Radionavigation Laboratory Conference Proceedings

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    A Proposal for Securing Terrestrial Radio-Navigation Systems
    (2020) Kor, Ronnie X.T.; Iannucci, Peter A.; Narula, Lakshay; Humphreys, Todd E.
    The security of terrestrial radio-navigation systems (TRNS) has not yet been addressed in the literature. This proposal builds on what is known about securing global navigation satellite systems (GNSS) to address this gap, re-evaluating proposals for GNSS security in light of the distinctive properties of TRNS. TRNS of the type envisioned in this paper are currently in their infancy, unburdened by considerations of backwards compatibility: security for TRNS is a clean slate. This paper argues that waveform- or signal-level security measures are irrelevant for TRNS, preventing neither spoofing nor unauthorized use of the service. Thus, only security measures which modify navigation message bits merit consideration. This paper proposes orthogonal mechanisms for navigation message encryption (NME) and authentication (NMA), constructed from standard cryptography primitives and specialized to TRNS: message encryption allows providers to offer tiered access to navigation parameters on a bit-by-bit basis, and message authentication disperses the bits of a message authentication code across all data packets, posing an additional challenge to spoofers. The implementation of this proposal will render TRNS more secure and resilient than traditional civil GNSS.
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    Multi-Antenna Vision-and-Inertial-Aided CDGNSS for Micro Aerial Vehicle Pose Estimation
    (2020) Yoder, James E.; Iannucci, Peter A.; Narula, Lakshay; Humphreys, Todd E.
    A system is presented for multi-antenna carrier phase differential GNSS (CDGNSS)-based pose (position and orientation) estimation aided by monocular visual measurements and a smartphone-grade inertial sensor. The system is designed for micro aerial vehicles, but can be applied generally for low-cost, lightweight, high-accuracy, geo-referenced pose estimation. Visual and inertial measurements enable robust operation despite GNSS degradation by constraining uncertainty in the dynamics propagation, which improves fixed-integer CDGNSS availability and reliability in areas with limited sky visibility. No prior work has demonstrated an increased CDGNSS integer fixing rate when incorporating visual measurements with smartphone-grade inertial sensing. A central pose estimation filter receives measurements from separate CDGNSS position and attitude estimators, visual feature measurements based on the ROVIO measurement model, and inertial measurements. The filter's pose estimates are fed back as a prior for CDGNSS integer fixing. A performance analysis under both simulated and real-world GNSS degradation shows that visual measurements greatly increase the availability and accuracy of low-cost inertial-aided CDGNSS pose estimation.
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    Open-World Virtual Reality Headset Tracking
    (2020) Humphreys, Todd E.; Kor, Ronnie X.T.; Iannucci, Peter A.; Yoder, James E.
    A novel outdoor Virtual Reality (VR) concept called Open-World Virtual Reality (OWVR) is presented that combines precise GNSS positioning and a smartphone-grade inertial sensor to provide globally-referenced centimeter-and-degree-accurate tracking of the VR headset. Unlike existing augmented and virtual reality systems, which perform camera-based inside-out headset tracking relative to a local reference frame (e.g., an ad-hoc frame fixed to a living room), OWVR's globally-referenced tracking enables a novel VR experience in which the user's outdoor exploration is robust to extremes in lighting conditions and local visual texture. This paper introduces the OWVR concept and presents a prototype OWVR system with two candidate sensor fusion architectures, one loosely and one tightly coupled. Comparative performance is evaluated in terms of tracking accuracy and availability of an integer-aperture-test-validated fixed tracking solution. For scenarios with degraded GNSS availability, which will be typical for outdoor VR, the tightly-coupled architecture is shown to offer a critical tracking robustness advantage.
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    Cross-Modal Localization: Using Automotive Radar for Absolute Geolocation within a Map Produced with Visible-Light Imagery
    (IEEE, 2020) Iannucci, Peter A.; Narula, Lakshay; Humphreys, Todd E.
    This paper explores the possibility of localizing an automotive-radar-equipped vehicle within an urban environment relative to an existing map of the environment created using data from visible light cameras. Such cross-modal localization would enable robust, low-cost absolute localization in poor weather conditions based only on radar even when the vehicle has never previously visited the area. This is because a pre-existing absolutely-referenced visible-light-based map (e.g., constructed from Google Street View images) could be exploited for localization provided that a correspondence between features in this map and the vehicle’s radar returns can be established. The greatest challenge presented by cross-modal localization with automotive radar is the extreme sparseness of automotive-radar-produced features, which prevents application of standard computer vision techniques for the cross-modal registration. To the best of the authors’ knowledge, cross-modal localization using automotive- grade radar within a visible-light-based map is unprecedented. The current paper demonstrates that it can be used for vehicle localization with horizontal errors below 61 cm (95%).
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    Economical Fused LEO GNSS
    (IEEE, 2020) Iannucci, Peter A.; Humphreys, Todd E.
    In addition to Internet service, new commercial broadband low-Earth-orbiting (LEO) satellites could provide a positioning, navigation, and timing (PNT) service far more robust to interference than traditional Global Navigation Satellite Systems (GNSS). Previous proposals for LEO PNT require dedicated spectrum and hardware: a transmitter, antenna, and atomic clock on board every broadband satellite. This paper proposes a high- performance, low-cost alternative which fuses the requirements of PNT service into the existing capabilities of the broadband satellite. A concept of operations for so-called fused LEO GNSS is presented and analyzed both in terms of positioning performance and in terms of the economy of its use of constellation resources of transmitters, bandwidth, and time. This paper shows that continuous assured PNT service over ±60° latitude (covering 99.8% of the world’s population) with positioning performance exceeding traditional GNSS pseudoranging would cost less than 2% of system capacity for the largest new constellations, such as SpaceX’s Starlink or Amazon’s Project Kuiper.
