Performance Analysis of Simultaneous Tracking and Navigation with LEO Satellites Trier R. Mortlock and Zaher M. Kassas University of California, Irvine, USA BIOGRAPHIES Trier R. Mortlock is a Ph.D. student in the Department of Mechanical and Aerospace Engineering at the University of California, Irvine and a member of the Autonomous Systems Perception, Intelligence, and Navigation (ASPIN) Laboratory. He received a B.S. in Mechanical Engineering from the University of California, Berkeley. He serves in the U.S. Army Reserve serving as a Cyber Operations Officer. His current research interests include cyber-physical systems, satellite-based navigation, and situational awareness in dynamic uncertain environments. Zaher (Zak) M. Kassas is an associate professor at the University of California, Irvine and director of the ASPIN Laboratory. 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. In 2018, he received the National Science Foundation (NSF) Faculty Early Career Development Program (CAREER) award, and in 2019, he received the Office of Naval Research (ONR) Young Investigator Program (YIP) award. He is a recipient of 2018 IEEE Walter Fried Award, 2018 Institute of Navigation (ION) Samuel Burka Award, and 2019 ION Col. Thomas Thurlow Award. He is an Associate Editor for the IEEE Transactions on Aerospace and Electronic Systems and the IEEE Transactions on Intelligent Transportation Systems. His research interests include cyber-physical systems, estimation theory, navigation systems, autonomous vehicles, and intelligent transportation systems. ABSTRACT An opportunistic navigation framework using low Earth orbit (LEO) satellites is analyzed. This framework, termed simultaneous tracking and navigation (STAN), estimates a navigating vehicle’s state along with the states of orbiting LEO satellites. STAN employs an extended Kalman filter (EKF) to fuse measurements from satellite receivers and an inertial navigation system (INS). The navigation performance is analyzed due to: (i) the vehicle being equipped with (1) different inertial measurement unit (IMU) grades: consumer, industrial, and tactical and (2) different receiver clock quality: temperature-compensated crystal oscillators (TCXO) and oven-controlled crystal oscillators (OCXO) and (ii) the LEO satellites being equipped with different transmitter clock quality: OCXO and chip-scale atomic clock (CSAC). Additionally, the effect of utilizing a large number of LEO satellites for navigation is investigated. This analysis provides insight into the achievable performance of STAN, which can serve as an alternative navigation system in global navigation satellite system (GNSS)-denied environments. The performance predictions from simulations are compared with experimental results with real signals from the Orbcomm LEO constellation. A close match between the simulation and experimental results is demonstrated for an unmanned aerial vehicle (UAV) navigating via the STAN framework with signals from two Orbcomm LEO satellites for 160 seconds, the last 35 seconds of which are without GNSS signals. The UAV’s position root-mean squared error (RMSE) from simulations was 8.6 m, while the experimental position RMSE was 10 m. I. INTRODUCTION There has been a surge in recent years to establish resilient positioning, navigation, and timing (PNT) services which possess features of accessibility and integrity [1]. This surge embodies the paramount need for resilient PNT on numerous critical infrastructure (e.g. transportation systems, power grids, communications, military operations, emergency response missions) that rely on global navigation satellite systems (GNSS), which are vulnerable to inter- ference, jamming, and spoofing [2]. This paper examines the use of low Earth orbit (LEO) satellites for navigation purposes in GNSS-denied environments. Copyright c 2020 by T. R. Mortlock Preprint of the 2020 ION GNSS+ Conference and Z. M. Kassas St. Louis, Missouri, September 21–25, 2020 The paper focuses in particular on unmanned aerial vehicles (UAVs), which traditionally navigate their trajectories by relying on a tightly coupled system of inertial measurement units (IMUs), used for short-term positioning updates and a local navigation solution, and GNSS signals, used to correct accumulated errors from IMU measurements and to provide a navigation solution in a global frame. This traditional framework faces challenges as GNSS signals de- grade when navigating indoors, in deep urban canyons, or under dense foliage, reducing the accuracy and availability of the navigation system. Degradation poses serious safety risks for many navigation missions, including aviation, transportation, disaster