Initial alignment method based on information reuse and algorithm fusion

被引：1

作者：

Xu Z. ^{[1
]}

Zhou Z. ^{[1
]}

Chang Z. ^{[1
]}

Guo Q. ^{[2
]}

机构：

[1] Missile Engineering Institute, Rocket Force University of Engineering, Xi'an

[2] Unit 96902 of the PLA, Beijing

来源：

Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics | 2021年 / 43卷 / 05期

关键词：

Algorithm fusion; Information reuse; Initial alignment; Strapdown inertial navigation system (SINS);

D O I：

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

中图分类号：

学科分类号：

摘要：

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'. © 2021, Editorial Office of Systems Engineering and Electronics. All right reserved.

引用

页码：1310 / 1315

页数：5

共 31 条

[11]

LIU X X, XU X S, ZHAO Y, Et al., An initial alignment method for strapdown gyrocompass based on gravitational apparent motion in inertial frame, Measurement, 55, pp. 593-604, (2014)

[12]

LIU Y T, XU X S, LIU X X, Et al., A self-alignment algorithm for SINS based on gravitational apparent motion and sensor data de-nosing, Sensors, 15, 5, pp. 9827-9853, (2015)

[13]

CHANU Z J, ZHANU Z L, ZHOU Z F, Et al., Position updating and error analysis of SINS alignment for moving base, Systems Engineering and Electronics, 42, 1, pp. 172-178, (2020)

[14]

GAO F Q, DING C H, LIU J F., Initial alignment of strap down inertial navigation system using Kalman filter, Proc. of the International Conference on Computer Application and System Modeling, pp. 629-633, (2010)

[15]

MA Y F., Application of fault tolerant and federated strong tracking Kalman algorithm in integrated navigation system, Proc. of the IEEE Conference on Consumer Electronics, Communications and Networks, pp. 3946-3949, (2011)

[16]

WANG Q, GAO C F, YING Z H, Et al., Observability analysis of fixed position initial alignment of strapdown inertial navigation system, Chinese Journal of Lasers, 45, 1, pp. 24-29, (2018)

[17]

LI J L, WANG Y, GU B, Et al., State parameter adjustment filtering method of airborne POS based on instantaneous observable degree model, IEEE Trans. on Insfirurree-nlafiion and Measurement, 67, 9, pp. 2153-2159, (2018)

[18]

SILVA F O, HEMERLY E M, LEITE F W., On the error state selection for stationary sins alignment and calibration Kalman filters-part II: observability/estimability analysis, Sensors, 17, 3, (2017)

[19]

GAO K, REN S Q, CHEN X J, Et al., Observability analysis of vehicle-based-laser strapdown inertial navigation system initial alignment, Chinese Journal of Lasers, 45, 12, pp. 47-53, (2018)

[20]

XU J N, HE H Y, QIN F J, Et al., A novel autonomous initial alignment method for strap-down inertial navigation system, IEEE Trans. on Instrumentation and Measurement, 66, 9, pp. 2274-2282, (2017)

← 1 2 3 4 →