基于权衡因子的联邦滤波信息分配系数研究
|
摘要
组合导航系统的融合算法普遍采用运行速度快、实时性强、计算量小的联邦滤波算法。针对该算法中当前的信息分配原则无法同时兼顾系统滤波精度及容错性的缺陷,采用了一种基于权衡因子的自适应信息分配方法。通过各子系统的误差协方差及量测噪声方差分别计算出能够提高系统滤波精度和容错性的信息分配系数,将各子系统的故障概率归一化得出该子系统的权衡因子,并在权衡因子的作用下自适应调节上述两种信息分配系数所占的比重,达到同时兼顾系统滤波精度和容错性的目的。仿真结果表明该方法能够减小系统的融合误差,保证系统的工作性能及融合精度。
The fusion algorithm of integrated navigation system generally adopted a federated filtering algorithm with fast running speed, strong real-time performance, and low computational complexity. For the current information distribution principle in the algorithm couldn't take into account the defects of system filtering accuracy and fault tolerance at the same time, an adaptive information distribution method based on the balancing factor was adopted. Through the error covariance and measurement noise variance of each subsystem, the information distribution coefficient that could improve the filtering accuracy and fault tolerance of the system was calculated. The failure probability of each subsystem was normalized to obtain the balancing factor of the subsystem. Under the balancing factor, the proportion of the above two information distribution coefficients was adjusted adaptively, so as to achieve both the accuracy and fault tolerance of the system. Simulation results showed that the proposed method successfully reduced the fusion error of the system and guaranteed the performance and fusion accuracy of the system.
The fusion algorithm of integrated navigation system generally adopted a federated filtering algorithm with fast running speed, strong real-time performance, and low computational complexity. For the current information distribution principle in the algorithm couldn't take into account the defects of system filtering accuracy and fault tolerance at the same time, an adaptive information distribution method based on the balancing factor was adopted. Through the error covariance and measurement noise variance of each subsystem, the information distribution coefficient that could improve the filtering accuracy and fault tolerance of the system was calculated. The failure probability of each subsystem was normalized to obtain the balancing factor of the subsystem. Under the balancing factor, the proportion of the above two information distribution coefficients was adjusted adaptively, so as to achieve both the accuracy and fault tolerance of the system. Simulation results showed that the proposed method successfully reduced the fusion error of the system and guaranteed the performance and fusion accuracy of the system.
引文
[1] 卫博雅,寿业航,蒋雯.一种可视化的加权平均信息融合方法[J].西安交通大学学报,2018,52(4):145-149.Wei Boya,Shou Yehang,Jiang Weng.A visibility graph information fusion method with weighted average[J].Journal of Xi’an Jiaotong University,2018,52(4):145-149.
[2] 焦婷,董昱.多传感器融合定位在高速铁路的应用[J].电子科技,2009,22(11):40-42.Jiao Ting,Dong Yu.Application of multi-sensor fusion locating in the high speed railway[J].Electronic Science and Technology,2009,22(11):40-42.
[3] Carlson N A.Federated filter for computer-efficient,near-optimal GPS integration,position location and navigation symposium[J].IEEE Letter,1996(5):22-26.
[4] 张亮,高井祥,李增科.信息分配系数的自适应滤波在GPS/DR组合导航中的应用[J].测绘科学技术学报,2015,32(1):18-21.Zhang Liang,Gao Jingxiang,Li Zengke.Application of adaptive filter based on information distribution coefficient to GPS/DR integrated navigation[J].Journal of Geomatics Science and Technology,2015,32(1):18-21.
[5] 黄丽霞.GPS、北斗卫星导航系统组合单点定位模型及算法[J].电子科技,2014,27(7):23-25.Huang Lixia.Combination of single point positioning model and algorithm based on GPS and the beidou satellite navigation system[J].Electronic Science and Technology,2014,27(7):23-25.
