辅助信息源短时失效下的捷联惯性基组合导航算法

doi:10.3969/j.issn.1000-1093.2020.10.010

摘要/Abstract

摘要： 捷联惯性基组合导航中，辅助信息源短时失效会导致组合精度降低，对此提出高斯过程回归辅助捷联惯性基组合导航算法。该方法利用经过组合导航闭环校正的捷联惯性导航系统速度输出作为高斯过程回归的训练输入集，辅助信息源的速度输出作为训练输出集，在未发生失效时对高斯过程回归进行训练，辅助信息源失效时利用高斯过程回归预测辅助信息源输出以实现组合滤波量测更新。在实测实验中，利用低精度微机电系统/全球定位系统组合车载数据，比较所提算法与传统算法在失效情况下姿态、位置和陀螺常值漂移估计效果。结果显示：在60 s的短时失效情况下，所提算法纵摇、横摇、位置估计效果提高90%左右，航向估计提高70.7%，东向、北向陀螺漂移估计分别提高10.3%，10.8%，证明所提算法可以有效地解决组合导航中辅助信息源短时失效问题。

关键词: 捷联惯性导航, 组合导航, 四元数无迹卡尔曼滤波, 高斯过程回归

Abstract: In strapdown inertial integrated navigation, the short-term failure of auxiliary information source may lead to the reduction of integrated navigation accuracy. To solve this problem, a Gaussian process regression aided strapdown inertial navigation algorithm is proposed. In the proposed algorithm, the output speed of strapdown inertial navigation sysytem corrected by integrated navigation closed-loop is used as the training input set of Gaussian process regression, and the speed output of auxiliary information source is used as the training output set. Gaussian process regression is trained whithout failure of auxiliary information source. When the auxiliary information source fails, Gaussian process regression is used to predict the output of auxiliary information source to realize the measurement update of combined filter. In the experiment, the attitude, position and gyro constant drift estimated by the proposed algorithm are compared with those of the traditional algorithm in the case of failure by using the low precision MEMS/GPS integrated vehicle data. The results show that, in the case of 60 s short-term failure, the proposed algorithm improves the effects of pitch, roll and position estimations by about 90%, the effect of yaw estimation by 70.7%, and the effects of east and north gyro drift estimations by 10.3% and 10.8%, respectively. The results show that the proposed algorithm can effectively solve the problem of short-term failure of auxiliary information sources in integrated navigation.

Key words: strapdowninertialnavigatopn, integratednavigation, quaternionunscentedKalmanfilter, Gaussianprocessregression

中图分类号:

U666.12⁺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.

ZHANG Mengde, LI Kailong, HU Baiqing, L Xu. Strapdown Inertial Integrated Navigation Algorithm under Short-term Failure of Information Sources[J]. Acta Armamentarii, 2020, 41(10): 2008-2015.

