基于5阶降维平方根-容积卡尔曼滤波的动基座对准应用研究

doi:10.3969/j.issn.1000-1093.2016.02.004

兵工学报 ›› 2016, Vol. 37 ›› Issue (2): 219-225.doi: 10.3969/j.issn.1000-1093.2016.02.004

基于5阶降维平方根-容积卡尔曼滤波的动基座对准应用研究

黄湘远，汤霞清，武萌，吴伟胜

（装甲兵工程学院控制工程系，北京 100072）

收稿日期:2015-06-02 修回日期:2015-06-02 上线日期:2016-04-22
通讯作者: 黄湘远 E-mail:huangxiangyuan.623@163.com
作者简介:黄湘远（1988—），男，博士研究生
基金资助:
军队计划项目（51309030106）

Research on Initial Alignment of Moving Base with 5th-degree Dimensionality Reduction SR-CKF

HUANG Xiang-yuan, TANG Xia-qing, WU Meng, WU Wei-sheng

(Department of Control Engineering, Academy of Armored Force Engineering, Beijing 100072, China)

Received:2015-06-02 Revised:2015-06-02 Online:2016-04-22
Contact: HUANG Xiang-yuan E-mail:huangxiangyuan.623@163.com

摘要/Abstract

摘要： 为提高动基座下捷联惯导系统的对准精度、数值稳定性和减小计算量，将5阶容积卡尔曼滤波(CKF)、降维算法、多次离散和平方根(SR)滤波结合起来，形成5阶降维SR-CKF非线性对准方案。为减小5阶CKF的计算量，建立非线性-线性分离的系统模型，引入降维算法；为提高1阶龙格-库塔法的逼近精度，设计多次离散和时间更新的滤波框架；为提高数值稳定性，推导了5阶降维SR-CKF；比较常规3阶SR-CKF、5阶CKF和5阶降维SR-CKF的各项特性。实车动基座对准实验结果表明：该方案对准精度高、数值稳定性强、计算量小，满足应用需要。

关键词: 兵器科学与技术, 容积卡尔曼滤波, 降维, 平方根滤波, 多次离散

Abstract: In order to achieve higher alignment precision, stronger numerical stability and lower computational cost for nonlinear alignment of strapdown inertial navigation system (SINS) on moving base, a scheme of 5th-degree dimensionality reduction SR-CKF nonlinear alignment is proposed，which combines 5th-degree cubature Kalman filter (CKF), dimensionality reduction algorithm, multiple discretization, and square root(SR) filter. A nonlinear-linear separation system model is established, and the dimensionality reduction algorithm is introduced to reduce the calculated amount. A multiple discretization and time update filter framework is designed to improve the approximation accuracy. The 5th-degree dimensionality reduction SR-CKF is deduced to improve the numerical stability. The features of the conventional 3rd-degree SR-CKF, 5th-degree CKF and the proposed algorithm are compared. The experimental results show that the proposed method has a high alignment precision, strong numerical stability and little calculated amount, which meets the application requirements.

Key words: ordnance science and technology, cubature Kalman filter, dimensionality reduction, square root filter, multiple discretization

中图分类号:

U666.1

黄湘远，汤霞清，武萌，吴伟胜. 基于5阶降维平方根-容积卡尔曼滤波的动基座对准应用研究[J]. 兵工学报, 2016, 37(2): 219-225.

HUANG Xiang-yuan, TANG Xia-qing, WU Meng, WU Wei-sheng. Research on Initial Alignment of Moving Base with 5th-degree Dimensionality Reduction SR-CKF[J]. Acta Armamentarii, 2016, 37(2): 219-225.

