全球导航卫星系统应用
计算机科学
支持向量机
信号(编程语言)
多径传播
特征(语言学)
集合(抽象数据类型)
人工智能
模式识别(心理学)
卫星系统
特征向量
数据集
数据挖掘
实时计算
全球定位系统
电信
频道(广播)
哲学
程序设计语言
语言学
作者
Yuze Wang,Peilin Liu,Honggang Chen,Muhammad Adeel,Jiuchao Qian,Xiaoxi Jin,Rendong Ying
摘要
Statistical characteristics of signal reception conditions vary greatly in different types of environments. Hence, Global National Satellite System (GNSS) receivers must recognize surroundings for choosing the most suitable positioning methods in real time. Targeting vehicular positioning applications in a city, a novel environment recognition algorithm based only on the GNSS signal characteristics is proposed to distinguish between six distinct settings. To characterize different environmental interferences, a signal feature vector is built to represent the signal attenuation, blockage, and multipath. By training the classification model with labeled feature vectors, the support vector machine (SVM) algorithm is used to predict the scene type. A temporal filtering method is proposed to improve the accuracy. With advanced training of the model, this recognition method can work for the receiver in real time. To prove the extensive applicability of the proposed algorithm, the prediction data set and the training data set are collected in different cities. The testing results show overall recognition accuracy of 89.3% across different environments.
科研通智能强力驱动
Strongly Powered by AbleSci AI