An Adaptive Square-Root Unscented Kalman Filter for Underwater Vehicle Navigation

被引：0
|
作者：
Lin, Yanping [1 ,2 ]
Liu, Kaizhou [2 ]
Wang, Xiulian [1 ]
机构：
[1] Shenyang Ligong Univ, Sch Informat Sci & Engn, Shenyang, Liaoning Provin, Peoples R China
[2] Shenyang Inst Automat, State Key Lab Robot, Shenyang, Liaoning Provin, Peoples R China
来源：
2014 IEEE INTERNATIONAL CONFERENCE ON MECHATRONICS AND AUTOMATION (IEEE ICMA 2014) | 2014年
关键词：
adaptive square-root unscented Kalman filter (ASRUKF); modified Sage-Husa noise statistics estimator; underwater vehicle navigation; Human Occupied Vehicle (HOV);
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In order to increase the approximation accuracy of the state estimate of nonlinear systems and to guarantee numerical stability of the unscented Kalman filter (UKF), a novel adaptive square-root unscented Kalman filter (ASRUKF) based on modified Sage-Husa noise statistics estimator is proposed. The new adaptive filter method with adaptability to statistical characteristic of noise is able to compensate the lack of a priori knowledge of the system's noise statistics. A six-degree-offreedom dynamic model is introduced to denote the motion model of Human Occupied Vehicle (HOV) in the water, while the adaptive SRUKF is employed for off-line estimation of the state of HOV. Tests are conducted with respect to the data obtained from previous sea trial, and the results are compared with those obtained by normal UKF and SRUKF to indicate its effectiveness and improvements.
引用
收藏
页码：717 / 722
页数：6
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