  Air traffic management (ATM) of manned and unmanned aerial vehicles (AVs)
relies critically on ubiquitous location tracking. While technologies exist for
AVs to broadcast their location periodically and for airports to track and
detect AVs, methods to verify the broadcast locations and complement the ATM
coverage are urgently needed, addressing anti-spoofing and safe coexistence
concerns. In this work, we propose an ATM solution by exploiting noncoherent
crowdsourced wireless networks (CWNs) and correcting the inherent
clock-synchronization problems present in such non-coordinated sensor networks.
While CWNs can provide a great number of measurements for ubiquitous ATM, these
are normally obtained from unsynchronized sensors. This article first presents
an analysis of the effects of lack of clock synchronization in ATM with CWN and
provides solutions based on the presence of few trustworthy sensors in a large
non-coordinated network. Secondly, autoregressive-based and long short-term
memory (LSTM)-based approaches are investigated to achieve the time
synchronization needed for localization of the AVs. Finally, a combination of a
multilateration (MLAT) method and a Kalman filter is employed to provide an
anti-spoofing tracking solution for AVs. We demonstrate the performance
advantages of our framework through a dataset collected by a real-world CWN.
Our results show that the proposed framework achieves localization accuracy
comparable to that acquired using only GPS-synchronized sensors and outperforms
the localization accuracy obtained based on state-of-the-art CWN
synchronization methods.
