Adaptive Unscented Kalman Filter for Deep-sea Tracked Vehicle Localization

摘要：

This paper introduces a kinematic model of deep-sea mining vehicle in presence of sliding parameters. The model describes both the noises features of sliding parameters and the deep-sea condition features. To handle sliding parameters noises, a recursive algorithm to minimize difference between the filter-computed and the actual innovation covariance is adopted, which is a novel integrated navigation method based on unscented Kalman filters (UKF). Taking into account the influence of measurement data delay, UKF fuses the localization information of long base line (LBL) sonar localization system and dead-reckoning (DR) to perform the state estimation. Simulation results show that the adaptive UKF has better localization estimation than a normal UKF for deep-sea tracked vehicle (DTV).

作者: Hongqian Zhu Huosheng Hu Weihua Gui

作者单位: College of Information Science and Engineering,Central South University,Changsha,410083 China College of Information Science and Engineering,Central South University,410083 China

会议类型: 国际会议

会议名称: 2009 IEEE International Conference on Information and Automation(2009年 IEEE信息与自动化国际学术会议)

会议地点: 珠海、澳门

会议语种:英文

页码: 1056-1061

在线出版日期: 2009-06-22（万方平台首次上网日期，不代表论文的发表时间）