With the threat of spoofing and jamming on the rise, this work demonstrates how cellular signals can be used as a reliable PNT source for aircraft.
ZAHER (ZAK) M. KASSAS, SHAGHAYEGH SHAHCHERAGHI THE OHIO STATE UNIVERSITY, JOE J. KHALIFE, ALI A. ABDALLAH, NADIM KHAIRALLAH UNIVERSITY OF CALIFORNIA, IRVINE, CHIAWEI LEE, JUAN JURADO, STEVEN WACHTEL, JACOB DUEDE, ZACHARY HOEFFNER, THOMAS HULSEY, RACHEL QUIRARTE US AIR FORCE, RUNXUAN TAY REPUBLIC OF SINGAPORE AIR FORCE
GNSS jamming and spoofing incidents have been bubbling over the past decade, reaching an outburst in 2024 with numerous aviation-related incidents from the Baltic to the Atlantic to the Mediterranean Sea. These incidents jeopardize aviation’s continuous, efficient and safe operation.
In 2021, based on in-flight monitoring of aircraft GNSS receivers, the International Telecommunication Union (ITU) reported that more than 10,000 radio frequency interference (RFI) events were detected globally. EUROCONTROL, a pan-European, civil-military organization dedicated to supporting European aviation, concluded that 38.5% of European enroute flight traffic operates through regions intermittently but regularly affected by GNSS RFI. Two major RFI incidents were reported in the U.S. in 2022. The first, lasting 33 hours, in which air traffic control (ATC) warned
pilots that GPS was unreliable within a 50-nautical-mile radius of the Denver International Airport, with RFI likely to be experienced by aircraft on the ground and as high as 40,000 feet above sea level. The second, lasting 44 hours, shut down a runway at Dallas-Fort Worth International Airport after aviation
authorities said GPS signals there weren’t reliable, forcing approaching and departing aircraft to take cumbersome routes.
Tensions between NATO and Russia have risen since the Ukraine invasion more than two years ago. In 2024, hundreds of passenger jets were affected by an alleged Russian attack on GPS signals in the Baltic region. The attack started on Easter Sunday and lasted more than 63 hours. Due to GPS jamming, Finnair, the only international airline operating flights to the second largest airport in Estonia, suspended its daily flights for the month of May. North Korea “upgraded” its GPS attacks from mere jamming, which led to a South Korean drone crashing in 2012 killing and injuring three people, to spoofing, affecting 200+ planes over five-days in June. Alleged Israeli GPS spoofing for 5+ months has wreaked havoc in neighboring Lebanon, even extending to Cyprus, some 200 km away from Lebanon. Whacky GPS receivers across Lebanon showed their location at Beirut’s International Airport. Lebanese citizens have not been able to rely on GPS in their daily lives, with pilots now abandoning GPS and flying with a compass and a paper map.
A Call for Action
Several national and international regulatory bodies have put forth calls to find GNSS alternatives. In 2021, the National Institute of Standards and Technology (NIST) issued a report on “Foundational PNT Profile: Applying the Cybersecurity Framework for the Responsible Use of PNT Services,” where it identified signals of opportunity (SOPs) and terrestrial radio frequency (RF) sources as a mitigation category that apply to the positioning, navigation and timing (PNT) profile. In 2023, the International Air Transport Association (IATA) invited the International Civil Aviation Organization (ICAO), in coordination with manufacturers and airspace user communities, to develop a global strategy on Alternative PNT (A-PNT) to ensure continuity of flight and air traffic management (ATM) operations during GNSS interruptions. IATA added that “on-board availability of alternative navigation capability using inertial navigation system (INS)/inertial reference unit (IRU) or other conventional radio navigation aids can be helpful.”