[6] 胡健,马大为,程向红,等.联邦滤波信息分配方法及其在传递对准中的应用[J].南京理工大学学报,2011,35(2):224-229.Hu Jian,Ma Dawei,Cheng Xianghong,et al.Information sharing method in federated filter and its application in transfer alignment[J].Journal of Nanjing University of Science and Technology,2011,35(2):224-229.
[7] 吴玲,孙永荣,陈传德.多星座组合导航自适应信息融合滤波算法[J].航天控制,2010,28(6):38-42.Wu Ling,Sun Yongrong,Chen Chuande.The adaptive information fusion filtering algorithm for multi-constellation integrated navigation system[J].Aerospace Control,2010,28(6):38-42.
[8] 陈晶,袁书明,程建华,等.基于改进联邦Kalman滤波的组合校准方法研究[J].导航定位与授时,2016,3(6):21-25.Cheng Jing,Yuan Shuming,Cheng Jianhua,et al.Research on integrated calibration based on improved federated kalman filter[J].Navigation Positioning & Timing,2016,3(6):21-25.
[9] 张远海,翁佩纯.卡尔曼滤波对舰船导航系统精确性研究[J].舰船科学技术,2016,38(24):118-120.Zhang Yuanhai,Weng Peichun.Research on the accuracy of ship navigation system with kalman filter[J].Ship Science and Technology,2016,38(24):118-120.
[10] 周红坤.小型无人机多传感器组合导航系统设计与实现[J].电子科技,2016,29(8):10-13.Zhou Hongkun.Design and implementation of the multi sensor integrated navigation system for Mini-UAV[J].Electronic Science and Technology,2016,29(8):10-13.
[11] 袁赣南,袁克非,张红伟,等.联邦滤波器信息分配原则的探讨[J].中国航海,2012,35(3):11-15.Yuan Gannan,Yuan Kefei,Zhang Weihong,et al.Discussion of information sharing principe for federated filter[J].Navigation of China,2012,35(3):11-15.
[12] 冯文,郝顺义,冯兴春,等.基于改进B型灰色关联度与权衡因子的容错联邦滤波算法[J].计算机应用,2012,32(5):1307-1310.Feng Wen,Hao Shunyi,Feng Xingchun,et al.Fault-tolerant federated filtering algorithm based on improved B-style grey relationship degree and balance coefficient[J].Journal of Computer Applications,2012,32(5):1307-1310.
[13] 刘瑞华,刘建业.联邦滤波信息分配新方法[J].中国惯性技术学报,2001(2):29-33.Liu Ruihua,Liu Jianye.A new method of information sharing in federated filter[J].Journal of Chinese Inertial Technology,2001(2):29-33.
[14] Gu Qitai,Fang Jing.Global optimality for generalized federated filter[J].Acta Automatica Sinica,2009,35(10):1310-1316.
[15] 王勋,王新龙,车欢.一种捷联惯性/GPS/陆基容错复合导航方案设计[J].航空兵器,2015(5):11-17.Wang Xun,Wang Xinlong,Che Huan.A SINS / GPS/land-based fault-tolerant integrated navigation design scheme[J].Aero Weaponry,2015(5):11-17.
[16] 段睿,张小红,朱锋.多源信息融合的组合导航自适应联邦滤波算法[J].系统工程与电子技术,2018,40(2):267-272.Duan Rui,Zhang Xiaohong,Zhu Feng.Adaptive federated filter for multi-sources information fusion in integrated navigation system[J].Systems Engineering and Electronics,2018,40(2):267-272.
[2] 焦婷,董昱.多传感器融合定位在高速铁路的应用[J].电子科技,2009,22(11):40-42.Jiao Ting,Dong Yu.Application of multi-sensor fusion locating in the high speed railway[J].Electronic Science and Technology,2009,22(11):40-42.
[3] Carlson N A.Federated filter for computer-efficient,near-optimal GPS integration,position location and navigation symposium[J].IEEE Letter,1996(5):22-26.
[4] 张亮,高井祥,李增科.信息分配系数的自适应滤波在GPS/DR组合导航中的应用[J].测绘科学技术学报,2015,32(1):18-21.Zhang Liang,Gao Jingxiang,Li Zengke.Application of adaptive filter based on information distribution coefficient to GPS/DR integrated navigation[J].Journal of Geomatics Science and Technology,2015,32(1):18-21.
