不同水深层的GNSS信号特征分析及其导航场景聚类划分
GNSS Signal Characteristics Analysis in Different Water Layers and Navigation Context Clustering
陈惟杰 1朱锋 2郭斐 1张小红1
作者信息
- 1. 武汉大学测绘学院,湖北 武汉,430079
- 2. 武汉大学测绘学院,湖北 武汉,430079;湖北珞珈实验室,湖北 武汉,430079
- 折叠
摘要
导航场景的划分对于自适应无缝导航定位服务具有重要意义.针对目前水上/水下导航场景划分简单、颗粒度不够的问题,通过采集位于不同水域内的全球导航卫星系统(global navigation satellite system,GNSS)观测数据,分析了不同水深层的GNSS观测信号质量与特征,结果表明,水下导航场景具有显著的分层性.考虑到导航场景具有分割、合并、联通等固有属性,通过K均值聚类将水下导航场景细分为浸水(＜6.5 cm)、浅水(6.5～8.5 cm)、深水(＞8.5cm)3层.利用多种GNSS信号特征进行了导航场景分类实验,结果表明,聚类正确率达到90.4％,验证了浸水、浅水、深水导航场景划分的有效性.
Abstract
Objective:Navigation context comprises observation environment and carrier activity.While global navigation satellite system(GNSS)can provide high-precision navigation,positioning,and timing services for users,users can also annotate the types of navigation scenarios they are in by distinguishing the quality and features of terminal observation values.Navigation context clustering is important for adaptive seamless navigation and location services,which can significantly promote context adaptability,configura-tion flexibility,and system robustness.Methods:In terms of current issues that the division of water/under-water context is simple and has insufficient granularity,the quality and characteristics of GNSS observation signals in different water depth layers are analyzed by collecting GNSS observations in different water re-gions.Results and Conclusions:The results show that the underwater navigation context has remarkable layering.Considering that the navigation context has inherent properties such as segmentation,merging,and connectivity,this paper subdivides the underwater navigation context into three layers:Submerged wa-ter(about less than 6.5 cm),shallow water(about greater than 6.5 cm and less than 8.5 cm),and deep wa-ter(about greater than 8.5 cm)through K-means clustering,and conducts navigation context classification experiments using various GNSS signal features.It reveals that the clustering accuracy rate is 90.4％,indi-eating the effectiveness of submerged-shallow-deep navigation context division.
关键词
GNSS/导航场景感知/水上水下场景/K均值聚类Key words
global navigation satellite system/navigation context awareness/water/underwater scene/K-means clustering引用本文复制引用
基金项目
国家重点研发计划(2020YFB0505803)
国家自然科学基金(42104021)
出版年
2024
NETL
NSTL
维普
万方数据