Prof. Kassas and Student Win Best Student Paper Award at IEEE Vehicular Technology Conference 2022
Ohio State ECE Professor Zak Kassas and his student Nadim Khairallah won the overall Best Student Paper award at the 2022 IEEE Vehicular Technology Conference (VTC). The paper was chosen from among 614 papers accepted at IEEE VTC 2022.
The paper, titled "An Interacting Multiple Model Estimator of LEO Satellite Clocks for Improved Positioning," addressed a ctitical problem facing the exploitation of unknown low Earth orbit (LEO) satellite signals for positioning, navigation, and timing (PNT). In order to use LEO satellites’ signals for PNT, the LEO satellites’ clock error must be known.
Unlike global navigation satellite system (GNSS) satellites (e.g., GPS), LEO satellites generally do not openly transmit information about their clock error in their downlink signals. While the clock error states (bias and drift) can be estimated, the stability of the oscillator is generally unknown. Knowledge of the oscillator’s stability is essential to calculate the covariance matrix of the process noise driving the clock error states.
"We are witnessing a space renaissance. Tens of thousands of broadband LEO satellites are expected to be launched by the end of this decade," said Kassas. "These planned megaconstellations of LEO satellites along with existing constellations will shower the Earth with a plethora of signals of opportunity tht could be exploited for PNT in the inevitable event that GNSS signals become unavailable (e.g., in deep urban canyons, under dense foliage, during unintentional interference, and intentional jamming) or untrustworthy (e.g., under malicious spoofing attacks)."
The paper addressed this challenge by developing an interacting multiple-model (IMM) estimator to adaptively estimate the process noise covariance of LEO satellite clocks. Experimental results were presented showing a stationary ground receiver localizing itself with carrier phase measurements from a single Orbcomm LEO satellite. The developed IMM is shown to reduce the localization error and improve filter consistency over two fixed, mismatched extended Kalman filters (EKFs). Starting with an initial receiver position error of 1.45 km, the IMM yielded a final error of 111.26 m, while the errors of conservative and optimistic EKFs converged to 254.71 m and 429.35 m, respectively.
"I am proud of Nadim's hard work and ingenuity in tackling this problem. I am greatful for the Office of Naval Research (ONR), the National Science Foundation (NSF), and the U.S. Department of Transportation (USDOT) for supporting this research," Kassas added.
