一种基于改进粒子滤波的水下采矿导航方法  

An underwater mining navigation method based on an improved particle filter

作　　者：张志慧 冯迎宾[1,2] 李智刚[1,2] 赵小虎 ZHANG Zhihui;FENG Yingbin;LI Zhigang;ZHAO Xiaohu(State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China;Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China;University of Chinese Academy of Sciences, Beijing 100049, China)

机构地区：[1]中国科学院沈阳自动化研究所机器人学国家重点实验室,沈阳110016 [2]中国科学院机器人与智能制造创新研究院,沈阳110169 [3]中国科学院大学,北京100049

出　　处：《中国科学院大学学报（中英文）》2020年第4期507-515,共9页Journal of University of Chinese Academy of Sciences

基　　金：National Key Research and Development Program of China(2016YFC0304102-6)。

摘　　要：针对水下采矿导航系统所面临的噪声具有非高斯性和频率随机性的问题,提出基于粒子滤波的深海采矿导航算法,并针对粒子滤波的粒子退化和贫化提出一种新的重采样算法。结合湖试数据,仿真实验表明新的重采样算法在获得更好的滤波精度的同时可以避免粒子贫化现象。最后,将基于改进的粒子滤波的深海采矿导航算法与基于无迹卡尔曼滤波算法的导航算法进行对比。结果表明本文提出的算法具有较高的精度和优良的鲁棒性。An underwater mining navigation method based on an improved particle filter(PF)is proposed to solve the problems of non-Gaussian and intense measurement noise during underwater mining,and a new resampling algorithm is designed,as an improvement,to eliminate the influences of particle degeneration and particle impoverishment of PF.Compared to the resampling algorithms,the proposed algorithm avoids particle impoverishment and improves estimation accuracy.Finally,the estimation accuracies of underwater mining navigation algorithms based on the improved PF and the unscented Kalman filter(UKF)are compared by combining the lake trial data and underwater mining navigation model.The results of simulation experiments manifest that the proposed method has more accurate estimation and remarkable robustness.

关 键 词：粒子滤波 重采样 水下采矿导航 粒子退化 粒子贫化 

分 类 号：P751[交通运输工程—港口、海岸及近海工程]