基于交互式多模型自适应滤波AUV定位算法
AUV localization algorithm based on interactive multi-model adaptive filtering
谢思雅 1魏连锁 2孙强 1曹望成1
作者信息
- 1. 牡丹江师范学院计算机与信息技术学院,黑龙江牡丹江 157000
- 2. 宿迁学院信息工程学院,江苏宿迁 223800
- 折叠
摘要
为实现自主水下航行器(AUV)有效定位,针对现有单模型滤波定位算法滤波误差大、定位精度低等问题,提出一种基于交互式多模型自适应滤波AUV定位算法(IMAF).该算法采用平滑高斯-半马尔可夫模型描述AUV运动状态,结合AUV动力学状态方程构建机动和非机动交互式运动模型;再利用平滑高斯-半马尔可夫模型状态转移概率实现AUV不同运动状态的转换进而实现自适应定位.仿真实验表明,所提算法与基于条件最小化非线性滤波算法、单模型的扩展卡尔曼滤波算法和卡尔曼滤波算法相比,AUV定位精度分别提升了 53.6％,62.1％和83.5％.
Abstract
In order to achieve effective positioning of autonomous underwater vehicle(AUV),an interactive multi-model adaptive filtering AUV positioning algorithm was proposed to solve the problems of large filtering error and low positioning accuracy of existing single-model filtering positioning algorithm.The smooth Gauss-semi-Markov model was used to describe the motion state of AUV,and the linear and nonlinear interactive motion models were constructed by combining the dynamic state equations of AUV.The state transition probability of the smooth Gauss-semi-Markov model is used to realize the transformation of different motion states of AUV and then realize the adaptive positioning.Simulation experiments show that compared with the nonlinear filtering algorithm based on conditional minimization,the Extended Kalman Filtering algorithm based on single model and the Kalman Filtering algorithm,the proposed algorithm in this paper can improve the positioning accuracy of AUV by 53.6％,62.1％and 83.5％.
关键词
自主水下航行器/卡尔曼滤波/交互式多模型/定位算法Key words
autonomous underwater vehicle/Kalman Filter/interactive multi-model/location algorithm引用本文复制引用
基金项目
黑龙江省教育厅项目资助(1453QN019)
黑龙江省教育厅项目资助(1453ZD033)
2023年度新时代龙江优秀硕士、博士学位论文项目(ljyxl2023-086)
黑龙江省经济社会发展重点研究课题(22356)
黑龙江省高等教育学会高等教育研究课题(23GJYBF099)
出版年
2024
NETL
NSTL
万方数据