ANFIS-based Measurement Information Anomaly Detection Method for Multi-AUV Cooperative Localization System
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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)协同定位系统的不利影响, 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, 提出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, 基于自适应容积卡尔曼滤波(Adaptive cubature Kalman filter, ACKF)和马氏距离构造反映量测异常的特征信息作为ANFIS的输入; 其次, 基于预定义的量测异常信息建立了初始混合数据库以训练ANFIS模型实现对量测异常的在线实时检测与隔离; 最后, 利用湖水实验数据进行了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(False positive rate, FPR)与漏检率(False negative rate, FNR)均减少70%以上.
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关键词:
- 自主水下航行器 /
- 协同定位 /
- 自适应神经模糊推理系统 /
- 水声测距 /
- 量测异常
Abstract: In this paper, a measurement anomaly detection method based on adaptive neuro-fuzzy inference system (ANFIS) is proposed to address the problem that abnormal underwater acoustic ranging information has adverse effects on the multi autonomous underwater vehicles (AUV) cooperative localization system and the traditional fault detection methods have low detection efficiency when the multi acoustic ranging information is alternately confused. Firstly, the ANFIS model corresponding to each underwater acoustic ranging system is established. Secondly, the characteristic information reflecting the measurement anomaly, which is based on adaptive cubature Kalman filter (ACKF) and Mahalanobis distance, is used as the input of ANFIS. Then we established an initial hybrid database of pre-defined abnormal measurement information to train ANFIS model to realize online real-time detection and isolation of measurement anomalies. Finally, the lake test data are used to verify the AUV cooperative localization simulation. The experimental results show that this method can accurately identify the abnormal situation of measurement information, as the false positive rate (FPR) and the false negative rate (FNR) are reduced by more than 70% compared with the traditional fault detection method. -
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 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 17 隔离量测异常的AUV的估计轨迹
Fig. 17 Estimated trajectories of follower AUV that isolate measurement anomalies
图 18 隔离量测异常的AUV的定位误差
Fig. 18 Positioning errors of follower AUV that isolate measurement anomalies
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 GPS OEMV-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% DVL DS-99 速度精度 0.2 kn 
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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 23 388 17 34 双阈值检测 34 387 18 23 ANFIS检测 47 401 4 10
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