Shaghayegh Shahcheraghi

This letter presents the first stationary user equipment (UE)-based positioning framework that blindly estimates both always-on and on-demand 5G downlink signals with multiple 5G gNBs, transmitting on the same channel. The framework opportunistically exploits the full bandwidth of downlink 5G signals. In the acquisition stage, the maximum likelihood function of the beamformed signal is computed to differentiate the gNBs in both carrier frequency offset and angular domains, followed by blind beacon estimation of each gNB. Carrier-aided code delay tracking loops are developed to track the carrier phase and code delay to extract navigation observables. The Cram´ er–Rao lower bounds (CRLBs) for carrier frequency offset, azimuth, and elevation angles are derived. Experimental results are presented demonstrating that the estimated beacon exhibits a narrower autocorrelation function compared to that of always-on signals, due to the exploitation of on-demand signals, leading to higher time resolution estimation. Monte Carlo (MC)–based positioning results for a stationary UE using pseudorange and azimuth angle measurements from two co-channel 5G gNBs are presented. With initial positions randomly located within a 99% confidence circle of 2.1 km radius, the mean final position estimate across MC realizations was within 8.6 m of the true UE position.

This paper presents a navigation framework, complemented with real-world experiments, for exploiting uncooperative low Earth orbit (LEO) satellites. To deal with the unknown nature of the satellite's signals, a receiver capable of acquiring and tracking the satellite's Doppler frequency is developed. A sequential matched subspace detector is derived to estimate the number of active LEO satellites. A generalized likelihood ratio (GLR)-based algorithm is developed to estimate the Doppler frequency of LEO satellites with and without integer constraint modulation. A Kalman filter-based tracking loop is designed to track the Doppler. To deal with the poorly known satellites' ephemerides, a differential Doppler LEO-aided inertial measurement unit (IMU) framework is developed, comprising a stationary base with a known position and a mobile rover. Experimental results are presented demonstrating the proposed framework, where the base and the rover acquired and tracked 5 LEO satellites (1 OneWeb, 1 Starlink, 1 Iridium, and 2 Orbcomm). The rover (ground vehicle) was equipped with an altimeter, an industrial-grade IMU, a global navigation satellite systems (GNSS) receiver, and the proposed LEO receiver. The vehicle traversed a trajectory of 540 m in 60 seconds. GNSS signals were available for the first 20 seconds but were fictitiously cut off for the last 40 seconds, during which the vehicle traveled 492 m. The differential LEO-aided IMU navigation system yielded a threedimensional (3-D) position root mean squared error (RMSE) of 7.6 m, compared to 87.7 m with the GNSS-aided IMU

There has been significant interest over the past few years in integrated sensing and communication (ISAC) to enable applications, such as the massive Internet of Things, highly automated transportation systems, and military surveillance. Many ISAC studies in the literature considered designing, from scratch, next-generation systems that are endowed with ISAC capabilities. This article argues that “the truth is out there” and that one can sense and exploit unknown signals, whether non-ISAC “legacy” or ISAC devised. The article presents a framework termed cognitive sensing and opportunistic navigation ( COSON ). COSON can be thought of as an instantiation of ISAC, but, instead of having the “luxury” of designing signals with ISAC capabilities, COSON senses arbitrary, unknown communication signals and exploits them for positioning, navigation, and timing purposes. COSON is composed of four stages: 1) Blind signal acquisition, which comprises spectrum sensing and signal activity detection, blind beacon estimation, initial Doppler and Doppler rate estimation, and blind source enumeration; 2) blind signal tracking and beacon refinement; 3) interference and multipath classification; and 4) sensing and navigation. Extensive experimental results are presented, demonstrating the broad applicability of COSON to terrestrial and nonterrestrial sources, transmitting with various modulation and multiple access schemes: cellular 4G and 5G signals, GPS, and low-Earth orbit satellite (Starlink, Orbcomm, and Iridium) signals. COSON is demonstrated to localize stationary antennas and navigate unmanned aerial vehicles and a ground vehicle, to meter-level accuracy, without global navigation satellite system signals.

Wi-Fi sensing applications have achieved remarkable results over the last decade, offering accurate device-free localization and gesture recognition capabilities. Indeed, Wi-Fi sensing has quickly become a critical field of research for future communication systems under the paradigm known as joint communication and sensing. However, device-free wireless sensing can also be exploited for malign purposes against unaware victims, and the omnipresence of Wi-Fi transceivers poses a significant threat to people’s privacy. Therefore, it is essential to develop functional solutions that can effectively thwart wireless sensing. All the current attempts to hinder illegitimate wireless sensing rely on specialized hardware deployed in the environment, but their cost and complexity can undermine widespread deployment. In this paper, we explore the possibility of using native capabilities of Wi-Fi systems, namely beamforming, to thwart wireless sensing. To this end, we propose for the first time a solution that enables complete control over the beamforming in commercial Wi-Fi devices. On top of that, we build BeamDancer, which randomizes beamforming vectors to inhibit channel fingerprinting. We empirically demonstrate the effectiveness of the proposed solution against three different wireless sensing techniques, both data-driven and model-based, while preserving almost entirely the legitimate Wi-Fi traffic at the same time.

Aircraft navigation without global navigation satellite system (GNSS) signals is considered. To this end, a high sensitivity receiver design is presented that could exploit terrestrial cellular signals of opportunity. The receiver operates on downlink orthogonal frequency division multiplexing (OFDM) signals transmitted by long-term evolution (LTE) eNodeBs. Two challenges encountered on high altitude aircraft are addressed: weak signal power and insufficiently accurate initial Doppler estimation. A so-called ultimate reference signal is proposed, which exploits all available LTE resources transmitted by eNodeB's multiple antenna ports. A time-domain-based receiver design is presented to extract carrier phase observables without the need to reconstruct the received OFDM frame. The proposed approach is shown to significantly improve the receiver's sensitivity, amplifying the received power by nearly 21 dB, while also improving the carrier phase estimation accuracy. To evaluate the efficacy of the proposed receiver, experimental results are presented of an aircraft flying over two regions in California, USA: (i) Region A: Edwards Air Force Base (rural) and (ii) Region B: Palmdale (semi-urban). For three aircraft maneuvers (climbing teardrop, descending teardrop, and grid), the results were consistent: more than 100 eNodeBs were trackable, some of which more than 100 km away, at altitudes as high as about 11,000 ft with carrier-to-noise ratio over 40 dB-Hz. Upon fusing the carrier phase observables with altimeter data via an extended Kalman filter, a sustained accurate navigation solution was achieved. Over trajectories of 43.6 km and 56.8 km in regions A and B, traversed in 455 s and 601 s, a three-dimensional position root mean-squared error (RMSE) of 6.8 m and 8.2 was achieved by exploiting an average of about 22 and 11 eNodeBs, respectively