SOPs refer to ambient RF signals not intended as PNT sources, e.g., AM/FM, digital television, cellular, and satellite communication signals [1]. SOPs, particularly cellular, possess attractive attributes for aircraft navigation. First, in contrast to dead-reckoning-type sensors (e.g., INS), absolute position information can be extracted from SOPs. Second, they are abundant in most locales of interest, and their received carrier-to-noise ratio (CNR) is 20 to 30 dBs higher than that of GNSS [2]. Third, they are cost-effective: The infrastructure is already deployed and retrofitting aircraft with SOP receivers is not as cumbersome as adding other sensors (e.g., radar, LiDAR, cameras, etc.). Fourth, radars and LiDARs are “proximity” sensors and are not particularly helpful at high altitudes due to lack of features and nearby objects, while navigation with camera images coupled with feature and elevation maps requires additional terrain knowledge that might not be readily available onboard the aircraft. Plus, clouds below the aircraft would prevent cameras from getting usable images from the ground. Finally, many SOPs are practically unaffected by dense smoke, fog, rain, snow, and other poor weather conditions. Previous studies have shown cellular SOPs could yield meter-level accuracy on ground vehicles in GPS-jammed environments [3], [4] and sub-meter-level accuracy on low-altitude unmanned aerial vehicles (UAVs) in differential [5], [6] and non-differential [7] fashions.
This article demonstrates the viability of using cellular SOPs as PNT sources for aircraft. The results were achieved from a collaboration between the United States Air Force (USAF) and the Autonomous Systems Perception, Intelligence, and Navigation (ASPIN) Laboratory through a week-long flight campaign called “SNIFFER: Signals of Opportunity for Navigation In Frequency-Forbidden EnviRonments.” ASPIN Laboratory’s cognitive software-defined receiver (SDR), Multichannel Adaptive TRansceiver Information eXtractor (MATRIX), was flown on a Beechcraft C-12 Huron, a fixed-wing USAF aircraft, to collect ambient cellular signals for flight runs over three regions in California: (i) Region A (rural): Edwards Air Force Base (AFB), (ii) Region B (semi-urban): Palmdale, and (iii) Region C (urban): Riverside. The flights spanned different altitudes and a multitude of trajectories including straight segments, banking turns, benign and aggressive maneuvers, and ascending/descending teardrops with a descent rate ranging between 0 to 1,500 ft/min. The flights were performed by members of the USAF Test Pilot School. The main conclusions from SNIFFER are:
• Cellular signals are surprisingly powerful at both (i) high altitudes, exhibiting CNR of 25–55 dB-Hz at altitudes of 2,000 to 23,000 feet above ground level (AGL) and (ii) horizontal distances exceeding 100 km [8].
• ASPIN’s MATRIX SDR was able to acquire and simultaneously track 100+ cellular base station (BS) towers over trajectories of tens of kilometers [9].
• Upon fusing the cellular carrier phase observables with altimeter measurements via an extended Kalman filter (EKF), meter-level 3D position root mean-squared error (RMSE) was achieved [10].
• Radio simultaneous localization and mapping (radio SLAM) enables the exploitation of BSs whose positions are not precisely know a priori without appreciable degradation in navigation [11].
SNIFFER Hardware Setup
The hardware used to collect cellular data in the SNIFFER flight campaign was assembled on a rack, which was mounted on the C-12 aircraft. The rack was equipped with:
• A quad-channel universal software radio peripheral (USRP)-2955.
• A desktop computer equipped with solid-state drive for data storage.
• A laptop computer running real-time cellular acquisition, which was operated during the flight by a flight engineer to determine when, where and what cellular channels were available to tune the USRP-2955 accordingly. The USRP-2955 was connected to the laptop via a peripheral component interconnect express (PCIe) cable.
• A GPS antenna to (i) feed GPS measurements to the aircraft navigation system and (ii) discipline the USRP’s onboard GPS-disciplined oscillator (GPSDO). Three consumer-grade 800/1,900 MHz Laird cellular antennas were mounted to the bottom of the C-12 and were connected to the USRP-2955. The USRP was tuned to listen to three carrier frequencies corresponding to U.S. cellular providers: T- Mobile, AT&T and Verizon. The sampling rate of each cellular channel was 10 mega samples per second.
The MATRIX SDR was used to post-process the stored data. Figure 1 shows the C-12 aircraft, known as Ms. Mabel, along with ASPIN researchers and USAF pilots. Figure 2 shows the hardware setup with which Ms. Mabel was equipped.
Flight Regions and Aircraft Maneuvers
A flight campaign over four consecutive days was conducted, during which samples of cellular signals were stored for post-processing via the MATRIX SDR. The flights took place over three regions in California, (i) Region A (rural): Edwards AFB, (ii) Region B (semi-urban): Palmdale, and (iii) Region C (urban): Riverside. This article shows results from Regions A and B (illustrated in Figure 3).
Additional data and analyses in all three regions can be found in [8-11].
