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基于ANFIS的多AUV协同定位系统量测异常检测方法

徐博 李盛新 王连钊 王权达

徐博, 李盛新, 王连钊, 王权达. 基于ANFIS的多AUV协同定位系统量测异常检测方法. 自动化学报, 2021, x(x): 1−16 doi: 10.16383/j.aas.c200921
引用本文: 徐博, 李盛新, 王连钊, 王权达. 基于ANFIS的多AUV协同定位系统量测异常检测方法. 自动化学报, 2021, x(x): 1−16 doi: 10.16383/j.aas.c200921
Xu Bo, Li Sheng-Xin, Wang Lian-Zhao, Wang Quan-Da. ANFIS-based measurement information anomaly detection method for Multi-AUV cooperative localization system. Acta Automatica Sinica, 2021, x(x): 1−16 doi: 10.16383/j.aas.c200921
Citation: Xu Bo, Li Sheng-Xin, Wang Lian-Zhao, Wang Quan-Da. ANFIS-based measurement information anomaly detection method for Multi-AUV cooperative localization system. Acta Automatica Sinica, 2021, x(x): 1−16 doi: 10.16383/j.aas.c200921

基于ANFIS的多AUV协同定位系统量测异常检测方法

doi: 10.16383/j.aas.c200921
基金项目: 国家自然科学基金(61203225), 国家自然科学基金(61633008), 黑龙江省自然科学基金(F2018009), 省博士后科研启动金(LBH-Q15032), 装备预研重点实验室基金(614221801050717), 国家海洋工程重点实验室公开项目(1616), 国家装备预研基金(61403110306)资助
详细信息
    作者简介:

    徐博:哈尔滨工程大学智能科学与工程学院副教授. 2011年获得哈尔滨工程大学精密仪器及机械博士学位. 主要研究方向为传递对准, 组合导航, 协同导航. E-mail: xubocarter@sina.com

    李盛新:哈尔滨工程大学智能科学与工程学院博士研究生. 主要研究方向为信息融合和协同定位. 本文通信作者. E-mail: lishengxin_lsx@163.com

    王连钊:哈尔滨工程大学智能科学与工程学院博士研究生. 主要研究方向为信息融合和协同定位. E-mail: 18804623593@163.com

    王权达:哈尔滨工程大学智能科学与工程学院硕士研究生. 2016年获得哈尔滨工程大学学士学位. 主要研究方向为水下机器人协同导航. E-mail: wqd1114393780@163.com

ANFIS-based Measurement Information Anomaly Detection Method for Multi-AUV Cooperative Localization System

Funds: Supported by National Natural Science Foundation of China (61203225), National Natural Science Foundation of China (61633008), Natural Science Foundation of Heilongjiang Province of China (F2018009), Heilongjiang province postdoctoral research start-up funding project (LBH-Q15032), Science and Technology on Underwater Information and Control Laboratory (614221801050717), the open project funding project of the State Key Laboratory for Marine Engineering (1616), Equipment Pre-research Foundation of China (61403110306)
More Information
    Author Bio:

    XU Bo Ph.D. associate professor at the College of Intelligent Systems Science and Engineering, Harbin Engineering University. He received his Ph.D.degree from Harbin Engineering University in 2011. His research interest covers transfer alignment, integrated navigation, and cooperative navigation

    LI Sheng-Xin Ph.D. candidate at the College of Intelligent Systems Science and Engineering Harbin Engineering University, His research interest covers information fusion, cooperative localization. Corresponding author of this paper

    WANG Lian-Zhao Ph.D. candidate at the College of Intelligent Systems Science and Engineering Harbin Engineering University, His research interest covers inertial navigation system, integrated navigation

    WANG Quan-Da Master student at the College of Intelligent Systems Science and Engineering, Harbin Engineering University. He received his bachelor degree from Harbin Engineering University in 2016. His research interest covers filtering algorithm and cooperative navigation

