一种简化的发射系下SINS/GPS/CNS组合导航系统无迹卡尔曼滤波算法

doi:10.3969/j.issn.1000-1093.2015.03.016

兵工学报 ›› 2015, Vol. 36 ›› Issue (3): 484-491.doi: 10.3969/j.issn.1000-1093.2015.03.016

一种简化的发射系下SINS/GPS/CNS组合导航系统无迹卡尔曼滤波算法

潘加亮¹，熊智¹，王丽娜²，郁丰³，赵慧¹，林爱军¹

(1.南京航空航天大学自动化学院, 江苏南京 210016; 2.北京航天自动控制研究所, 北京 100854;

收稿日期:2014-07-15 修回日期:2014-07-15 上线日期:2015-05-01
通讯作者: 潘加亮 E-mail:panjl@nuaa.edu.cn
作者简介:潘加亮(1990—)男，硕士研究生
基金资助:
国家留学基金委项目(201203070126)；江苏省六大人才高峰项目(2013-JY-013)；江苏高校优势学科建设工程项目(2011年)；中央高校基本科研业务费专项资金项目（NZ2014406）;国家自然科学基金项目（61374115、61203188、91016019、 60904091、61210306075、61203197）

A Simplified UKF Algorithm for SINS/GPS/CNS Integrated Navigation System in Launch Inertial Coordinate System

PAN Jia-liang¹， XIONG Zhi¹, WANG Li-na², YU Feng³, ZHAO Hui¹, LIN Ai-jun¹

（1.College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, Jiangsu， China；2.Beijing Aerospace Automatic Control Research Institute, Beijing 100854, China；3.College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, Jiangsu， China）

Received:2014-07-15 Revised:2014-07-15 Online:2015-05-01
Contact: PAN Jia-liang E-mail:panjl@nuaa.edu.cn

摘要/Abstract

摘要： 在弹载等高动态环境下组合导航系统状态方程具有强非线性，且各状态相互耦合影响，传统的扩展卡尔曼滤波(EKF)算法因忽略高阶项相互影响，其模型线性化展开会导致模型不准确引起导航精度下降；无迹卡尔曼滤波（UKF）算法能有效避免引入线性化误差，却存在因组合导航系统维数过高引起大量粒子递推滤波计算复杂而影响算法实时性的问题。为此，针对发射惯性系下弹载组合导航系统对滤波算法高实时性和高精确性的要求，设计了一种简化UKF(SUKF)算法，SUKF算法通过对导航系统的状态参数直接进行建模估计，解决了传统UKF算法实时性差的问题，同时继承了传统UKF算法无需模型一阶线性化展开的优点，提高了导航系统的精度。算法仿真结果表明，SUKF算法有效提高了系统解算的实时性和滤波精度，非常适合用于实际工程系统。

关键词: 控制科学与技术, 无迹卡尔曼滤波, 简化无迹卡尔曼滤波, 捷联惯导系统, 导航星全球定位系统, 天文导航系统, 组合导航

Abstract: Each state of integrated navigation system is strongly nonlinear and coupled with each other under missile-borne high-dynamic environment. Since the traditional extended Kalman filter algorithm ignors the interaction of higher order terms, its linear expansion may teads to its inaccuracy and the decrease in navigation precision. UKF algorithm can effectively avoid bringing a linearization error, but the real-time of the algorithm can be affected by the complex filtering calculation for a large number of particles in high-dimensional integrated navigation system. A simplified unscented Kalman filter (SUKF) algorithm is proposed for the requirement of high real-time and accuracy for the missile-borne integrated navigation system in launch inertial coordinate system. SUKF can be used to directly estimates the state parameters of the navigation system to achieve the real-time and improve the navigation precision without linearization extension in traditional EKF. The simulation results show that SUKF effectively improves the real-time and filtering precision of algorithm, so SUKF is very suitable for the actual engineering system.

Key words: control science and technology, unscented Kalman filter, simplified unscented Kalman filter, SINS, GPS, CNS, integrated navigation

中图分类号:

V448.2

潘加亮，熊智，王丽娜，郁丰，赵慧，林爱军. 一种简化的发射系下SINS/GPS/CNS组合导航系统无迹卡尔曼滤波算法[J]. 兵工学报, 2015, 36(3): 484-491.

PAN Jia-liang， XIONG Zhi, WANG Li-na, YU Feng, ZHAO Hui, LIN Ai-jun. A Simplified UKF Algorithm for SINS/GPS/CNS Integrated Navigation System in Launch Inertial Coordinate System[J]. Acta Armamentarii, 2015, 36(3): 484-491.

