2 research outputs found
Direction of Arrival Estimation for a Vector Sensor Using Deep Neural Networks
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. While angle estimation with machine learning for linear
arrays has been well studied, there has not been a similar solution for the
vector sensor. In this paper, we propose neural networks to determine the
number of the sources and estimate the angle of arrival of each source, based
on the covariance matrix extracted from received data. Also, we provide a
solution for matching output angles to corresponding sources and examine the
error distributions with this method. The results show that neural networks can
achieve reasonably accurate estimation with up to 5 sources, especially if the
field-of-view is limited
Centimeter-Level Indoor Localization using Channel State Information with Recurrent Neural Networks
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 do not meet the requirement of indoor cases with
multiple paths. Meanwhile as a feature robust to noise and time variations,
Channel State Information (CSI) has shown its advantages over Received Signal
Strength Indicator (RSSI) at more accurate positioning. To this end, this paper
proposes the neural network method to estimate the centimeter-level indoor
positioning with real CSI data collected from linear antennas. It utilizes an
amplitude of channel response or a correlation matrix as the input, which can
highly reduce the data size and suppress the noise. Also, it makes use of the
consistency in the user motion trajectory via Recurrent Neural Network (RNN)
and signal-noise ratio (SNR) information, which can further improve the
estimation accuracy, especially in small datasize learning. These contributions
all benefit the efficiency of the neural network, based on the results with
other classic supervised learning methods