TY - GEN
T1 - Application for autonomous underwater vehicle initial alignment with divided difference filter
AU - Zhao, Guiling
AU - Gao, Wei
AU - Zhang, Xin
AU - Ben, Yueyang
PY - 2010
Y1 - 2010
N2 - Regarding the problem of the Autonomous Underwater Vehicle (AUV) departing from the mother ship and starting up, this paper proposed a divided difference filter (DDF) basing on the Stirling interpolation formula. When AUV separated from the mother ship and restarted, the coarse alignment was not ideal. Its level misalignment error precision was high, but its heading misalignment error precision was low. The large heading misalignment error model was applied to the fine alignment on moving base. The large heading misalignment error model was nonlinear. The first order divided difference (DD1) filter and the second order divided difference (DD2) filter were adopted to the nonlinear filtering. Simulation results show that the DD1 alignment precision is equivalent with Extended Kalman Filter (EKF), but the alignment time shorts by 33%. The DD2 alignment precision is equivalent with Unscented Kalman Filter (UKF), but the alignment time shorts by 28%. The alignment precision raises by 60% compared to the EKF.
AB - Regarding the problem of the Autonomous Underwater Vehicle (AUV) departing from the mother ship and starting up, this paper proposed a divided difference filter (DDF) basing on the Stirling interpolation formula. When AUV separated from the mother ship and restarted, the coarse alignment was not ideal. Its level misalignment error precision was high, but its heading misalignment error precision was low. The large heading misalignment error model was applied to the fine alignment on moving base. The large heading misalignment error model was nonlinear. The first order divided difference (DD1) filter and the second order divided difference (DD2) filter were adopted to the nonlinear filtering. Simulation results show that the DD1 alignment precision is equivalent with Extended Kalman Filter (EKF), but the alignment time shorts by 33%. The DD2 alignment precision is equivalent with Unscented Kalman Filter (UKF), but the alignment time shorts by 28%. The alignment precision raises by 60% compared to the EKF.
KW - Autonomous underwater vehicle
KW - Divided difference filter
KW - Large heading misalignment error
KW - Nonlinear filtering
UR - https://www.scopus.com/pages/publications/77955730309
U2 - 10.1109/ICINFA.2010.5512290
DO - 10.1109/ICINFA.2010.5512290
M3 - 会议稿件
AN - SCOPUS:77955730309
SN - 9781424457021
T3 - 2010 IEEE International Conference on Information and Automation, ICIA 2010
SP - 1352
EP - 1356
BT - 2010 IEEE International Conference on Information and Automation, ICIA 2010
T2 - 2010 IEEE International Conference on Information and Automation, ICIA 2010
Y2 - 20 June 2010 through 23 June 2010
ER -