[5] 黄丽霞.GPS、北斗卫星导航系统组合单点定位模型及算法[J].电子科技,2014,27(7):23-25.Huang Lixia.Combination of single point positioning model and algorithm based on GPS and the beidou satellite navigation system[J].Electronic Science and Technology,2014,27(7):23-25.
[6] 胡健,马大为,程向红,等.联邦滤波信息分配方法及其在传递对准中的应用[J].南京理工大学学报,2011,35(2):224-229.Hu Jian,Ma Dawei,Cheng Xianghong,et al.Information sharing method in federated filter and its application in transfer alignment[J].Journal of Nanjing University of Science and Technology,2011,35(2):224-229.
[7] 吴玲,孙永荣,陈传德.多星座组合导航自适应信息融合滤波算法[J].航天控制,2010,28(6):38-42.Wu Ling,Sun Yongrong,Chen Chuande.The adaptive information fusion filtering algorithm for multi-constellation integrated navigation system[J].Aerospace Control,2010,28(6):38-42.
[8] 陈晶,袁书明,程建华,等.基于改进联邦Kalman滤波的组合校准方法研究[J].导航定位与授时,2016,3(6):21-25.Cheng Jing,Yuan Shuming,Cheng Jianhua,et al.Research on integrated calibration based on improved federated kalman filter[J].Navigation Positioning & Timing,2016,3(6):21-25.
[9] 张远海,翁佩纯.卡尔曼滤波对舰船导航系统精确性研究[J].舰船科学技术,2016,38(24):118-120.Zhang Yuanhai,Weng Peichun.Research on the accuracy of ship navigation system with kalman filter[J].Ship Science and Technology,2016,38(24):118-120.
[10] 周红坤.小型无人机多传感器组合导航系统设计与实现[J].电子科技,2016,29(8):10-13.Zhou Hongkun.Design and implementation of the multi sensor integrated navigation system for Mini-UAV[J].Electronic Science and Technology,2016,29(8):10-13.
[11] 袁赣南,袁克非,张红伟,等.联邦滤波器信息分配原则的探讨[J].中国航海,2012,35(3):11-15.Yuan Gannan,Yuan Kefei,Zhang Weihong,et al.Discussion of information sharing principe for federated filter[J].Navigation of China,2012,35(3):11-15.
[12] 冯文,郝顺义,冯兴春,等.基于改进B型灰色关联度与权衡因子的容错联邦滤波算法[J].计算机应用,2012,32(5):1307-1310.Feng Wen,Hao Shunyi,Feng Xingchun,et al.Fault-tolerant federated filtering algorithm based on improved B-style grey relationship degree and balance coefficient[J].Journal of Computer Applications,2012,32(5):1307-1310.
[13] 刘瑞华,刘建业.联邦滤波信息分配新方法[J].中国惯性技术学报,2001(2):29-33.Liu Ruihua,Liu Jianye.A new method of information sharing in federated filter[J].Journal of Chinese Inertial Technology,2001(2):29-33.
[14] Gu Qitai,Fang Jing.Global optimality for generalized federated filter[J].Acta Automatica Sinica,2009,35(10):1310-1316.
[15] 王勋,王新龙,车欢.一种捷联惯性/GPS/陆基容错复合导航方案设计[J].航空兵器,2015(5):11-17.Wang Xun,Wang Xinlong,Che Huan.A SINS / GPS/land-based fault-tolerant integrated navigation design scheme[J].Aero Weaponry,2015(5):11-17.
[16] 段睿,张小红,朱锋.多源信息融合的组合导航自适应联邦滤波算法[J].系统工程与电子技术,2018,40(2):267-272.Duan Rui,Zhang Xiaohong,Zhu Feng.Adaptive federated filter for multi-sources information fusion in integrated navigation system[J].Systems Engineering and Electronics,2018,40(2):267-272.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |