most citedCentimeter-Level Indoor Localization using Channel State Information with Recurrent Neural Networks

2 citations · 3 across the 5 of their papers we have counts for

collaborators

6 papers

eess.SP2022

Multi-Modal Recurrent Fusion for Indoor Localization

Jianyuan Yu, Pu, Wang +2

This paper considers indoor localization using multi-modal wireless signals including Wi-Fi, inertial measurement unit (IMU), and ultra-wideband (UWB). By formulating the localizat…

eess.SP2020

Predicting Bit Error Rate from Meta Information using Random Forests

Jianyuan Yu, Yue Xu, Hussein Metwaly Saad +1

With the increasing power of machine learning-based reasoning, the use of meta-information (e.g., digital signal modulation parameters, channel conditions, etc.) to predict the per…

eess.SP20201 cited

Direction of Arrival Estimation for a Vector Sensor Using Deep Neural Networks

Jianyuan Yu, William W. Howard, Daniel Tait +1

A vector sensor, a type of sensor array with six collocated antennas to measure all electromagnetic field components of incident waves, has been shown to be advantageous in estimat…

eess.SP20202 cited

Centimeter-Level Indoor Localization using Channel State Information with Recurrent Neural Networks

Jianyuan Yu, R. Michael Buehrer

Modern techniques in the Internet of Things or autonomous driving require more accuracy positioning ever. Classic location techniques mainly adapt to outdoor scenarios, while they…

eess.SP2020

Multiple Angles of Arrival Estimation using Neural Networks

Jianyuan Yu

MUltiple SIgnal Classification (MUSIC) and Estimation of signal parameters via rotational via rotational invariance (ESPRIT) has been widely used in super resolution direction of a…

eess.SP2020

Interference Classification Using Deep Neural Networks

Jianyuan Yu, Mohammad Alhassoun, R. Michael Buehrer

The recent success in implementing supervised learning to classify modulation types suggests that other problems akin to modulation classification would eventually benefit from tha…