地球信息科学学报 ›› 2019, Vol. 21 ›› Issue (4): 542-551.doi: 10.12082/dqxxkx.2019.180502

• 论文 • 上一篇    下一篇

轻小型无人机遥感组网飞行的高程安全监测冗余 容错算法研究

王勇军1,2(), 李智2, 孙山林2, 马兴元1, 晏磊2,3,*()   

  1. 1. 桂林电子科技大学 电子工程与自动化学院,桂林 541004
    2. 桂林航天工业学院 无人遥测重点实验室,桂林 541004
    3. 空间信息集成与3S工程应用北京市重点实验室(北京大学),北京 100871
  • 收稿日期:2018-10-15 修回日期:2019-03-05 出版日期:2019-04-24 发布日期:2019-04-24
  • 通讯作者: 晏磊 E-mail:dongwang@guat.edu.cn;lyan@pku.edu.cn
  • 作者简介:

    作者简介:王勇军(1985-),男,山东聊城人,博士生,副教授,主要从事小型无人机组合导航技术研究。E-mail: dongwang@guat.edu.cn

  • 基金资助:
    “广西特聘专家”专项经费;广西自然科学基金项目(2015GXNSFBA139251);广西科技重大专项(桂科AA172 04086);广西自然科学基金重点项目(2016GXNSFDA380031);国家重点研发计划项目(2017YFB0503004-4)

Research on the Redundancy Fault-tolerant Algorithm of Altitude Safety Monitoring for Remote Sensing Networking Flight of Light and Small UAVs

Yongjun WANG1,2(), Zhi LI2, Shanlin SUN2, Xingyuan MA1, Lei YAN2,3,*()   

  1. 1. School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin 541004, China
    2. Key Laboratory of Unmanned Aerial Vehicle Telemetry, Guilin University of Aerospace Technology, Guilin 541004 China
    3. Beijing Key Laboratory of Spatial Information Integration and 3S Application (Peking University), Beijing 100871, China
  • Received:2018-10-15 Revised:2019-03-05 Online:2019-04-24 Published:2019-04-24
  • Contact: Lei YAN E-mail:dongwang@guat.edu.cn;lyan@pku.edu.cn
  • Supported by:
    "Guangxi Specially-invited Experts" Special Funds;Guangxi Natural Science Foundation, No.2015GXNSFBA139251;Guangxi Science and Technology Projects, No. AA17204086;Key Projects of Guangxi Natural Science Foundation, No.2016GXNSFDA380031;National Key Research and Development Plan, No.2017YFB0503004-4

摘要:

针对轻小型无人机遥感组网飞行的高程安全要求,本文设计了基于INS/GPS/气压计的多源信息冗余容错测量方案。通过分析轻小型无人机遥感组网应用时复杂多变的工作环境对可靠性与容错能力的要求,采用了联邦滤波算法进行多传感器冗余信息融合。本文分析了联邦滤波结构及算法,通过计算得出了系统容错性好以及滤波精度高的信息分配系数取值原则,并在此基础上提出一种基于故障特征信息熵的Pignistic概率转换容错信息分配方法。该算法可得到清晰准确的故障概率分布,根据此概率分布运用信息熵来确定系统故障概率,进而结合信息分配系数的取值原则得出组合测量系统各个子系统的权重比。通过算例仿真验证了不同信息分配系数主要影响子系统的估计误差和容错性能,而对联邦主滤波器的融合估计误差影响较小,说明了本文的容错信息分配方法能够为各子系统分量提供可靠的分配系数。在旋翼无人机平台上的定高悬停实验证明了该方法能将无人机高程误差减小为传统联邦滤波算法的四分之一,进一步说明了该方法能提高无人机高程安全监测系统的精度及容错性。

关键词: 轻小型无人机, 多源信息融合, 容错性, 信息熵, 组网飞行, 高程安全

Abstract:

To satisfy the altitude safety monitoring for remote sensing networking flight of light and small Unmanned Aerial Vehicles (UAVs), a multi-source information redundancy measurement scheme based on INS/GPS/ barometer is designed in this paper. By analyzing the requirement of UAV reliability and fault tolerance in the complex and changeable working environment of remote sensing network application, the federated filtering algorithm is adopted to fuse the redundant information of multi-sensor. In this paper, the structure and algorithm of federated filtering are analyzed, and the principle of selecting information allocation coefficients with good fault-tolerance and high filtering accuracy is obtained. Then a Pignistic probabilistic transformation fault-tolerant information allocation method based on information entropy is proposed. This algorithm can obtain a clear and accurate fault probability distribution, from which the system fault probability is determined by information entropy . Then the weight ratio of each subsystem of the group measurement system is obtained by combining the value principle of information allocation coefficient. Simulation results show that different information allocation coefficients mainly affect the estimation error and fault-tolerant performance of subsystems, but which have little influence on the fusion estimation error of federated main filter. It shows that the fault-tolerant information allocation method in this paper can provide reliable allocation coefficients for each subsystem component. The fixed-altitude hovering experiment on the multi-rotor UAV platform proves that this method can be used to reduce the altitude error of UAV to one quarter of the traditional federated filtering algorithm, which further proves that this method can be used to improve the accuracy and fault-tolerance of the UAV altitude safety monitoring system.

Key words: light and small UAVs, multi-source information fusion, altitude monitoring, fault tolerance, entropy of information, networking flight, altitude safety