Treatment Method for Multi-AUV Cooperative Positioning Underwater Acoustic Propagation Delay Based on IMM Algorithm
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摘要: 针对主从式自主水下航行器(AUV)协同定位系统存在的水声探测与通信的延迟问题, 提出一种基于交互式多模型(IMM)算法的时间延迟处理方法。首先建立多AUV系统的协同定位计算模型, 针对系统非线性运动方程与非线性量测, 分析扩展卡尔曼滤波(EKF)协同定位结果因水声信号传播延迟产生的定位误差; 其次阐述常规延迟扩展卡尔曼滤波(DEKF)在处理时间延迟环节中无法实现对机动性目标AUV运动状态的精准跟踪问题; 最终设计IMM-DEKF算法, 选择适当的运动模型作为子滤波器, 利用新息更新模型概率, 精确跟踪主AUV运动状态, 降低从AUV滤波器中对主AUV状态值的估计误差, 实现整体协同系统定位精度的提高。仿真结果验证了所提算法在常规EKF的基础上有效提高了从AUV滤波器对主AUV航迹预测精度, 使得协同定位系统的整体定位精度得到提升。Abstract: To address the delay problem of underwater acoustic detection and communication in a master-slave autonomous undersea vehicle(AUV) cooperative positioning system, a time-delay processing method based on an interacting multiple model(IMM) algorithm is proposed. First, a cooperative positioning calculation model for a multi-AUV system is established. Aiming at the nonlinear motion equation and nonlinear measurement of the system, the positioning error caused by the propagation delay of underwater acoustic signal in the cooperative positioning result of extended Kalman filter(EKF) is analyzed. Second, the problem that delay EKF(DEKF) cannot accurately track the motion state of the maneuverable target AUV in handling time delay is described. Finally, the IMM-DEKF algorithm is designed, the appropriate motion models are selected as the sub filters, the model probability is updated via innovation, the motion states of the main AUVs are accurately tracked, the estimation errors of the state value of the main AUVs from the slaver AUV’s filter are reduced, and the positioning accuracy of the overall cooperative system is improved. The simulation results verify that the proposed algorithm effectively improves the prediction accuracy of the main track of the AUVs from the slave AUV filter based on the conventional EKF and improves the overall positioning accuracy of the cooperative positioning system.
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图 1 主从式AUV协同定位方式示意图
Figure 1. Diagram of master-slave AUV cooperative positioning method
图 7 多场景下从AUV定位距离RMSE曲线
Figure 7. RMSE curves of slaver AUV positioning distance in multi-scene
图 8 DEKF与IMM-DEKF算法下的从AUV距离RMSE曲线对比
Figure 8. Comparison of RMSE curves from slaver AUV between DEKF and IMM-DEKF algorithms
图 9 IMM-DEKF算法下AUV模型概率更新过程
Figure 9. Model probability update processes of AUV in IMM-DEKF algorithm
图 10 DEKF与IMM-DEKF算法下主AUV1航迹预测距离RMSE曲线
Figure 10. RMSE curves of track prediction distance from master AUV1 under DEKF and IMM-DEKF algorithms
图 11 DEKF与IMM-DEKF算法下主AUV2航迹预测距离RMSE曲线
Figure 11. RMSE curves of track prediction distance of master AUV2 under DEKF and IMM-DEKF algorithms
表 1 [1 000 s, 1 500 s]时段滤波状态误差平均值
Table 1. Average values of state errors in [1 000 s, 1 500 s]
AUV [x, y, d]/m IMM-DEKF DEKF 主AUV1 [4.27, 6.83, 8.06] [7.06, 11.90, 13.85] 主AUV2 [6.83, 4.20, 8.04] [11.89, 6.90, 13.75] 从AUV [5.95, 2.71, 6.58] [5.36, 7.50, 9.36] 
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表 2 [1 500 s, 2 000 s]时段滤波状态误差平均值
Table 2. Average values of state errors in [1 500 s, 2 000 s]
AUV [x, y, d]/m IMM-DEKF DEKF 主AUV1 [2.46, 2.28, 3.39] [2.16, 1.88, 2.90] 主AUV2 [2.20, 2.32, 3.23] [2.08, 2.21, 3.08] 从AUV [3.52, 2.82, 4.52] [3.51, 3.14, 4.75]
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表 3 [2 000 s, 2 500 s]时段滤波状态误差平均值
Table 3. Average values of state errors in [2 000 s, 2 500 s]
AUV [x, y, d]/m IMM-DEKF DEKF 主AUV1 [4.32, 7.05, 8.27] [7.26, 12.23, 14.23] 主AUV2 [6.62, 4.22, 7.86] [12.09, 7.20, 14.07] 从AUV [4.98, 3.78, 6.30] [6.39, 7.26, 9.69]
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表 4 IMM-DEKF算法下各AUV距离误差下降百分比
Table 4. Percentage decrease in distance error of each AUV
AUV 距离误差/% [1 000 s, 1 500 s] [2 000 s, 2 500 s] 主AUV1 41.81 41.88 主AUV2 41.53 44.14 从AUV 29.70 34.98
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