The C-12 flew at altitudes up to 23,000 feet AGL and performed two types of maneuvers. The first were climbing/descending teardrop-like patterns. These patterns were used to assess the ability to acquire BS towers at different altitudes and to characterize the CNR. The second was a grid-like pattern with many turns and straight elements. These patterns were used to stress-test the cellular navigation receiver’s tracking loops. The navigation solution was computed from the proposed receiver’s output and compared with the aircraft’s ground truth, which was obtained from the C-12’s on-board Honeywe H764-ACE EGI INS/GPS.
Challenges of Cellular OFDM-Based High-Altitude Aircraft Navigation
Signals received from terrestrial transmitters by high altitude aircraft suffer from high path loss due to long-range wireless propagation. By design, cellular BSs are optimized for ground-based user equipment (UEs)–the BS tower’s antennas are tilted downwards to prevent inter-cell interference and provide service to UEs via the antenna’s main lobe.
The current and previous cellular generations (4G LTE and 5G) adopt orthogonal frequency-division multiplexing (OFDM). OFDM-based navigation receivers in the literature were designed for ground platforms or low altitude UAVs operating in close proximity of the BS, where the UE is within the BS antenna’s main lobe or some of the more powerful sidelobes. Employing such receivers on high altitude aircraft revealed challenges in acquiring and tracking terrestrial cellular signals, especially from BSs exhibiting low CNRs. Figure 4a shows the acquisition results of 4G LTE’s primary synchronization signal (PSS) with the state-of-the-art cellular navigation receiver on a ground-based receiver [12]. The same receiver was able to acquire and track cellular signals on low altitude UAVs, achieving submeter-level accuracy [7]. However, upon testing this receiver on cellular data collected on an aircraft at 5,500 ft AGL, the receiver failed to acquire the PSS, as shown in Figure 4b.
Another challenge on high altitude aircraft is the receiver’s ability to estimate, in the acquisition stage, the initial Doppler with sufficient accuracy to achieve lock in the tracking loops. Such accuracy is limited due to the small duty factor of the synchronization signals, defined as the ratio between the number of exploited symbols in a frame to the total number of symbols in a frame [4]. This limitation becomes more severe on high dynamics platforms, where Doppler shifts have a wider range.
Opportunistic Cellular Navigation Receiver
To address the aforementioned challenges, a radical time-domain-based receiver that operates on a so-called ultimate reference signal (URS) was designed [9]. State-of-the-art navigation receivers only consider the orthogonality of the synchronization and channel estimation reference signals (RSs) in the frequency-domain, i.e., the transmitted OFDM frame is always reconstructed from the received time-domain serial data. Then, the navigation observables are estimated by using the RS with the highest bandwidth. Cellular navigation receivers adopted such an approach by following the design outlined in cellular communication receivers. However, in communication applications, it is necessary to reconstruct the OFDM frame to extract various system information, which allows for two-way communication between the UE and the BS. In contrast, in opportunistic UE-based navigation applications, the ultimate objective is to obtain navigation observables by using the most available frequency (bandwidth) and time (duty factor) resources in the received signal. The developed receiver exploits the
orthogonality property of OFDM signals in both frequency and time, where all available resource elements (REs) are combined and used simultaneously in a time-domain-based URS.
Figure 5 summarizes the acquisition and tracking stages of the proposed receiver. Note the similarities between the proposed receiver’s tracking stage and the tracking stage of a conventional GNSS receiver: virtually all building blocks are identical (carrier wipe-off, correlators, filters, discriminators, and numerically-controlled oscillator (NCO)), with the main difference being the pseudorandom noise (PRN) generator being replaced with the URS generator.
The developed URS-based receiver amplifies the received OFDM signal by a factor of 120 ≈ 21 dB. Thus, using the URS addresses the challenge of weak signals, which arises in high altitude aircraft navigation, among other applications (e.g., indoor and deep urban navigation). In addition, the proposed URS-based receiver improves the duty cycle by a factor of 60, which improves the carrier phase
estimation accuracy and initial Doppler shift estimation [9].
