多表冗余惯导数据融合算法及在自对准中的应用

郭建刚; 陈鹏; 郑伟

doi:10.13700/j.bh.1001-5965.2019.0604

多表冗余惯导数据融合算法及在自对准中的应用

doi: 10.13700/j.bh.1001-5965.2019.0604

郭建刚^{1, 2},
陈鹏³,
郑伟^1, ,

1.
国防科技大学空天科学学院, 长沙 410073
2.
北京航天时代激光导航技术有限责任公司, 北京 100094
3.
上海航天技术研究院北京研发中心, 北京 100081

基金项目:

国家自然科学基金 0901010517001

详细信息

作者简介:
郭建刚男, 博士研究生, 高级工程师。主要研究方向：飞行动力学与控制

郑伟男, 博士, 教授，博士生导师。主要研究方向：飞行动力学与控制

通讯作者:
郑伟, E-mail: zhengwei@nudt.edu.cn

中图分类号: V249.3
计量
- 文章访问数: 670
- HTML全文浏览量: 149
- PDF下载量: 116
- 被引次数: 0
出版历程
- 收稿日期: 2019-11-26
- 录用日期: 2019-12-06
- 网络出版日期: 2020-12-20

Data fusion algorithm of multi-sensor redundant inertial navigation and its application in self-alignment

GUO Jiangang^{1, 2},
CHEN Peng³,
ZHENG Wei^{1
, ,}

1.
College of Aerospace Science and Engineering, National University of Defense Technology, Changsha 410073, China
2.
Beijing Aerospace Times Laser Inertial Technology Company, Ltd., Beijing 100094, China
3.
Shanghai Academy of Spaceflight Technology Beijing R&D Center, Beijing 100081, China

Funds:

National Natural Science Foundation of China 0901010517001

More Information

Corresponding author: ZHENG Wei, E-mail: zhengwei@nudt.edu.cn

摘要

摘要:
针对多表冗余惯导系统的自对准问题，基于某型三正交两斜置冗余的十表惯导系统，对仪表数据融合算法进行了研究。通过分析和仿真验证了最小二乘估计的数据融合算法在提高系统自对准精度中的有效性，并在静态对准试验的基础上，构造了基于仪表零偏稳定性的加权矩阵。试验结果表明：相较于只使用正交仪表的数据，数据融合可以有效提高系统自对准精度，使斜置冗余仪表的数据得到充分利用，改进算法精度比马尔可夫估计有所提高。
- 多表冗余 /
- 数据融合 /
- 自对准 /
- 最小二乘估计 /
- 惯导系统
Abstract:
For the self-alignment problem of the multi-sensor redundant inertial navigation system, the data fusion algorithm of the inertial navigation system is studied based on ten-sensor inertial navigation system which consists of three-orthogonal and two-skew sensors. The validity of the least square estimation data fusion algorithm in improving the self-alignment accuracy of the system is verified through analysis and simulation. Then, a static alignment experiment is conducted and a weighted matrix based on the bias stability is established. The results of the experiment show that, compared with using the data of orthogonal instruments only, the data fusion can effectively improve the self-alignment accuracy of the system and make full use of the data of the skew redundant instruments. The accuracy of the improved algorithm is slightly better than that of Markov estimation.
- multi-sensor redundant /
- data fusion /
- self-alignment /
- least square estimation /
- inertial navigation system

HTML全文

图 1 惯性仪表安装定向示意图

Figure 1. Schematic diagram of inertial instrument installation orientation

下载: 全尺寸图片幻灯片

图 2 激光陀螺捷联惯导自对准试验

Figure 2. Self-alignment experiment of LSINS

下载: 全尺寸图片幻灯片

表 1 偏航角误差的Monte Carlo仿真结果对比

Table 1. Comparison of Monte Carlo simulation results of yaw angle error

序号	噪声标准差	三表解算		五表数据融合		比值
序号	噪声标准差	均值/(10^-5(°))	方差/(10^-4(°))	均值/(10^-5(°))	方差/(10^-4(°))	均值	方差
1组	σ_g=0.03 σ_a=0.001	1.21	0.095	1.12	0.079	0.93	0.83
2组	σ_g=0.06 σ_a=0.002	2.39	0.18	1.83	0.15	0.77	0.83
3组	σ_g=0.09 σ_a=0.003	3.47	0.27	3.30	0.24	0.95	0.89
4组	σ_g=0.12 σ_a=0.004	4.79	0.36	4.37	0.32	0.91	0.89
均值						0.89	0.86

