reuse and algorithm fusion

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