Cellular Signal Characterization
The developed opportunistic cellular receiver was used to process the collected cellular in-phase and quadrature (I&Q) samples during the aircraft’s flights. Figures 6–11 show the receiver’s navigation observables (pseudorange and Doppler) along with the CNR and number of tracked cellular BSs during various flight trajectories and maneuvers. The following conclusions can be made from these results:
• The developed receiver, by design, possessed high sensitivity. This high sensitivity enabled the receiver to acquire and track much weaker signals from further away BSs, increasing the number of hearable BSs by an order of magnitude compared to state-of-the-art opportunistic cellular navigation receivers. Essentially, re-processing the raw LTE samples increased the acquirable and trackable BSs from about a dozen with the receiver developed in [12] to more than 100 with the newly developed receiver in [9].
• In rural and semi-urban regions, no matter the aircraft maneuvers, tens of BSs were simultaneously trackable, some of which were more than 100 km away. A significant factor behind the change in the number of tracked BSs during the aircraft’s flight is attributed to the aircraft’s body and wings causing signal blockage and severe attenuation during banking.
• Co-channel interference does not seem to be problematic for BSs within tens of kilometers from the aircraft, as the proposed receiver was able to track all such BSs with sufficiently high CNR.
Aircraft Navigation Results
The navigation observables produced by the opportunistic cellular navigation receiver were fused with altimeter measurements via an EKF to estimate the aircraft’s states (position and
velocity) along with the clock error (bias and drift) difference between the aircraft’s receiver and each BS [9]. To this end, given knowledge of the aircraft’s trajectory (from its onboard GPS-INS system), some of the tracked cellular BSs were mapped, cross checked via Google Earth, and associated with the produced pseudoranges.
Figures 12 and 13 show the produced CNR, pseudoranges, and range error (difference between the receiver’s pseudorange and the true range) to the mapped BSs. The drifting behavior in the range error is due to the clocks’ drift. Figures 14 and 15 show the EKF error plots and the ±3σ bounds. These plots show the EKF errors remain bounded throughout the aircraft’s trajectory. The variations in the σ-bounds are due to a combination of: relative geometry between the aircraft and BSs, number of tracked BSs (eNodeBs), and model mismatch between the aircraft’s
maneuvers (especially during banking) and assumed aircraft dynamical model (Wiener process acceleration model). Table 1 summarizes the navigation performance in Regions A and B while Figures 16 and 17 show the aircraft’s traversed trajectory, estimated trajectory, and BS positions. It is worth emphasizing that the reported performance is expected to improve if an INS is coupled with the LTE navigation observables (e.g., via a tightly coupled SOP-aided INS [13]) and/or observables from all tracked BSs (see Figures 6 to 11) are fused in the EKF.
Radio SLAM with Unknown Cellular Base Station Position
If the cellular BS positions are not known a priori, the aircraft can still navigate to a relatively acceptable degree of accuracy by adopting the radio SLAM framework [11] (Figure 18). In this framework, the aircraft estimates its states (position, velocity, clock errors), simultaneously with the states of the cellular BS (position and clock error) [13]. For aircraft dynamics, one can use either (i) the aircraft’s on-board INS, aided by navigation observables extracted from the cellular BSs or (ii) adopt a dynamical model describing the aircraft’s motion (e.g., Wiener process acceleration model, coordinated maneuver, etc.).
A thorough radio SLAM study was conducted in [11], applied on four flight trajectories in Regions A, B and C, which:
• Compared the performance with (i) INS coupling versus (ii) adopting a Wiener process acceleration model
• Evaluated the degradation in performance due to having to map the SOP positions simultaneously with navigating the aircraft. Different a priori conditions of the SOPs’ positions were studied: from all unknown, to some known, to all known.
• Evaluated the performance due to intermittent pseudorange measurements.
The cellular towers’ initial position uncertainty in the local north-east-down (NED) frame Pr,sop (0|0) was set to diag[105, 105, 102] m2, which correspond to an initial 2–D 95% uncertainty circle of radius 774 in the North-East plane. The radio SLAM EKF was initialized with the true aircraft states (obtained from the aircraft’s onboard GNSS-INS system), after which GNSS was assumed unavailable throughout the flight run.
Figure 19 shows a sample of the achieved navigation results on one of the flight runs, while Figures 20 to 22 show the EKF error plots. Table 2 summarizes the navigation performance of the altimeter-INS filter, the SOP-INS, and Wiener process acceleration (in parentheses) radio SLAM frameworks with varying levels of a priori knowledge on the SOP BSs’ true positions (n=number of SOPs with known BS position; m=number of SOPs with unknown BS position).