参考文献

［1］严恭敏. 捷联惯导算法及车载组合导航系统研究[D]. 西安：西北工业大学, 2004.
YAN G M. Research on strapdown inertial navigation system and vehicle integrated navigation system[D]. Xi'an:Northwest University of Technology, 2004.(in Chinese)
[2］肖烜，王清哲，程远.捷联惯导系统/里程计高精度紧组合导航算法[J].兵工学报, 2012, 33(4):395-400.
XIAO X,WANG Q Z,CHENG Y. High precision compact integrated navigation algorithms for strapdown inertial navigation system/odometer[J］. Acta Armamentarii, 2012, 33(4):395-400.(in Chinese)
[3］邹强，付超，莫申童.基于卡尔曼滤波的四轴飞行器成像、惯性和高度组合导航[J].传感技术学报,2019, 32(1):1-7.
ZOU Q,FU C,MO S T. Four-axis vehicle imaging, inertia and height integrated navigation based on Kalman filter[J].Chinese Journal of Sensors and Actuators,2019, 32(1):1-7.(in Chinese)
[4］李闻白, 刘明雍, 张立川,等. 单领航者相对位移测量的多自主水下航行器协同导航[J]. 兵工学报, 2011, 32(8): 1002-1007.
LI W B,LIU M Y,ZHANG L C,et al. Multi-autonomous underwater vehicle cooperative navigation based on relative displacement measurement of single pilot[J].Acta Armamentarii,2011, 32(8): 1002-1007. (in Chinese)
[5］ CRASSIDIS J L，MARKLEY F. Unscented filtering for spacecraft attitude estimation[J].Journal of Guidance，Control，and Dynamics，2003，26(4)：536-542．
[6］ LEE J G, CHAN G P, PARK H W. Multiposition alignment of strapdown inertial navigation system[J]. IEEE Transactions on Aerospace and Electronic Systems, 1993, 29(4):1323-1328.
[7］ HAO Y S, XU A G, SUI X, et al. A modified extended Kalman filter for a two-antenna GPS/INS vehicular navigation system[J]. Sensors, 2018,18(11):3809.
[8］ LI K L, CHANG L B, HU B Q. Unscented attitude estimator based on dual attitude representations[J]. IEEE Transactions on Instrumentation and Measurement, 2015, 64(12): 3564-3576.
[9］ WU Y X，PAN X F．Velocity/position integration formula，Part I. application to in-flight coarse alignment[J].IEEE Transactions on Aerospace and Electronic Systems，2013，49(2)：1006-1023．
[10］ REN H, KAZANZIDES P. Investigation of attitude tracking using an integrated inertial and magnetic navigation system for hand-held surgical instruments[J].IEEE/ASME Transactions on Mechatronics, 2012, 17(2):210-217.
[11］ CRASSIDIS J L，MARKLEY F，CHENG Y．Survey of nonlinear attitude estimation methods[J]．Journal of Guidance，Control，and Dynamics，2007，30(1)：12-28.
[12］ WANG Y D, SUN S M, LI L. Adaptively robust unscented Kalman filter for tracking a maneuvering vehicle[J].Journal of Gui-dance, Control, and Dynamics, 2014, 37(5):1697-1701.
[13］杨小军, 施坤林, 汪仪林. 基于磁传感器/GPS组合制导飞行弹体的姿态和位置估计[J]. 兵工学报, 2008, 29(2):169-173.
YANG X J,SHI K L,WANG Y L. Attitude and position estimation of flying missile based on magnetic sensor/GPS integrated guidance [J].Acta Armamentarii, 2008, 29(2):169-173.(in Chinese)
[14］靳标，李建行，朱德宽.基于自适应有限冲激响应-卡尔曼滤波算法的GPS/INS导航[J]. 农业工程学报, 2019, 35(3):83-89.
JIN B,LI J X,ZHU D K. GPS/INS navigation based on adaptive finite impulse response-Kalman filter[J]. Journal of Agricultural Engineering,2019, 35(3):83-89.(in Chinese)
［15］ FENG K Q, LI J, ZHANG X, et al. An improved strong tracking cubature Kalman filter for GPS/INS integrated navigation systems[J].Sensors,2018,18(6):1919.
[16］ ZHOU J,KNEDLIK S, LOFFELD O. INS/GPS tightly-coupled integration using adaptive unscented particle filter[J].Journal of Navigation, 2010,63(3):491-512.
[17］孙灿龙, 张严, 李辉. 航速惯导组合导航系统在AUV上的应用[J]. 鱼雷技术, 2007, 15(6):25-27.
SUN C L,ZHANG Y,LI H. Application of velocity INS integrated navigation system in AUV[J]. Torpedo Technology,2007, 15(6): 25-27.(in Chinese)
[18］ WANG X X, LIANG Y, PAN Q, et al. Gaussian smoothers for nonlinear systems with one-step randomly delayed measurements[J]. IEEE Transactions on Automatic Control, 2013,58(7):1828-1835.
[19］ XI M, XIN Y. Fault-tolerant integrated navigation system based on NEURONS[J]. Transactions of Nanjing University of Aeronautics and Astronautics, 1998(2):4-7.
[20］王凌轩，甘雨，隋立芬.INS辅助的实时动态GNSS单频周跳探测[J]. 武汉大学学报(信息科学版), 2019, 44(3):51-57.
WANG L X,GAN Y,SUI L F. INS-assisted real-time dynamic GNSS single frequency cycle-slip detection[J]. Journal of Wuhan University (Information Science Edition)， 2019, 44(3):51-57. (in Chinese)
[21］ LIN X Y, YOU H E. Failure detection method of double satellite positioning in its integrated system with SINS[J]. Acta Geodaetica et Cartographica Sinica, 2006, 35(4):332-336.
[22］ MALLESWARAN M, VAIDEHI V, MOHANKUMAR M. A hybrid approach for GPS/INS integration using Kalman filter and IDNN[C]∥Proceedings of 2011 Third International Conference on Advanced Computing. Chennai, India: IEEE，2012：378-383.
[23］朱倚娴.AUV组合导航系统容错关键技术研究[D].南京：东南大学,2018.
ZHU Y X. Research on fault tolerant key technologies of AUV integrated navigation system[D].Nanjing: Southeast University,2018.(in Chinese)
[24］李兵，李战群，湛雷.基于GPS/SINS/里程计的车载组合导航研究[J].测控技术，2012,31（11）：43-47.
LI B,LI Z Q,ZHAN L.Research on vehicle integrated navigation based on GPS/SINS/odometer[J]. Measurement and Control Technology,2012,31（11）：43-47.(in Chinese)
[25］李浩，杨家胜.车辆运动数学模型辅助的动基座对准方法[J].火力与指挥控制，2014,39（9）：36-39.
LI H,YANG J S. Alignment method of moving base assisted by mathematical model of vehicle motion[J]. Fire Control & Command Control，2014,39（9）：36-39.(in Chinese)
[26］ CRASSIDIS J L. Sigma-point Kalman filtering for integrated GPS and inertial navigation[J]. IEEE Transactions on Aerospace and Electronic Systems，2006，42(2)：750-756．
[27］ ZHANG L, LIU Z, ZHANG J Q. A genetic Gaussian process regression model based on memetic algorithm[J]. Journal of Central South University, 2013, 20(11):3085-3093.
[28］ KO J, FOX D. GP-BayesFilters: Bayesian filtering using Gaussian process prediction and observation models[C]∥Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems. Nice, France: IEEE,2009:75-90.

