[1]韩月,陈鹏云,沈鹏.基于改进粒子滤波的AUV海底地形辅助定位方法[J].智能系统学报,2020,15(3):553-559.[doi:10.11992/tis.201903027]
 HAN Yue,CHEN Pengyun,SHEN Peng.Seabed terrain-aided positioning method based on improved particle filtering for AUVs[J].CAAI Transactions on Intelligent Systems,2020,15(3):553-559.[doi:10.11992/tis.201903027]
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基于改进粒子滤波的AUV海底地形辅助定位方法(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第15卷
期数:
2020年3期
页码:
553-559
栏目:
学术论文—机器学习
出版日期:
2020-05-05

文章信息/Info

Title:
Seabed terrain-aided positioning method based on improved particle filtering for AUVs
作者:
韩月12 陈鹏云2 沈鹏3
1. 太原旅游职业学院 现代教育信息中心,山西 太原 030032;
2. 中北大学 机电工程学院,山西 太原 030051;
3. 国家深海基地管理中心,山东 青岛 266237
Author(s):
HAN Yue12 CHEN Pengyun2 SHEN Peng3
1. Modern Education Information Centre, Taiyuan Tourism College, Taiyuan 030032, China;
2. College of Mechatronic Engineering, North University of China, Taiyuan 030051, China;
3. National Deep Sea Centre, Qingdao 266237, China
关键词:
水下无人航行器水下环境多波束测深地形辅助定位Bayesian估计粒子滤波辅助采样半物理仿真
Keywords:
autonomous underwater vehicleunderwater environmentmulti-beam soundingterrain-aided positioningBayesian estimationparticle filterauxiliary samplingsemi-physical simulation
分类号:
TP24
DOI:
10.11992/tis.201903027
摘要:
针对自主式水下无人航行器的地形辅助导航问题,提出一种基于粒子滤波的地形辅助定位方法。为了解决粒子滤波的“粒子贫化”问题,引入了辅助采样,提出一种基于辅助采样粒子滤波的海底地形辅助定位方法,减小了由于重采样带来的粒子多样性的损失。基于半物理测试平台的仿真实验表明:本文所提出方法的精度较高,可适应不同地形特征下的地形辅助定位,可满足水下无人航行器(autonomous underwater vehicle, AUV) 水下导航定位的需求。
Abstract:
Focusing on the seabed terrain-aided navigation of autonomous underwater vehicle (AUV), a terrain-aided positioning method based on the particle filtering method is proposed in this study. To solve the particle depletion problem of the particle filtering method, the auxiliary sampling technology is introduced. Then, a terrain-aided positioning method based on the auxiliary sampling particle filtering method, which can reduce the loss of particle diversity caused by resampling, is proposed. Simulation tests based on the semi-physical test platform show that the proposed method has high terrain positioning accuracy and strong adaptability to terrain features, which can meet the demand of AUV navigation.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2019-03-21。
基金项目:国家自然科学基金项目(51909245);山西省自然科学基金项目(201901D211244,201801D221210);山西省高等学校科技创新项目(2019L0537);高性能舰船技术教育部重点实验室基金项目(gxnc19051802)
作者简介:韩月,助教,主要研究方向为计算机人工智能技术、视景仿真技术;陈鹏云,副教授,博士,主要研究方向为无人系统的自适应控制技术、地球物理导航技术。主持国家自然科学基金青年基金项目1项、省部级项目多项。发表学术论文20余篇;沈鹏,工程师,主要研究方向为水下机器人作业技术、地球物理导航技术
通讯作者:陈鹏云.E-mail:chenpengyun@hrbeu.edu.cn
更新日期/Last Update: 1900-01-01