It is interesting to note the navigation performance does not degrade appreciably even when all SOPs are unknown and mapped simultaneously. It is worth emphasizing that the reported performance is expected to improve significantly if all tracked BSs (Figures 6 to 11) are fused in the EKF and/or motion planning strategies are adopted [14].
Future Outlook and Recommendations
This article demonstrated the tremendous potential of cellular signals as an A-PNT source to GNSS for aircraft navigation. With a specialized receiver design, more than 100 cellular BSs were acquirable and trackable at altitudes as high as 23,000 feet AGL and from horizontal distances exceeding 100 km. Upon fusing the cellular receiver’s navigation observables with altimeter data via an EKF, sustained meter-level accurate navigation was demonstrated over trajectories exceeding hundreds of kilometers, while exercising various maneuvers over different regions in Southern California. When the cellular BS positions were not known a priori, radio SLAM was shown to be effective without appreciable degradation in navigation performance.
While the revealed results are promising, particular needs of military operations versus civil aviation (e.g., low-altitude urban air mobility) must be addressed if cellular SOPs are used in practice. For instance, while the conducted research in [8-11] focused on showing the ranging/accuracy aspects, issues of integrity, availability, and continuity were not studied and warrant further research. In addition, how can we deal with potentially irreconcilable conflicts between the long timeline of civil aviation operation versus the short timeline of cellular technology? What commitments should we require from governing bodies (e.g., 3GPP) or local cellular operators? The authors hope this article will initiate a robust discussion by standards bodies, government agencies, and aviation stakeholders into using (or dual purposing) the existing cellular infrastructure as a complementary PNT (CPNT) solution. Future work should study the achievable performance and its repeatability to satisfy minimum operational performance standards (MOPS).
Acknowledgements
This work was supported in part by the U.S. Department of Transportation (USDOT) under Grant 69A3552348327 for the CARMEN+ University Transportation Center (UTC), the Air Force Office of Scientific Research (AFOSR) under Grant FA9550-22-1-0476, the Office of Naval Research (ONR) under Grant N00014-19-1-2511, the National Science Foundation (NSF) under Grant 2240512, and the Laboratory Directed Research and Development program at Sandia National Laboratories. DISTRIBUTION STATEMENT A. Approved for public release; Distribution is unlimited. 412TW-PA-20146. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE- NA0003525. This paper describes objective technical results and analysis. Any subjective views or opinions that might be expressed in the paper do not necessarily represent the views of the U.S. Department of Energy or the United States Government.
References
(1) J. Raquet et al., “Position, navigation, and timing technologies in the 21st century,” J. Morton, F. van Diggelen, J. Spilker, Jr., and B. Parkinson, Eds. Wiley-IEEE, 2021, vol. 2, Part D: Position, Navigation, and Timing Using Radio Signals-of-Opportunity, ch. 35–43, pp. 1115–1412.
(2) A. Abdallah, J. Khalife, and Z. Kassas, “Experimental characterization of received 5G signals carrier-to-noise ratio in indoor and urban environments,” in Proceedings of IEEE Vehicular Technology Conference, April 2021, pp. 1–5.
(3) Z. Kassas, J. Khalife, A. Abdallah, and C. Lee, “I am not afraid of the GPS jammer: resilient navigation via signals of opportunity in GPS-denied environments,” IEEE Aerospace and Electronic Systems Magazine, vol. 37, no. 7, pp. 4–19, July 2022.
(4) Z. Kassas and A. Abdallah, “No GPS no problem: Exploiting cellular OFDM-based signals for accurate navigation,” IEEE Transactions on Aerospace and Electronic Systems, vol. 59, no. 6, pp. 9792–9798, December 2023.
(5) J. del Peral-Rosado, P. Nolle, F. Rothmaier, S. Razavi, G. Lindmark, X. Jiang, D. Shrestha, F. Gunnarsson, S. Parsawar, R. Mundlamuri, F. Kaltenberger, N. Sirola, O. Sarkka, U. Noman, J. Rostrom, K. Vaarala, P. Miettinen, S. Garlaschi, L. Canzian, H. Babaroglu, E. RastorguevaFoi, M. Turunen, J. Talvitie, and D. Flachs, “Proof-of-concept of dedicated aerial 5G and GNSS testbed for enhanced hybrid positioning,” in Proceedings of ION GNSS Conference, September 2022, pp. 2362– 2376.