参考文献

［1］ Wang R, Xiong Z, Liu J, et al. SINS/GPS/CNS information fusion system based on improved Huber filter with classified adaptive factors for high-speed UAVs［C］∥Proceeding of the 2012 IEEE/ION Position, Location and Navigation Symposium (PLANS). Myrtle Beach,South Cardina: IEEE, 2012: 441-446.
［2］ Hu H D, Huang X L.SINS/CNS/GPS integrated navigation algorithm based on UKF［J］. Journal of Systems Engineering and Electronics,2010, 21(1):102-109.
［3］江晓东,谢京稳,郭军海. 基于UKF的再入弹道高精度估计方法研究［J］. 航天控制,2011,29(3):28-32.
JIANG Xiao-dong, XIE Jing-wen, GUO Jun-hai.The precision estimation method of reentry target trajectory based on the UKF［J］. Aerospace Control, 2011,29(3):28-32.(in Chinese)
［4］ Wang Q T, Xiao D. The research and application of robust UKF algorithm for GPS/SINS integrated system［J］. Journal of Convergence Information Technology,2011,6(6):202-211.
［5］ Majeed M,Kar I N. Aerodynamic parameter estimation using adaptive unscented Kalman filter［J］. Aircraft Engineering and Aerospace Technology, 2013, 85(4):267-279.
［6］罗楠,许录平,张华. 基于UKF和信息融合的航天器自主导航方法［J］. 中国空间科学技术,2012,32(2):1-9.
LUO Nan, XU Lu-ping, ZHANG Hua. Method of autonomous celestial navigation based on UKF and information fusion［J］. Chinese Space Science and Technology, 2012,32(2):1-9.(in Chinese)
［7］ Zhang H T. Unscented Kalman filter and its nonlinear application for tracking a moving target［J］. Optik-International Journal for Light and Electron Optics,2013,13(3):4468-4471.
［8］ Kols S,Foss B A, Schei T S. Constrained nonlinear state estimation based on the UKF approach［J］. Computers and Chemical Engineering, 2009, 33(8):1386-1401.
［9］周丕森,鲍其莲. 组合导航系统UKF滤波算法设计［J］. 上海交通大学学报,2009,43(3):389-392.
ZHOU Pei-sen, BAO Qi-lian. Design of filter in micro-integrated navigation system［J］.Journal of Shanghai Jiaotong University,2009,43(3):389-392.(in Chinese)
［10］ Luo Z,Fang H J. Modified state prediction algorithm based on UKF［J］. Journal of Systems Engineering and Electronics,2013,24(1):135-140.
［11］ Liu J, Ma J. Pulsar/CNS integrated navigation based on federated UKF［J］. Journal of Systems Engineering and Electronics,2010,21(4):675-681.
［12］ Ge Z X, Yang Y M, Zheng H. A new UKF based fault detection method in non-linear systems［J］.International Journal of Plant Engineering and Management,2006, 11(3):179-183.
［13］ Rhudy M,Gu Y, Jason V, et al. Evaluation of matrix square root operations for UKF within a UAV GPS/INS sensor fusion application［J］. International Journal of Navigation and Observation, 2011:1-11.
［14］ Nowak T, Eidloth A. Dynamic multipath mitigation applying unscented Kalman filters in local positioning systems［J］. International Journal of Microwave and Wireless Technologies,2011, 3(3): 365-372.

[1]	王振峰，李飞，王新宇，杨建森，秦也辰. 基于交互式多模型无迹卡尔曼滤波的悬架系统状态估计[J]. 兵工学报, 2021, 42(2): 242-253.
[2]	张征, 刘春光, 马晓军, 张运银. 一种基于数据融合的全轮驱动车辆质心侧偏角估计方法[J]. 兵工学报, 2020, 41(5): 842-849.
[3]	张梦得, 李开龙, 胡柏青, 吕旭. 辅助信息源短时失效下的捷联惯性基组合导航算法[J]. 兵工学报, 2020, 41(10): 2008-2015.
[4]	张成举，王聪，曹伟，王金强. 基于无迹卡尔曼滤波的超空泡航行体最优控制研究[J]. 兵工学报, 2019, 40(6): 1235-1243.
[5]	马艳，刘小东. 状态自适应无迹卡尔曼滤波算法及其在水下机动目标跟踪中的应用[J]. 兵工学报, 2019, 40(2): 361-368.
[6]	雷晓云，张志安，杜忠华. 基于改进无迹卡尔曼滤波的弹道射程修正算法研究[J]. 兵工学报, 2018, 39(9): 1701-1710.
[7]	闵艳玲，熊智，邢丽，刘建业，殷德全. 基于对偶四元数的惯性/卫星/天文组合导航系统改进联邦滤波方法[J]. 兵工学报, 2018, 39(2): 315-324.
[8]	谢恺，秦鹏程. 基于七维状态向量反向无迹卡尔曼滤波的弹道外推算法[J]. 兵工学报, 2018, 39(10): 1945-1950.
[9]	丁传俊，张相炎，刘宁. 基于自适应无迹卡尔曼滤波算法的多股螺旋弹簧动态响应模型参数辨识和分析[J]. 兵工学报, 2018, 39(1): 28-37.
[10]	苗建明，王少萍，范磊，李元. 欠驱动自主水下航行器空间曲线路径跟踪控制研究[J]. 兵工学报, 2017, 38(9): 1786-1796.
[11]	查峰，覃方君，李京书，叶斌. 三轴旋转惯性导航系统的旋转控制建模研究[J]. 兵工学报, 2017, 38(8): 1610-1618.
[12]	伍明，张国良，李琳琳，付光远，李承剑. 基于动态和静态环境对象观测一致性约束的移动机器人多传感器标定优化方法[J]. 兵工学报, 2017, 38(8): 1630-1641.
[13]	王世峰，都凯悦，孟颖，王锐. 基于机器学习的车辆路面类型识别技术研究[J]. 兵工学报, 2017, 38(8): 1642-1648.
[14]	周荻，董金鲁. 带襟翼的飞行器非线性控制系统分析和设计[J]. 兵工学报, 2017, 38(7): 1322-1329.
[15]	郑睿，赵伟，方明星，杜友武. 基于多圈环绕结构的超流体陀螺噪声抑制方法[J]. 兵工学报, 2017, 38(7): 1330-1335.

一种简化的发射系下SINS/GPS/CNS组合导航系统无迹卡尔曼滤波算法

A Simplified UKF Algorithm for SINS/GPS/CNS Integrated Navigation System in Launch Inertial Coordinate System

PDF

可视化

被引次数

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