摘要
针对扩展卡尔曼滤波(EKF)在自主水下航行器(AUV)移动声学网络协同导航中,存在强非线性观测方程条件下线性化误差大、计算复杂等缺点,文章提出一种基于无迹卡尔曼滤波(UKF)的AUV移动声学网络协同导航方法。利用移动长基线原理和UKF方法,建立基于UKF的协同导航滤波算法,并通过仿真实验与传统的EKF协同导航算法进行对比。仿真结果表明,基于UKF的协同导航算法能明显减小导航定位误差,在导航精度上优于EKF方法,是AUV协同导航中一种更加简单有效的导航滤波方法。
The core of the introduction of the full paper consists of： in view of the defects such as great linearization error and high calculating complexity which exist when the extended Kalman filter（EKF） is applied in cooperative navigation method of AUV（ autonomous underwater vehicles） in a network of moving acoustic beacons, a new meth- od based on unscented Kalman filter （UKF）, which we believe is more effective than those already available, is proposed. Our new method is explained in sections 1 and 2. Section 1 briefs the experimentally validated MLBL （ moving long baseline） concept proposed by Jirome Vagenay et al in Ref. 7. Section 2 is entitled ＂Implementation of Our UKF Based Filtering Algorithm＂. Subsection 2. 1 establishes its kinematic model. Subsection 2. 2 imple- ments our algorithm using MLBL concept and UKF; it endsup with a 3-step detailed procedure for performing itera- tire calculations. Simulation results, presented in Fi~. 4 and Table 1 and comparing UKF with traditional EKF, show preliminarily that UKF operation can suppress the errors of navigation positions and it performs better in navigation precision, therefore, it is indeed a more effective filter for AUV cooperative navigation.
出处
《西北工业大学学报》
EI
CAS
CSCD
北大核心
2011年第6期934-938,共5页
Journal of Northwestern Polytechnical University
基金
自然科学基金(509790935
1179156
51109179)
航空科学基金(2011ZC53043)资助
关键词
无迹卡尔曼滤波
移动声网络
协同导航
AUV
algorithms, auonomous underwater vehicles, calculations, computational complexity, errors, iterativemethods, Kalman filtering, kinematics, models, navigation, nonlinear iystems, simulation, underwa-ter acoustics
cooperative navigation, moving acoustic network