(6) J. Khalife and Z. Kassas, “Differential framework for submeter-accurate vehicular navigation with cellular signals,” IEEE Transactions on Intelligent Vehicles, vol. 8, no. 1, pp. 732–744, January 2023.
(7) J. Khalife and Z. Kassas, “On the achievability of submeter-accurate UAV navigation with cellular signals exploiting loose network synchronization,” IEEETransactions on Aerospace and Electronic Systems, vol. 58, no. 5, pp. 4261–4278, October 2022.
(8) Z. Kassas, J. Khalife, A. Abdallah, C. Lee, J. Jurado, S. Wachtel, J. Duede, Z. Hoeffner, T. Hulsey, R. Quirarte, and R. Tay, “Assessment of cellular signals of opportunity for high-altitude aircraft navigation,” IEEE Aerospace and Electronic Systems Magazine, vol. 37, no. 10, pp. 4–19, October 2022.
(9) Z. Kassas, A. Abdallah, Shahcheraghi, J. Khalife, C. Lee, J. Jurado, S. Wachtel, J. Duede, Z. Hoeffner, T. Hulsey, R. Quirarte, and R. Tay, “I can hear you loud and clear: GNSS-less high altitude aircraft navigation with terrestrial cellular signals of opportunity,” IEEE Transactions on Aerospace and Electronic Systems, 2024, accepted.
(10) Z. Kassas, J. Khalife, A. Abdallah, C. Lee, J. Jurado, J. Duede, Z. Hoeffner, T. Hulsey, R. Quirarte, S. Wachtel, and R. Tay, “Flight demonstration of high altitude aircraft navigation with cellular signals,” IEEE Intelligent Transportation Systems Magazine, vol. 15, no. 4, pp. 150–165, 2023.
(11) Z. Kassas, N. Khairallah, J. Khalife, C. Lee, J. Jurado, S. Wachtel, J. Duede, Z. Hoeffner, T. Hulsey, R. Quirarte, and R. Tay, “Aircraft navigation in GNSS-denied environments via radio SLAM with terrestrial signals of opportunity,” IEEE Transactions on Intelligent Transportation Systems, 2024, accepted.
(12) K. Shamaei and Z. Kassas, “LTE receiver design and multipath analysis for navigation in urban environments,” NAVIGATION, Journal of the Institute of Navigation, vol. 65, no. 4, pp. 655–675, December 2018.
(13) J. Morales and Z. Kassas, “Tightly-coupled inertial navigation system with signals of opportunity aiding,” IEEE Transactions on Aerospace and Electronic Systems, vol. 57, no. 3, pp. 1930–1948, 2021.
(14) Y. Yang, J. M. J. Khalife and, and Z. Kassas, “UAV waypoint opportunistic navigation in GNSS-denied environments,” IEEE Transactions on Aerospace and Electronic Systems, vol. 58, no. 1, pp. 663–678, February 2022.
Authors
Zaher (Zak) M. Kassas is the TRC Endowed Chair in Intelligent Transportation Systems, Professor at The Ohio State University, and Director of the Autonomous Systems Perception, Intelligence, and Navigation (ASPIN) Laboratory. He is also Director of the U.S. Department of Transportation Center: Center for Automated Vehicle Research with Multimodal AssurEd Navigation (CARMEN), focusing on navigation resiliency and security of highly automated transportation systems. He received a B.E. in Electrical Engineering from the Lebanese American University, an M.S. in Electrical and Computer Engineering from The Ohio State University, and an M.S.E. in Aerospace Engineering and a Ph.D. in Electrical and Computer Engineering from The University of Texas at Austin. His awards include the National Science Foundation (NSF) CAREER award, Office of Naval Research (ONR) Young Investigator Program (YIP) award, Air Force Office of Scientific Research (AFOSR) YIP award, IEEE Walter Fried Award, IEEE Harry Rowe Mimno Award, Institute of Navigation (ION) Samuel Burka Award, and ION Col. Thomas Thurlow Award. He is a Fellow of IEEE and ION and a Distinguished Lecturer of the IEEE Aerospace and Electronic Systems Society.
Joe Khalife was a postdoctoral fellow at the University of California, Irvine, and member of the ASPIN Laboratory. He received a B.E. in Electrical Engineering and an M.S. in Computer Engineering from the Lebanese American University and a Ph.D. in Electrical Engineering and Computer Science from the University of California, Irvine. He is a recipient of the 2018 IEEE Walter Fried Award and 2021 IEEE Robert T. Hill Best Dissertation Award.