参考文献

［1］ Arasaratnam I, Haykin S. Cubature Kalman filters［J］. IEEE Transactions on Automatic Control, 2009, 54(6): 1254-1269.
［2］ Ge Q b, Xu D X, Wen C L. Cubature information filters with correlated noises and their applications in decentralized fusion ［J］. Signal Processing, 2014, 94: 434-444.
［3］ Gadsden S A, Al-Shabi M, Arasaratnam I, et al. Combined cubature Kalman and smooth variable structure filtering: a robust nonlinear estimation strategy ［J］. Signal Process, 2014, 96:290-299.
［4］ Jia B, Xin M, Cheng Y. High-degree cubature Kalman filter ［J］. Automatica, 2013, 49(2): 510-518.
［5］ Jia B, Xin M. Rauch-Tung-Striebel high-degree cubature Kalman smoother ［C］ ∥Proceddings of American Control Conference. Washington, DC, US:the American Automatic Control Council, 2013:2472-2477.
［6］ Wang S Y, Feng J C, Tse C K. Spherical simplex-radial cubature Kalman filter ［J］. IEEE Signal Processing Letters, 2014, 21(1): 43-46.
［7］钱华明,葛磊,黄蔚,等. 降维CKF算法及其在SINS初始对准中的应用［J］. 系统工程与电子技术, 2013, 35(7):1492-1497.
QIAN Hua-ming, GE Lei, HUANG Wei, et al. Reduced dimension CKF algorithm and its application in SINS initial alignment ［J］. Systems Engineering and Electronics, 2013, 35(7): 1492-1497.(in Chinese)
［8］李方能,许江宁,亓洪标.基于边缘采样UKF滤波的捷联惯导初始对准方法［J］.中国惯性技术学报, 2014, 22(5): 612-618.
LI Fang-neng, XU Jiang-ning, QI Hong-biao. Initial alignment of strapdown inertial navigation system based on marginalized unscented Kalman filter［J］. Journal of Chinese Inertial Technology, 2014, 22(5):612-618. (in Chinese)
［9］孙枫, 唐李军. Cubature卡尔曼滤波-卡尔曼滤波算法［J］. 控制与决策, 2012, 27(10): 1561-1565.
SUN Feng, TANG Li-jun. Cubature Kalman filter-Kalman filter algorithm ［J］.Control and Decision, 2012, 27(10): 1561-1565. (in Chinese)
［10］赵曦晶,刘光斌,汪立新,等. 扩展容积卡尔曼滤波-卡尔曼滤波组合算法［J］.红外与激光工程, 2014,43(2): 647-653.
ZHAO Xi-jing, LIU Guang-bin, WANG Li-xin, et al. Augmented cubature Kalman filter/Kalman filter integrated algorithm［J］. Infrared and Laser Engineering, 2014, 43(2): 647-653. (in Chinese)
［11］ Chandra K P B, Gu D W, Postlethwaite I. Square root cubature information filter［J］. IEEE Sensors Journal, 2013, 13(2): 750-758.
［12］ Wang S Y, Feng J C, Tse C K. Novel cubature Kalman filtering for systems involving nonlinear states and linear measurements ［J］. International Journal of Electronics and Communications, 2015, 69: 314-320.
［13］张秋昭,张书毕,刘志平,等. 基于奇异值分解的鲁棒容积卡尔曼滤波及其在组合导航中的应用［J］.控制与决策,2014,29(2): 341-346
ZHANG Qiu-zhao, ZHANG Shu-bi, LIU Zhi-ping, et al. Robust cubature Kalman filter based on SVD and its application to integrated navigation［J］. Control and Decision, 2014,29(2): 341-346. (in Chinese)
［14］严恭敏,严卫生,徐德民.基于欧拉平台误差角的SINS 非线性误差模型研究［J］. 西北工业大学学报, 2009,27(4): 511-516.
YAN Gong-min, YAN Wei-sheng, XU De-min. A SINS nonlinear error model reflecting better characteristics of SINS errors［J］. Journal of Northwestern Polytechnical University, 2009, 27(4): 511-516. (in Chinese)
［15］ Ito K F, Xiong K Q. Gaussian filter for nonlinear filtering problem ［J］.IEEE Transactions on Automatic Control, 2000, 45(5): 910- 927.
［16］ Julier S J, Uhlmann J K, Durrant-Whyte H F. Unscented filter-ing and nonlinear estimation ［J］. Proceedings of the IEEE Aerospace and Electronic Systems, 2004, 92(3):401-422.
［17］ Lu J, Darmofal D L. Higher-dimensional integration with Gaussian weight for applications in probabilistic design ［J］. SIAM Journal on Scientific Computing, 2004, 26(2):613-624.
［18］ Wu Y X, Hu D W, Wu M P, et al. A numerical-integration perspective on Gaussian filters ［J］. IEEE Transactions on Signal Processing, 2006, 54(8): 2910-2921.

