Recurrent neural network based scenario recognition with Multi-constellation GNSS measurements on a smartphone
作者: Yan XiaShuguo PanWang GaoBaoguo YuXingli GanYue ZhaoQing Zhao
作者单位: 1School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China
2Key Laboratory of Micro-Inertial Instrument and Advanced Navigation Technology, Ministry of Education, Nanjing 210096, China
3State Key Laboratory of Satellite Navigation System and Equipment Technology, Shijiazhuang 050081, China
4The 54th Research Institute of China Electronics Technology Group Corporation, Shijiazhuang 050081, China
5School of Transportation, Southeast University, Nanjing 210096, China
刊名: Measurement, 2020, Vol.153
来源数据库: Elsevier Journal
DOI: 10.1016/j.measurement.2019.107420
关键词: Scenario recognitionRecurrent Neural Network (RNN)Long Short-Term Memory (LSTM)Multi-constellationGNSS measurementsSmartphone
英文摘要: Abstract(#br)As an upper layer context-aware mobile application, fast and accurate scenario recognition is essential for seamless indoor and outdoor localization and robust positioning in complex environments. With the popularity of multi-constellation smartphones, scenario recognition based on smartphone Global Navigation Satellite System (GNSS) measurements becomes desirable. In this paper, we divide the complex environments into four categories (deep indoor, shallow indoor, semi-outdoor and open outdoor) and conduct research work in two areas. Firstly, we analyze in detail the influence of multi-constellation satellite signals on scenario recognition performance based on a Hidden Markov Model (HMM) algorithm. The experimental results show that the scenario recognition accuracy is...
全文获取路径: Elsevier  (合作)
影响因子:1.13 (2012)

  • constellation 星座
  • recognition 识别
  • GNSS Global Navigation Satellite System
  • scenario 剧情
  • neural 神经系统的
  • network 网络
  • based 基于
  • Network 网络