A Fault-Tolerant Navigation Algorithm for AUV Based on Collaborative Fault Detection and Robust Estimation
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摘要: 自主水下航行器(AUV)组合导航系统在面对多普勒测速仪(DVL)缓变故障时, 传统自适应滤波算法因噪声估计与故障检测机制相互冲突, 难以实现有效容错。为此, 文中提出一种融合长短期记忆网络(LSTM)故障检测与变分贝叶斯自适应卡尔曼滤波(VBAKF)/IGG-III抗差滤波的协同容错导航方法。该方法通过LSTM网络实现对缓变故障早期特征的有效识别; 在确认故障后将滤波器由VBAKF切换至IGG-III抗差滤波模式, 动态构造等价权矩阵以抑制故障量测影响; 故障结束后恢复VBAKF以维持最优估计。实验结果表明, 在DVL发生缓变故障时, 所提方法的导航精度优于几种主流滤波算法, 有效抑制了状态估计失真, 提升了AUV组合导航系统在不确定水下环境中的精度和鲁棒性。
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关键词:
- 自主水下航行器 /
- 容错导航算法 /
- 长短期记忆网络 /
- 变分贝叶斯自适应卡尔曼滤波 /
- IGG-III抗差估计
Abstract: When facing progressive faults in the Doppler velocity log (DVL), traditional adaptive filters fail to provide effective fault tolerance in autonomous underwater vehicle(AUV) integrated navigation systems due to conflicts between noise estimation and fault detection. To address this issue, this paper proposes a collaborative fault-tolerant navigation method that integrates long short-term memory (LSTM) networks for fault detection with variational bayesian adaptive Kalman filter (VBAKF) and IGG-III robust filtering. The proposed approach utilizes LSTM networks to effectively identify early characteristics of progressive faults. Upon fault confirmation, the filter switches from VBAKF to IGG-III robust filtering mode, dynamically adjusting weights to mitigate fault impact. Normal operation resumes using VBAKF after fault resolution. Experimental results demonstrate that, in the event of DVL progressive faults, the proposed method achieves higher navigation accuracy than several mainstream filtering algorithms, effectively suppresses state estimation distortion, and enhances the precision and robustness of AUV integrated navigation systems in uncertain underwater environments. -
图 10 自适应滤波算法故障检测值曲线
Figure 10. Fault detection value curve of adaptive filtering algorithm
表 1 故障类型
Table 1. Type of faults
故障类型 数学模型 突变故障 $f(t)=5 \times r_0 \times n(t) $ 线性缓变故障 $f(t)=\alpha\left(t-t_0\right) \times n(t) $ 二次方缓变故障 $ f(t)=\alpha\left(t-t_0\right)^2 \times n(t) $ 周期性缓变故障 $ f(t)=\alpha \times \sin \left(t-t_0\right) \times n(t) $ 对数型缓变故障 $ f(t)=\alpha \times \ln \left(1+\left(t-t_0\right)\right) \times n(t) $ 
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表 2 LSTM模型测试集评价指标
Table 2. Evaluation index of LSTM model test set
故障类型 精确率 召回率 F1分数 系统正常 0.976 6 0.967 2 0.971 9 突变故障 0.956 3 0.985 0 0.970 4 线性故障 0.858 9 0.843 2 0.851 0 二次方故障 0.902 8 0.910 7 0.906 7 周期性故障 0.987 4 0.993 7 0.990 5 对数型故障 0.916 1 0.902 5 0.909 3 算数平均 0.933 0 0.933 7 0.933 3 加权平均 0.933 4 0.933 7 0.933 5
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表 3 算法结果对比
Table 3. Algorithm result comparison
算法 东向速度误差/(m/s) 北向速度误差/(m/s) 经度误差/m 纬度误差/m 航向角误差(′) KF 0.1 121 0.1 134 44.98 74.34 0.18 RKF 0.0 548 0.0 487 16.55 25.45 0.13 SHAKF 0.0 608 0.0 440 19.44 24.87 0.11 VBAKF 0.0 352 0.0 153 15.86 22.82 0.09 LSTM-IGG-VBAKF 0.0 210 0.0 091 8.17 18.45 0.12
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