Application of state estimation algorithm for UUV docking
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摘要: 无人水下航行器(UUV)水下自主动态对接是实现其远航程协同作业的关键技术之一。针对UUV水下动态对接中对对接装置运动状态估计不足的问题, 提出交互多模型自适应无迹卡尔曼滤波(IMM-AUKF)进行运动状态估计的方法。考虑对接中UUV自身传感器获取的对接装置运动状态量测误差较大, 建立了UUV非线性观测模型, 采用自适应无迹卡尔曼滤波(AUKF)算法在线实时更新观测噪声以降低观测误差; 考虑单运动模型对UUV和对接装置相对运动描述困难, 建立了UUV与对接装置相对运动模型集, 采用交互多模型算法描述UUV与对接装置相对运动以提高滤波精度。通过UUV对接试验数据, 进行UKF、AUKF和IMM-AUKF算法对对接装置运动状态估计结果对比, 结果表明IMM-AUKF算法对对接装置运动状态估计具有较高的精确性和稳定性, 可满足水下动态对接需求, 提高UUV对接成功率。Abstract: Underwater autonomous dynamic docking of unmanned undersea vehicle (UUV) is one of the key technologies for achieving long-range cooperative of UUV. Aiming at the problem of insufficient estimation of the motion state of the docking device in docking, an interacting multi-model adaptive unscented Kalman filter method is proposed to estimate the docking device motion state. Considering that the measurement error of the motion state of the docking device obtained by the UUV sensor is large, the UUV nonlinear observation model is established, and the adaptive unscented Kalman filter (AUKF) algorithm is used to update the observation noise model in real time to reduce the observation error. Considering that the difficulty of the describing the relative motion of UUV and docking device with a single model, the motion state model set of the docking device is established, and the interactive multi-model algorithm is used to describe the motion state of the docking device to improve the filtering accuracy and realize the accurate estimation of the motion state of the docking device by UUV in underwater docking. Based on UUV docking test data, the estimation results of unscented Kalman filter, adaptive unscented Kalman filter and interacting multi-model adaptive unscented Kalman filter are compared. The results show that the accuracy and stability of interacting multi-model adaptive unscented Kalman filter are better than the other two algorithms. It can be applied to underwater autonomous docking scenarios with UUV to improve the success rate.
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图 2 NED地理参考坐标系和运载体坐标系相对位置
Figure 2. Relative position of navigation coordinate system and carrier coordinate system
图 9 UUV与对接装置相对运动轨迹
Figure 9. The relative motion trajectory of the UUV and the docking device
表 1 动态、静态对接状态估计平均RMSE
Table 1. Average RMS error of dynamic and static docking state estimation
对接类型 平均RMSE 距离R/m 角度$ \delta $/° 静态对接AUKF 0.109 7 0.016 9 动态对接AUKF 0.131 0 0.017 1 
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表 2 各算法平均RMSE
Table 2. The average RMSE of each algorithm
算法类型 平均RMSE 距离$\rho $/m 角度$ \delta $/° UKF 0.3187 0.2812 AUKF 0.0553 0.2218 IMM-AUKF 0.0470 0.2206
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表 3 各算法峰值RMSE
Table 3. Peak RMSE of each algorithm
算法类型 峰值RMSE 距离$\rho $/m 角度$\delta $/° UKF 2.106 1.106 AUKF 0.314 0.872 IMM-AUKF 0.350 0.872
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