Aerial Vehicles Tracking Using Noncoherent Crowdsourced Wireless Networks

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, autoregressivebased 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.

Evolution of Non-Terrestrial Networks From 5G to 6G: A Survey

“…The proposed system relies on the RSS of UAV's Wi-Fi beacons captured by a crowd of terrestrial receivers to conduct a maximum likelihood estimation of the target UAV position. In [138] a time difference of arrival (TDoA)-based multilateration localization method of UAVs using a non-coherent CWN is proposed. In order to achieve the time synchronization needed for TDoA, an autoregressive synchronization method in companion with a Kalman filter is employed.…”

UAS Traffic Management Communications: The Legacy of ADS-B, New Establishment of Remote ID, or Leverage of ADS-B-Like Systems?

“…One more research in mixed traffic used ADS-B data to analyze the effects of lack in clock synchronization to the ATM. It provided solutions based on the availability of several trusted sensors in a large, uncoordinated network of UAVs [29]. In this context, the UAV functions as a relay airborne for ADS-B of manned flights.…”

Enabling Low-Overhead Over-the-Air Synchronization Using Online Learning

“…This trend can be seen in most recent processing paradigms, such as edge-computing [1], and federated learning [2] as well as networking paradigms, such as cell-free [3], and crowdsourced networks [4]. In order to realize the full potential of these paradigms, accurate network synchronization is needed as a key enabler for essential services such as coherent transmission [5], cooperative decoding [6], and localization [7]. While centralized networks can achieve high-precision synchronization via wired-based infrastructure or over-the-air (OTA) with acceptable overhead, distributed networks may not have wiredbased infrastructure due to cost and geographical constraints [7], and existing OTA synchronization solutions are unscalable for distributed networks due to the excessive overhead needed to synchronize significantly more devices when compared to centralized networks [4]- [8].…”

“…The model of digital clocks in wireless devices can be represented by a time-series process, i.e., discrete stochastic process [11]. An accurate clock model is a key enabler for clock drift prediction, which promises a reduced synchronization overhead by relying on the clock model to compensate for clock drifts, minimizing the frequency at which synchronization pilots are needed [6,7]. Recent state-of-the-art works addressed the digital clock modeling by using autoregressive models along with a Kalman filter [11] or by exploiting long short-term memory (LSTM)-based recurrent neural networks (RNNs) [7].…”