首页> 外文期刊>Mathematical Problems in Engineering: Theory, Methods and Applications >Huber's M-Estimation-Based Cubature Kalman Filter for an INS/ DVL Integrated System
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Huber's M-Estimation-Based Cubature Kalman Filter for an INS/ DVL Integrated System

机译:Huber基于M估计的用于INS/DVL集成系统的体积卡尔曼滤波

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摘要

To deal with the problems of outliers and nonlinearity in the complex underwater environment, a Huber's M-estimation-based cubature Kalman filter (CKF) is proposed for an inertial navigation system (INS)/Doppler velocity log (DVL) integrated system. First, a loosely coupled INS/DVL integrated system is designed, and the nonlinear system model is established in the case of big misalignment angle. Then, Huber's M-estimation is introduced for robust estimation to resist outliers. Meanwhile, the CKF is focused to handle the nonlinearity of the state equation. Finally, simulation and the vehicle test are conducted to evaluate the effectiveness of the proposed method. Results show that the proposed method outperforms the conventional Kalman filter (KF) and outlier detection-based robust cubature Kalman filter (RCKF) in terms of navigation accuracy in the complex underwater environment.
机译:针对复杂水下环境下的异常值和非线性问题,该文针对惯性导航系统(INS)/多普勒速度对数(DVL)集成系统,提出了一种基于M估计的Huber容积卡尔曼滤波(CKF)。首先,设计了松耦合INS/DVL集成系统,建立了大错位角情况下的非线性系统模型;然后,引入Huber的M估计进行鲁棒估计,以抵抗异常值。同时,CKF专注于处理状态方程的非线性。最后,通过仿真和整车试验对所提方法的有效性进行了评价。结果表明,所提方法在复杂水下环境下的导航精度优于传统的卡尔曼滤波(KF)和基于异常值检测的鲁棒容积卡尔曼滤波(RCKF)。

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