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    Intercepting Unmanned Aerial Vehicle Swarms with Neural-Network-Aided Game-Theoretic Target Assignment
    (IEEE, 2020) Montalbano, Nicholas G.; Humphreys, Todd E.
    This paper examines the use of neural networks to perform low-level control calculations within a larger game-theoretic framework for drone swarm interception. As unmanned aerial vehicles (UAVs) become more capable and less expensive, their malicious use becomes a greater public threat. This paper examines the problem of intercepting rogue UAV swarms by exploiting the underlying game-theoretic nature of large-scale pursuit-evasion games to develop locally optimal profiles for target assignment. It paper also examines computationally efficient means to streamline this process.
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    Low SWaP-C Radar for Urban Air Mobility
    (IEEE, 2020) Lies, William A.; Narula, Lakshay; Iannucci, Peter A.; Humphreys, Todd E.
    A method is developed and tested for extending the range of low-cost radar chipsets for use in urban air mobility (UAM) vehicles. The method employs weak-signal correlation techniques and long measurement intervals to achieve a 1 km range. Low-cost radar is an enabling technology for vertical take-off and landing (VTOL) aircraft envisioned for large-scale deployment in urban areas. These aircraft must be autonomously piloted to make them economically feasible, but autonomous systems have yet to match a human pilot’s ability to detect and avoid (DAA) obstacles. Visible light cameras are useful for this application, but cameras alone are insufficient, as they are fundamentally unable to resolve range. Existing commercial radar units would suffice for DAA, but their large size weight, power, and cost (SWaP-C) militates against their application to UAM. The technique detailed in this paper is a fused camera-radar solution that exploits the camera’s excellent angular resolution to guide radar signal processing so that signals arriving from a camera-detected target are combined constructively. Such guided processing significantly extends the range of low SWaP-C radar chipsets, making them useful for DAA. An analysis of the fused technique’s robustness to target velocity uncertainty is presented, along with experimental results indicating that a typically-sized VTOL aircraft would be detectable at a range of 1 km.
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    Automotive-Radar-Based 50-cm Urban Positioning
    (IEEE, 2020) Narula, Lakshay; Iannucci, Peter A.; Humphreys, Todd E.
    Deployment of automated ground vehicles (AGVs) beyond the confines of sunny and dry climes will require sub-lane-level positioning techniques based on radio waves rather than near-visible-light radiation. Like human sight, lidar and cameras perform poorly in low-visibility conditions. This paper develops and demonstrates a novel technique for robust 50-cm-accurate urban ground positioning based on commercially-available low-cost automotive radars. The technique is computationally efficient yet obtains a globally-optimal translation and heading solution, avoiding local minima caused by repeating patterns in the urban radar environment. Performance is evaluated on an extensive and realistic urban data set. Comparison against ground truth shows that, when coupled with stable short-term odometry, the technique maintains 95-percentile errors below 50 cm in horizontal position and 1 degree in heading.
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    TEX-CUP: The University of Texas Challenge for Urban Positioning
    (2020) Narula, Lakshay; LaChapelle, Daniel M.; Murrian, Matthew J.; Wooten, J. Michael; Humphreys, Todd E.; de Toldi, Elliot; Morvant, Guirec; Lacambre, Jean-Baptiste
    A public benchmark dataset collected in the dense urban center of the city of Austin, TX is introduced for evaluation of multi-sensor GNSS-based urban positioning. Existing public datasets on localization and/or odometry evaluation are based on sensors such as lidar, cameras, and radar. The role of GNSS in these datasets is typically limited to the generation of a reference trajectory in conjunction with a high-end inertial navigation system (INS). In contrast, the dataset introduced in this paper provides raw ADC output of wideband intermediate frequency (IF) GNSS data along with tightly synchronized raw measurements from inertial measurement units (IMUs) and a stereoscopic camera unit. This dataset will enable optimization of the full GNSS stack from signal tracking to state estimation, as well as sensor fusion with other automotive sensors. The dataset is available under Public Datasets. Efforts to collect and share similar datasets from a number of dense urban centers around the world are under way.
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    Automotive Collision Risk Estimation Under Cooperative Sensing
    (IEEE, 2020) LaChapelle, Daniel; Humphreys, Todd; Narula, Lakshay; Iannucci, Peter; Moradi-Pari, Ehsan
    This paper offers a technique for estimating collision risk for automated ground vehicles engaged in cooperative sensing. The technique allows quantification of (i) risk reduced due to cooperation, and (ii) the increased accuracy of risk assessment due to cooperation. If either is significant, cooperation can be viewed as a desirable practice for meeting the stringent risk budget of increasingly automated vehicles; if not, then cooperation—with its various drawbacks—need not be pursued. Collision risk is evaluated over an ego vehicle’s trajectory based on a dynamic probabilistic occupancy map and a loss function that maps collision-relevant state information to a cost metric. The risk evaluation framework is demonstrated using real data captured from two cooperating vehicles traversing an urban intersection.