[1]	吴则良，叶建川，王江，金忍. 基于深度自动编码器神经网络的飞行器翼型参数降维与优化设计[J]. 兵工学报, 2022, 43(6): 1326-1336.
[2]	陈林秀，郝明瑞，赵佳佳. 混合噪声条件下的目标被动定位算法[J]. 兵工学报, 2021, 42(9): 1923-1930.
[3]	卢雨，衣晓. 基于动态规划和双变量容积卡尔曼滤波的空基外辐射源定位算法[J]. 兵工学报, 2021, 42(4): 808-816.
[4]	王宁，李银伢，戚国庆，盛安冬. 不完全量测下基于信息一致性的分布式容积卡尔曼滤波算法[J]. 兵工学报, 2021, 42(11): 2396-2408.
[5]	李东瑾，杨瑞娟，董睿杰. 基于联合投影字典学习的辐射源调制识别[J]. 兵工学报, 2020, 41(7): 1464-1472.
[6]	李东瑾，杨瑞娟，李晓柏，朱晟坤，费太勇. 基于栈式降维与字典学习的辐射源调制识别[J]. 兵工学报, 2020, 41(10): 2023-2032.
[7]	徐博，李盛新，金坤明，王连钊. 基于径向基函数神经网络辅助容积卡尔曼滤波的多自主水下航行器协同定位方法[J]. 兵工学报, 2019, 40(10): 2119-2128.
[8]	孟广伟，魏彤辉，周立明，李锋. 基于泰勒展开法的结构混合可靠性分析[J]. 兵工学报, 2018, 39(7): 1404-1410.
[9]	梁苑，盛安冬，武兆斌，张蛟，戚国庆，李银伢. 事件触发容积卡尔曼滤波及其在火控探测网络中的应用[J]. 兵工学报, 2018, 39(3): 417-427.
[10]	娄伟鹏，郑长松，李和言，陈建文，张周立，马源. 高速《Symbol》v湿式换挡离合器排油阀临界开启和关闭压力研究[J]. 兵工学报, 2017, 38(9): 1665-1672.
[11]	王宪成，杨绍卿，马宁，赵文柱. 车辆柴油机缸套动载荷磨损计算模型研究[J]. 兵工学报, 2017, 38(9): 1673-1680.
[12]	孙皓泽，常天庆，王全东，孔德鹏，戴文君. 一种基于分层多尺度卷积特征提取的坦克装甲目标图像检测方法[J]. 兵工学报, 2017, 38(9): 1681-1691.
[13]	张玉燕，孙莎莎，王振春，曹海要，战再吉. 高速载流电枢表面瞬态温度场仿真与测量[J]. 兵工学报, 2017, 38(9): 1692-1698.
[14]	李臣明，宦超，石怀龙. 某箱式火箭炮对面目标分布式杀伤最优火力分配[J]. 兵工学报, 2017, 38(9): 1699-1704.
[15]	雷娟棉，张嘉炜，谭朝明. 小攻角下船尾外形对旋转弹丸马格努斯效应影响的数值研究[J]. 兵工学报, 2017, 38(9): 1705-1715.

基于5阶降维平方根-容积卡尔曼滤波的动基座对准应用研究

Research on Initial Alignment of Moving Base with 5th-degree Dimensionality Reduction SR-CKF

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价