下载: 导出CSV

表 2 偏航角自对准结果对比

Table 2. Self-alignment result comparison of yaw angles

序号	偏航角/(′)
序号	三表解算	五表数据融合
1	-7.5	-4.4
2	1.5	0.9
3	-3.9	-5.8
4	-2.1	-3.9
5	-5.7	-3.7
6	-7.5	-5.8
7	-0.3	-0.3
8	-0.3	-1.4
9	-0.2	-2.1
10	-2.1	0.1
11	-2.1	-3.6
12	-3.9	1.0
标准差	2.8	2.4

下载: 导出CSV

表 3 仪表 20 ms脉冲增量输出

Table 3. Incremental pulse output of sensors with 20 ms interval

序号	A_x	A_y	A_z	A_s	A_t	G_x	G_y	G_z	G_s	G_t
1	0	0	49	-21	-23	0	0	0	0	0
2	0	-1	48	-21	-23	0	0	0	0	0
3	0	0	49	-21	-23	0	0	0	0	0
4	0	0	49	-20	-23	0	0	0	0	0
5	0	0	48	-21	-23	0	0	0	0	0
6	0	0	48	-21	-24	0	1	1	0	0
7	0	0	49	-21	-22	0	0	0	0	0
8	0	0	49	-21	-23	0	0	0	0	0
9	0	0	48	-21	-23	0	0	0	0	0
10	0	0	49	-21	-23	0	0	0	0	1

下载: 导出CSV

表 4 五表数据融合的偏航角自对准结果

Table 4. Self-alignment results of yaw angles by data fusion of five instruments

序号	偏航角/(′)
1	-4.3
2	1.0
3	-4.6
4	-3.0
5	-3.4
6	-5.1
7	-0.1
8	-0.9
9	-1.5
10	-0.2
11	-2.7
12	0.2
标准差	2.1

下载: 导出CSV

参考文献(15)

