Login / Signup

Leveraging Deep Learning for Practical DoA Estimation: Experiments with Real Data Collected via USRP.

Hyeonjin ChungHyunwoo ParkSunwoo Kim
Published in: Sensors (Basel, Switzerland) (2022)
This paper presents an experimental validation of deep learning-based direction-of-arrival (DoA) estimation by using realistic data collected via universal software radio peripheral (USRP). Deep neural network (DNN) and convolutional neural network (CNN) structures are designed to estimate the DoA. Two types of data are used for training networks. One is the data synthesized by the signal model, and the other is the data collected by USRP. Here, the signal model considers both mutual coupling and multipath signals. Experimental results show that the estimation performance is most accurate when training DNN and CNN with the collected data. Furthermore, the estimation tends to be poor in the indoor environment, which suffers from the strong non-line-of-sight (NLoS) signals.
Keyphrases
  • deep learning
  • convolutional neural network
  • electronic health record
  • big data
  • artificial intelligence
  • machine learning
  • air pollution
  • mass spectrometry
  • room temperature