Ensemble and Fuzzy Kalman Filter for position estimation of an autonomous underwater vehicle based on dynamical system of AUV motion

作者：

Highlights：

• Ensemble Kalman Filter (EnKF) algorithm can be applied to estimate AUV position.

• Fuzzy Kalman Filter (FKF) algorithm can be applied to estimate AUV position.

• The system dynamic of AUV motion is used for true trajectory of estimation.

• EnKF estimation is better than FKF estimation in AUV position estimation.

• Performance of each method based on RMSE and computational time.

摘要

•Ensemble Kalman Filter (EnKF) algorithm can be applied to estimate AUV position.•Fuzzy Kalman Filter (FKF) algorithm can be applied to estimate AUV position.•The system dynamic of AUV motion is used for true trajectory of estimation.•EnKF estimation is better than FKF estimation in AUV position estimation.•Performance of each method based on RMSE and computational time.

论文关键词：AUV,Ensemble Kalman Filter,Fuzzy Kalman Filter

论文评审过程：Received 19 July 2016, Revised 21 September 2016, Accepted 2 October 2016, Available online 4 October 2016, Version of Record 17 October 2016.

论文官网地址：https://doi.org/10.1016/j.eswa.2016.10.003