relief, and military operations. Furthermore, GNSS signals are prone to unintentional in- terference, intentional jamming, or malicious spoofing, which could have catastrophic consequences [3]. The idea of exploiting ambient radio frequency signals of opportunity for navigation has been an area of extensive recent study [4–6]. Past works have exploited terrestrial signals from AM/FM radio, cellular, and digital television signals [7–14], as well as non-terrestrial signals from satellite constellations [15–21] for navigation purposes. Meter-level accurate navigation has been demonstrated on ground vehicles with terrestrial cellular and television signals [22–24], while sub-meter-level accurate navigation has been demonstrated on UAVs [25, 26]. Moreover, opportunistic navigation with LEO signals has been demonstrated on ground vehicles and UAVs with existing constellations, while showing the potential of achieving sub-meter level accuracy with future megaconstellation LEO satellites [27]. LEO satellites offer a promising source of signals to leverage opportunistically for navigation purposes. There are currently over 1,900 LEO satellites in operational orbits, and numerous companies like SpaceX, Samsung, Boeing, and OneWeb are engaged in launching tens of thousands more over the next decade [28]. Fig. 1 shows a subset of 717 active LEO satellites from the following six constellations: Starlink, OneWeb, Iridium, GlobalStar, Iridium Next, and Orbcomm [29]. The surge to add to the current LEO satellites is evident in the recent request made on behalf of SpaceX to add 30,000 LEO satellites to their current efforts [30]. LEO satellite signals offer a number of unique benefits for navigation purposes: (i) strong signal strength due to their lower orbits, (ii) diversity in their geometries and frequencies, (iii) availability from different orbits and constellations, and (iv) the ability to observe and collect free of charge, with the proper receivers. However, the use of LEO satellites for navigation comes with several challenges, most notably: (i) the satellites cannot be assumed to be transmitting their states, (ii) the satellites cannot be assumed to be equipped with atomic oscillators, nor to be tightly synchronized, and (iii) extracting navigation observables from LEO satellites is not yet fully understood. This paper focuses on the first two challenges by adopting the simultaneous tracking and navigation (STAN) framework. The STAN framework estimates the dynamic, stochastic states of the LEO satellites simultaneously with the states of the navigating vehicle [20]. STAN utilizes a filter (e.g. extended Kalman filter) that couples GNSS and LEO receivers with an IMU. STAN considered a simplified LEO satellite dynamical model in [20] and was adapted to account for the case where LEO satellites periodically transmit their positions in [31]. More elaborate LEO satellite dynamics models were studied in [32]. A differential framework was proposed in [21, 33]. Fig. 1. Subset of 717 active LEO satellites from the following six constellations: Starlink, OneWeb, Iridium, GlobalStar, Iridium Next, and Orbcomm [29] from June 2020. Map data: Google Earth. This paper presents an analysis of a vehicle’s navigation performance while operating via the STAN framework using LEO satellite signals. This type of performance characterization is vital to understanding the viability of this navigation framework and feasibility of its use under various conditions. The effect of the quality of the navigating vehicle’s IMU grade and both the quality of the vehicle-mounted oscillator and LEO satellites’ oscillators on the navigation solution is studied. Bounds for STAN’s performance as a function of the sensor types used, as well as the number of LEO satellites used, are presented through simulation. Experimental results that utilize the STAN framework for navigation is compared with the paper’s proposed performance characterization, showing a close match between the simulation and experimental results. These results demonstrate a UAV navigating via the STAN framework with signals from two Orbcomm LEO satellites for 160 seconds, the last 35 seconds of which are without GNSS signals. The UAV’s position root-mean squared error (RMSE) from simulations was 8.6 meters, while the experimental position RMSE was 10 meters. The future of high-availability navigation requires overcoming both purposeful and incidental GNSS degradation alike, and the analysis of navigation with LEO satellites under the STAN framework helps assess the feasibility of future alternative navigation sources. The remainder of this paper is organized as follows.