[1]	朱建良, 王立雅, 薄煜明. 行人GNSS/PDR组合导航优化估计方法[J]. 兵工学报, 2023, 44(10): 3137-3145.
[2]	张福斌，林家昀. 深度相机与微机电惯性测量单元松组合导航算法[J]. 兵工学报, 2021, 42(1): 159-166.
[3]	沈世斌，谢非，赵静，钱伟行，康国华，刘锡祥. 基于相位控制的惯性与卫星超紧组合导航系统信号解调方法[J]. 兵工学报, 2020, 41(3): 495-506.
[4]	胡杰，严勇杰，王子卉. 基于速度约束与模糊自适应滤波的车载组合导航[J]. 兵工学报, 2020, 41(2): 231-238.
[5]	常振军，张志利，周召发，徐志浩，郭琦. 基于尺寸效应在线补偿的旋转捷联惯性导航系统初始对准[J]. 兵工学报, 2020, 41(10): 2016-2022.
[6]	董春梅，任顺清，陈希军，李巍. 一种激光陀螺捷联惯性导航系统级标定方法[J]. 兵工学报, 2019, 40(8): 1618-1626.
[7]	徐昊玮，廉保旺，刘尚波. 基于滑动窗迭代最大后验估计的多源组合导航因子图融合算法[J]. 兵工学报, 2019, 40(4): 807-819.
[8]	李群生，赵剡，王进达. 一种适用于高动态强干扰环境的视觉辅助微机械捷联惯性导航系统/全球定位系统超紧组合导航系统[J]. 兵工学报, 2019, 40(11): 2241-2249.
[9]	刘生攀, 王文举, 饶兴桥. 单轴旋转捷联惯性导航系统误差分析与转位方案研究[J]. 兵工学报, 2018, 39(9): 1749-1755.
[10]	李伟，姜俊峰，任海波，张光辉. 行进间火炮指向稳定跟踪系统建模与控制[J]. 兵工学报, 2018, 39(8): 1479-1490.
[11]	周召发，胡文，张志利，徐梓皓，陈河. 一种新的捷联惯性导航系统姿态四元数方程求解方法[J]. 兵工学报, 2018, 39(3): 511-518.
[12]	陈河，张志利，周召发，刘朋朋，赵晓枫. 激光陀螺捷联惯性导航系统解析对准误差特性分析[J]. 兵工学报, 2018, 39(3): 502-510.
[13]	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.
[14]	邢丽, 熊智, 刘建业, 杭义军. 基于对偶四元数的圆锥及划船误差补偿改进算法[J]. 兵工学报, 2017, 38(7): 1336-1347.
[15]	黄欣，熊智，许建新，徐丽敏. 基于零速/航向自观测/地磁匹配的行人导航算法研究[J]. 兵工学报, 2017, 38(10): 2031-2040.