Ali A. Abdallah received a B.E. from the Lebanese American University and an M.S. and Ph.D.. in Electrical Engineering and Computer Science from the University of California, Irvine. He was a member of the ASPIN Laboratory. He is a recipient of the 2020 IEEE/ION Position, Location, and Navigation Symposium (PLANS) best student paper award.
Nadim Khairallah received a B.E. in Mechanical Engineering from the American University of Beirut and an M.S. in Mechanical and Aerospace Engineering from the University of California, Irvine. He was a member of the ASPIN Laboratory. He is a recipient of the 2022 U.S. Department of Transportation Graduate Student of the Year award and the 2022 IEEE Vehicular Technology Conference best student paper award.
Shaghayegh Shahcheraghi is a Ph.D. student in the Department of Electrical and Computer Engineering at The Ohio State University and a member of the ASPIN Laboratory. She received a B.S. and an M.S. in Electrical Engineering from Shiraz University and an M.S. in Telecommunication Engineering from Politecnico Di Milano.
Chiawei Lee is an Assistant Professor and Instructor Flight Test Engineer at the U.S. Air Force Test Pilot School. He serves as the Test Management Program Director where he oversees about a dozen student and staff led flight test projects each year. In addition, he is the Chief Test Safety Officer responsible for the safe execution of curriculum and flight test project safety packages. He received a B.S. in Aerospace Engineering from the University of California, Los Angeles, and a M.S. in Aero/Astro Engineering from Stanford University.
Juan Jurado is a U.S. Air Force Lieutenant Colonel and the Director of Education at the U.S. Air Force Test Pilot School. He holds a B.S. from Texas A&M University, M.S. from the Air Force Test Pilot School, and M.S. and Ph.D. from the Air Force Institute of Technology. Previously, he served as Director of Engineering for the 413th Flight Test Squadron and oversaw various C-130, V-22, and H-1 flight test programs. His research interests include aircraft performance modeling, online sensor calibration, image processing, and multi-sensor navigation.
Steven Wachtel is a U.S. Air Force Captain and a Flight Test Engineer, assigned to the 780th Test Squadron, Eglin AFB, FL. He received a B.S. in Mechanical Engineering from The Ohio State University, an M.S. in Flight Test Engineering from the U.S. Air Force Test Pilot School, and an M.S. in Systems Engineering from the Air Force Institute of Technology.
Jacob Duede is a Major in the U.S. Air Force. He was trained as a Communication/Navigation/Mission Systems apprentice on C-17 Globmaster II aircraft. He received a B.S. in Mechanical Engineering from the U.S. Air Force Academy and graduated from the U.S. Air Force Test Pilot School at Edwards Air Force Base. He is a Senior Pilot with over 2,000 hours and holds an M.S. in Engineering from the University of Arkansas and an M.S. in Flight Test Engineering from Air University.
Zachary Hoeffner is a flight test engineer at the U.S. Air Force. He received a B.S in Nuclear Engineering from the U.S. Air Force Academy, an M.S. in Flight Test Engineering from the U.S. Air Force Test Pilot School, an M.S. in Engineering Physics and Applied Physics from the Air Force Institute of Technology, and an M.S. in Nuclear Engineering from the Air Force Institute of Technology.
Thomas Hulsey is a U.S. Air Force Flight Commander of Operations Engineering. He received a B.S. in Aerospace Engineering from Missouri University of Science and Technology, an M.S. in Aeronautical Engineering from the Air Force Institute of Technology, and an M.S. in Experimental Flight Test Engineering from the United States Air Force Test Pilot School.
Rachel Quirarte is a KC-46 and KC-135 programmatic flight commander and test pilot in the 418th Flight Test Squadron in the U.S. Air Force. She received a B.S. in Aeronautical Engineering from the U.S. Air Force Academy, an M.S. in Flight Test Engineering from the U.S. Air Force Test Pilot School, and an M.S. in Mechanical Engineering from Rice University.
RunXuan Tay received a B.S. degree in Electrical Engineering from the University California, San Diego, and M.S. degree in Flight Test Engineering from the U.S. Air Force Test Pilot School. He is currently a test pilot at Air Warfare Center, Republic of Singapore Air Force, where he works on fixed wing test programs.
