194 research outputs found
Wiometrics: Comparative Performance of Artificial Neural Networks for Wireless Navigation
Radio signals are used broadly as navigation aids, and current and future
terrestrial wireless communication systems have properties that make their
dual-use for this purpose attractive. Sub-6 GHz carrier frequencies enable
widespread coverage for data communication and navigation, but typically offer
smaller bandwidths and limited resolution for precise estimation of geometries,
particularly in environments where propagation channels are diffuse in time
and/or space. Non-parametric methods have been employed with some success for
such scenarios both commercially and in literature, but often with an emphasis
on low-cost hardware and simple models of propagation, or with simulations that
do not fully capture hardware impairments and complex propagation mechanisms.
In this article, we make opportunistic observations of downlink signals
transmitted by commercial cellular networks by using a software-defined radio
and massive antenna array mounted on a passenger vehicle in an urban non
line-of-sight scenario, together with a ground truth reference for vehicle
pose. With these observations as inputs, we employ artificial neural networks
to generate estimates of vehicle location and heading for various artificial
neural network architectures and different representations of the input
observation data, which we call wiometrics, and compare the performance for
navigation. Position accuracy on the order of a few meters, and heading
accuracy of a few degrees, are achieved for the best-performing combinations of
networks and wiometrics. Based on the results of the experiments we draw
conclusions regarding possible future directions for wireless navigation using
statistical methods
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