[1]	SABLYNSKI R, PORDON R.A report on the flight of Delta II's redundant inertial flight control assembly (RIFCA)[C]//IEEE 1998 Position Location and Navigation Symposium, 1998: 98CH36153.
[2]	彭蓉, 严恭敏, 秦永元.箭载捷联惯导系统水平自对准的两种实用方法[J].中国惯性技术学报, 2009, 17(4):428-435. http://www.cnki.com.cn/Article/CJFDTotal-ZGXJ200904015.htm PENG R, YAN G M, QIN Y Y.Two practical methods of autonomous leveling alignment for launch vehicle SINS[J].Journal of Chinese Inertial Technology, 2009, 17(4):428-435(in Chinese). http://www.cnki.com.cn/Article/CJFDTotal-ZGXJ200904015.htm
[3]	崔潇, 秦永元, 严恭敏, 等.基于矩阵卡尔曼滤波的捷联惯导初始对准算法[J].中国惯性技术学报, 2018, 26(5):585-590. http://www.cqvip.com/QK/83064X/201805/6100094183.html CUI X, QIN Y Y, YAN G M, et al.Initial alignment algorithm for SINS based on state matrix Kalman filter[J].Journal of Chinese Inertial Technology, 2018, 26(5):585-590(in Chinese). http://www.cqvip.com/QK/83064X/201805/6100094183.html
[4]	郭泽, 缪玲娟, 赵洪松.一种改进的强跟踪UKF算法及其在大方位失准角初始对准中的应用[J].航空学报, 2014, 35(1):203-214. http://www.cnki.com.cn/Article/CJFDTotal-HKXB201401021.htm GUO Z, MIAO L J, ZHAO H S.An improved strong tracking UKF algorithm and its application in SINS initial alignment under large azimuth misalignment angles[J].Acta Aeronautica et Astronautica Sinica, 2014, 35(1):203-214(in Chinese). http://www.cnki.com.cn/Article/CJFDTotal-HKXB201401021.htm
[5]	HE H Y, XU J N, LI J S, et al.Improved fast backtracking alignment approach for strapdown inertial navigation system[J].Journal of Chinese Inertial Technology, 2015, 23(2):179-183. http://www.researchgate.net/publication/282986198_Improved_fast_backtracking_alignment_approach_for_strapdown_inertial_navigation_system
[6]	郭士荦, 许江宁, 李峰.强跟踪CKF及其在惯导系统初始对准中的应用[J].中国惯性技术学报, 2017, 25(4):436-441. http://www.cnki.com.cn/Article/CJFDTotal-ZGXJ201704003.htm GUO S L, XU J N, LI F.Strong tracking cubature Kalman filter for initial alignment of inertial navigation system[J].Journal of Chinese Inertial Technology, 2017, 25(4):436-441(in Chinese). http://www.cnki.com.cn/Article/CJFDTotal-ZGXJ201704003.htm
[7]	BAR-ITZHACK I Y, HARMAN R R.In-space calibration of a skewed gyro quadruplet[J].Journal of Guidance, Control, and Dynamics, 2002, 25(5):852-859. doi: 10.2514/2.4978
[8]	PITTELKAU M E.Calibration and attitude determination with redundant inertial measurement units[J].Journal of Guidance, Control, and Dynamics, 2005, 28(4):743-752. doi: 10.2514/1.7040
[9]	PITTELKAU M E.Cascaded and decoupled RIMU calibration filters[J].The Journal of the Astronautical Sciences, 2006, 54:449-466. doi: 10.1007/BF03256500
[10]	YUKSEL Y, EL-SHEIMY N.An optimal fusion method for skew-redundant inertial measurement units[J].Journal of Applied Geodesy, 2011, 5(2):99-115. http://adsabs.harvard.edu/abs/2011jageo...5...99y
[11]	秦永元, 张洪钺, 汪叔华.卡尔曼滤波与组合导航原理[M].西安:西北工业大学出版社, 2015:63-68. QIN Y Y, ZHANG H Y, WANG S H.Theory of Kalman filter and integrated navigation[M].Xi'an:Northwestern Polytechnical University Press, 2015:63-68(in Chinese).
[12]	GILMORE J P.SIRU development final report[R].Washington, D.C.: NASA, 1973: 11-16.
[13]	秦永元.惯性导航[M].北京:科学出版社, 2006:240-241. QIN Y Y.Inertial navigation[M].Beijing:Science Press, 2006:240-241(in Chinese).
[14]	梅春波, 秦永元, 游金川.SINS在大失准角条件下的非线性量测初始对准算法[J].宇航学报, 2016, 37(3):1-7. http://www.cqvip.com/QK/95668X/20163/668348697.html MEI C B, QIN Y Y, YOU J C.Nonlinear measurement based SINS initial alignment for large misalignment angles[J].Journal of Astronautics, 2016, 37(3):1-7(in Chinese). http://www.cqvip.com/QK/95668X/20163/668348697.html
[15]	夏益霖, 吴家驹.航天发射的低频振动环境及其模拟[J].强度与环境, 1998(1):1-8. http://www.cnki.com.cn/Article/CJFDTotal-QDHJ199801000.htm XIA Y L, WU J J.Low-frequency vibration environment and its simulation for aerospace launch[J].Structure & Environment Engineering, 1998(1):1-8(in Chinese). http://www.cnki.com.cn/Article/CJFDTotal-QDHJ199801000.htm