Aerial Vehicles Tracking Using Noncoherent Crowdsourced Wireless Networks

TL;DR

This paper presents a novel air traffic management system using crowdsourced wireless networks, addressing synchronization issues and anti-spoofing, with real-world data demonstrating accuracy comparable to GPS-synchronized sensors.

Contribution

It introduces methods to correct clock-synchronization in noncoordinated sensor networks for UAV tracking, combining MLAT and Kalman filtering for anti-spoofing.

Findings

Achieves localization accuracy comparable to GPS-synchronized sensors.

Outperforms existing CWN synchronization methods.

Demonstrates effectiveness with real-world CWN data.

Abstract

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.