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Radio signals are used broadly as navigation aids, and current and futureterrestrial wireless communication systems have properties that make theirdual-use for this purpose attractive. Sub-6 GHz carrier frequencies enablewidespread coverage for data communication and navigation, but typically offersmaller bandwidths and limited resolution for precise estimation of geometries,particularly in environments where propagation channels are diffuse in timeand/or space. Non-parametric methods have been employed with some success forsuch scenarios both commercially and in literature, but often with an emphasison low-cost hardware and simple models of propagation, or with simulations thatdo not fully capture hardware impairments and complex propagation mechanisms.In this article, we make opportunistic observations of downlink signalstransmitted by commercial cellular networks by using a software-defined radioand massive antenna array mounted on a passenger vehicle in an urban nonline-of-sight scenario, together with a ground truth reference for vehiclepose. With these observations as inputs, we employ artificial neural networksto generate estimates of vehicle location and heading for various artificialneural network architectures and different representations of the inputobservation data, which we call wiometrics, and compare the performance fornavigation. Position accuracy on the order of a few meters, and headingaccuracy of a few degrees, are achieved for the best-performing combinations ofnetworks and wiometrics. Based on the results of the experiments we drawconclusions regarding possible future directions for wireless navigation usingstatistical methods.

Wiometrics: Comparative Performance of Artificial Neural Networks for Wireless Navigation