  • 摘要: 针对异常水声测距信息对多自主水下航行器(Autonomous underwater vehicles, AUV)协同定位系统的影响, 以及传统故障检测方法在多水声测距信息交替混淆的情况下检测效率低的问题, 本文提出了一种基于自适应神经模糊推理系统(Adaptive neuro-fuzzy inference system, ANFIS)的量测异常检测方法. 首先, 分别建立与各水声测距系统相对应的ANFIS模型; 然后, 通过自适应容积卡尔曼滤波和马氏距离构造能够反映量测异常的特征信息作为ANFIS的输入, 并基于预定义的量测异常信息建立初始混合数据库, 训练ANFIS模型实现对量测异常的在线实时检测与隔离. 最后, 利用湖水试验数据进行了AUV协同定位仿真验证, 实验结果表明该方法可以准确识别异常水声测距信息, 与传统故障检测方法相比误报率与漏检率均减少70%以上.
  • 图  1  ANFIS结构及优化过程

    Fig.  1  ANFIS structure and optimization process

    图  2  基于ANFIS的量测异常检测原理图

    Fig.  2  Schematic diagram of measurement anomaly detection based on ANFIS

    图  3  湖试试验所用勘测船

    Fig.  3  The survey vessel utilized in the lake field test.

    图  4  湖试设备

    Fig.  4  Equipment for lake testing

    图  5  试验船航行轨迹

    Fig.  5  Test ship sailing track

    图  6  领航AUV交替通信的标志位

    Fig.  6  Flag bit of leader AUV alternate ranging

    图  7  跟随AUV与各领航AUV之间水声测距信息

    Fig.  7  The underwater acoustic ranging information between the follower AUV and each leader AUV

    图  8  跟随AUV与领航AUV-1之间距离对比

    Fig.  8  Comparison of distance between follower AUV and leader AUV-1

    图  9  跟随AUV与领航AUV-2之间距离对比

    Fig.  9  Comparison of distance between follower AUV and leader AUV-2

    图  10  基于CKF和ACKF提取的特征信息对比

    Fig.  10  Comparison of feature information extracted from CKF and ACKF

    图  11  训练前后的隶属度函数对比

    Fig.  11  Comparison of membership functions before and after training

    图  12  测距误差与特征信息之间关系的三维曲线图

    Fig.  12  Three-dimensional plot of the relationship between ranging error and feature information

    图  13  ANFIS-1模型输出值和量测异常状态判别

    Fig.  13  The output value of ANFIS-1 model and the judgment of abnormal state of measurement

    图  14  ANFIS-2模型输出值和量测异常状态判别

    Fig.  14  The output value of ANFIS-2 model and the judgment of abnormal state of measurement

    图  15  分别基于卡方和双阈值的量测异常检测结果

    Fig.  15  Measurement anomaly detection results based on chi-square and dual thresholds respectively

    图  16  三种检测方法的性能对比

    Fig.  16  Performance comparison of three detection methods

    图  17  隔离量测异常估计跟随AUV轨迹

    Fig.  17  Estimated to follower AUV trajectory without measurement anomaly

    图  18  隔离量测异常估计跟随AUV定位误差

    Fig.  18  Estimated positioning errors of follower AUV without measurement anomaly

    图  19  通过不同方法得到的定位误差的CDF

    Fig.  19  The CDF of localization errors obtained by different methods

    表  1  传感器参数

    Table  1  Parameters of sensors

    传感器型号性能指标
    水声调制解调器ATM-885通信范围8000 m
    传输速率6.9 kbit/s
    GPSOEMV-2RT-2单点精度1.8 m (RMS)
    惯性导航系统H/H HZ001航向精度0.1%−0.3%
    DVLDS-99速度精度0.2 kn
    下载: 导出CSV

    表  2  各量测异常检测方法的TP、TN、FP、FN统计

    Table  2  The quantity statistics of TP, TN, FP and FN obtained by each measurement anomaly detection method

    量测异常检测方法TP数量TN数量FP数量FN数量
    残差卡方检测233881734
    双阈值检测343871823
    ANFIS检测47401410
    下载: 导出CSV
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  • 收稿日期:  2020-11-06
  • 修回日期:  2021-04-29
  • 网络出版日期:  2021-06-12

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