Zak M. Kassas, Joe Khalife, and Ali Abdallah; University of California, Irvine; and Chiawei Lee; US Air Force Test Pilot School

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Abstract:

I am not afraid of the GPS jammer, as long as there are ambient signals of opportunity (SOPs) to exploit in the environment. In environments where GPS signals are challenged (e.g., indoors and deep urban canyons) and denied (e.g., under jamming and spoofing attacks), SOPs could serve as an alternative navigation source to GPS, and more generally, to global navigation satellite systems (GNSS). This paper presents a radio simultaneous localization and mapping (radio SLAM) approach that enables the exploitation of SOPs for resilient and accurate navigation. Radio SLAM estimates the states of the navigator-mounted receiver simultaneously with the SOPs’ states. Radio SLAM could produce an SOP-derived navigation solution in a standalone fashion or by fusing SOPs with sensors (e.g., inertial measurement unit (IMU), lidar, etc.), digital maps, and/or other signals (e.g., GNSS). The paper also overviews a core component of radio SLAM: a cognitive software-defined radio (SDR) called MATRIX: Multichannel Adaptive TRansceiver Information eXtractor, which produces navigation observables from terrestrial and space-based SOPs. Next, the paper showcases the most accurate navigation results to-date with terrestrial and space-based SOPs from low Earth orbit (LEO) satellites in different environments and on different platforms: indoor pedestrian, ground vehicles in urban and deep urban canyons, and aerial vehicles. Finally, the paper presents the first ever published experimental results for navigation with SOPs in a GPS-denied environment. These experiments took place at Edwards Air Force Base, California, USA, during which GPS was intentionally jammed with jamming-to-signal (J/S) ratio as high as 90 dB. The results showcase a ground vehicle traversing a trajectory of about 5 km in 180 seconds in the GPS-jammed environment, during which a GPS-IMU system drifted from the vehicle’s ground truth trajectory, resulting in a position root mean-squared error (RMSE) of 238 m. In contrast, the radio SLAM approach with a single cellular long-term evolution (LTE) SOP whose position was poorly known (an initial uncertainty on the order of several kilometers) achieved a position RMSE of 32 m.