no code implementations • 1 Sep 2022 • Jianyuan Yu, William W. Howard, Yue Xu, R. Michael Buehrer
Model order estimation (MOE) is often a pre-requisite for Direction of Arrival (DoA) estimation.
no code implementations • 19 Feb 2022 • Jianyuan Yu, Pu, Wang, Toshiaki Koike-Akino, Philip V. Orlik
This paper considers indoor localization using multi-modal wireless signals including Wi-Fi, inertial measurement unit (IMU), and ultra-wideband (UWB).
no code implementations • 28 Dec 2021 • Jianyuan Yu, Pu, Wang, Toshiaki Koike-Akino, Ye Wang, Philip V. Orlik, R. Michael Buehrer
The granularity matching is realized by pairing two feature maps from the CSI and beam SNR at different granularity levels and linearly combining all paired feature maps into a fused feature map with learnable weights.
no code implementations • 10 Jul 2020 • Jianyuan Yu, Yue Xu, Hussein Metwaly Saad, R. Michael Buehrer
With the increasing power of machine learning-based reasoning, the use of meta-information (e. g., digital signal modulation parameters, channel conditions, etc.)
no code implementations • 12 Apr 2020 • Jianyuan Yu, William W. Howard, Daniel Tait, R. Michael Buehrer
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 estimating the angle of arrival and polarization of the incident sources.
no code implementations • 4 Feb 2020 • Jianyuan Yu, R. Michael Buehrer
Modern techniques in the Internet of Things or autonomous driving require more accuracy positioning ever.
no code implementations • 3 Feb 2020 • 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 arrival estimation (DoA) in both Uniform Linear Arrays (ULA) or Uniform Circular Arrays (UCA).
no code implementations • 3 Feb 2020 • 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 that implementation.