Research Areas

Research at the Radionavigation Laboratory can be broadly categorized into five main areas: secure perception, software-defined radio, robust perception, precise mass-market mobility, and remote sensing.  

antispoofThe next few decades will see pervasive autonomous control systems become critical to the world economy—from autonomous cars and aircraft to smart homes, smart cities, and vast energy, communication, and financial networks controlled at multiple scales. Protecting these systems from malicious attacks is a matter of urgent societal interest.  

Our secure perception research has focused on an emergent category of cyber-physical attack that has seen little scrutiny in the secure control literature. Like cyber attacks, these attacks are hard to detect and can be executed from a distance, but unlike cyber attacks, they are effective even against control systems whose software, data, and communications networks are secure, and so can be considered a more menacing long-term threat. These are field attacks: attacks on the physical fields—electromagnetic, magnetic, acoustic, etc.—measured by system sensors. As specialized sensor attacks, field attacks seek to compromise a system’s perception of reality non-invasively—from without, not from within.  

We are striving to develop a coherent analytical foundation for secure perception in the presence of field attacks and a suite of algorithms and tools to detect such attacks. A key insight behind our approach is that the physics of field attacks impose fundamental difficulties on the attacker.  As with the one-way functions that underpin public-key cryptography, there are tests which are fundamentally difficult to circumvent even in the presence of process and measurement noise and when the system state is not fully observable from unaffected sensors. Our approach seeks to progressively build security into navigation, collision avoidance, and timing perception from the physical sensory layer to the top-level state estimation algorithms.


Recent Publications:


Besides securing autonomous system perception against deliberate attack, another imperative is to robustify perception to ensure reliable autonomous system navigation, collision avoidance, and timing despite harsh sensing environments. Our work on this topic has two themes:

  1. precise vision-based sensing, and
  2. massive signal-of-opportunity exploitation.

Our work in vision-based sensing is a novel fusion of GPS carrier phase measurements with camera images at the level of the so-called bundle adjustment process that is central to robust visual simultaneous localization and mapping (SLAM). In future work, our technique will attempt joint visual SLAM and carrier integer ambiguity resolution. If we are successful, the result will be a tight fusion of GPS and visual sensing that will be highly trustworthy due to the extraordinary richness and strong keyframe-to-keyframe correlation of the visual data.  Our second approach to robust perception extracts navigation and timing information from a large set of heterogeneous terrestrial and satellite signals-of-opportunity—essentially a diversity approach to robustness. The cooperative opportunistic navigation concept is at bottom an exercise in highly agile software-defined radio and in signal landscape SLAM, which differs from traditional landmark-based SLAM in that the landscape is dynamic. We have resolved fundamental questions of joint landscape and receiver state observability, and have built a sophisticated software-defined multi-system radio. This groundwork provides a launching point for a broader and deeper study of cooperative navigation and timing extraction based on signals of opportunity.


Recent Publications:

handGPS chipsets are getting smaller, cheaper, and more energy efficient. They are now ubiquitous in smartphones and tablets, enabling a host of location-based services. But the underlying positioning accuracy of consumer-grade GPS receivers has stagnated at approximately 2-3 meters.  We are engaged in bringing about the next revolution in consumer-grade mobile positioning, which will take us to centimeter accuracy.  The challenges of cm-accuracy on consumer devices are daunting:

  1. The GPS antennas on mobile handsets and tablets are little better than smashed paper clips. Their poor quality (15-20 dB below that of even a cheap patch antenna and dismal multipath mitigation) makes it extremely challenging to extract carrier phase measurements accurate enough for fast fixing of the integer ambiguities that arise in the carrier-phase differential technique. And mobile users are impatient: they may be persuaded to wait 30 seconds for a cm-accurate position fix, but only a resolute few would hold out for longer. 
  2. Differential carrier-phase-based positioning is power hungry compared with standard code-phase positioning. On a mobile device, milliwatts matter.
  3. Lack of a killer app.

We are working on innovations to meet these challenges. The most promising of these innovations exploits a mobile device’s camera in a near-optimal combination of visual SLAM and centimeter-accurate carrier phase differential GPS adapted for mobile devices. This combination will produce not only precise precision for mobile devices but a complete and precise pose (position and orientation), which will be a key enabler for convincing mobile augmented reality (AR).  We believe that video-see-through AR built on this technology has the potential to become a must-have application for future mobile devices.


Recent Publications:


Radio-frequency navigation and timing signals can be excellent sources of remotely-sensed science data, revealing structural details of the ionosphere and neutral atmosphere. Perhaps the most promising technique is GPS-based radio occultation (GPSRO), which yields electron density and precipitable water vapor or temperature profiles useful for numerical weather prediction (including space weather). Together with colleagues at UT and Cornell University, we have developed the first software-defined GPSRO sensor suitable for deployment on a cubesat.  We have also developed an instrument for ionospheric scintillation monitoring, which leverages our work in software-defined radio and applies techniques we developed for robust GPS signal tracking during scintillation.


 Recent Publications:







  • Cornell Scintillation Simulation Toolkit: This package allows the user to simulate ionospheric scintillation with a given S4 and τ0. Functions are written in Matlab and produce a data output file that can be input to a Spirent GPS Signal Simulator. This can be useful both for comparing multiple receivers when tracking under identical scintillation conditions, and for testing receiver performance under a wide range of scintillation severity.  The simulator is based on the paper Humphreys, Todd E., et al. "Simulating ionosphere-induced scintillation for testing GPS receiver phase tracking loops." IEEE Journal of Selected Topics in Signal Processing 3.4 (2009): 707-715.

Development and Evaluation of Anti-Spoofing Techniques for Civil GNSS

GNSS Interference Detection and Localization

The GPS Assimilator: A Method for Upgrading Existing GPS User Equipment to Improve Accuracy, Robustness, and Resistance to Spoofing


     UAVs have incredible potential for a variety of practical applications.  However, multiple hurdles lie in the way of everyday use cases for UAVs.  The Machine Games project will explore solutions to these problems in a real operating environment using our recently-completed ARENA (Autonomous Robotics Exploration Netted Arena).  Our goal is to bring drones into everyday life but there is a bevy of challenges which must first be overcome.  We identify three major problems facing mass-market adoption of drones:

1)  There is a need for a method via which a lone individual can control a large network of autonomous vehicles.  Current solutions to this problem either place too many burdens on the operator or allocate most of his control to a computer—either the computer or the operator are being used to his/her fullest, never both.  We hope to use the ARENA to study ways in which an operator and computer can cooperatively control autonomous systems.  One of our goals is to ensure that this mechanism makes sense at an intuitive level for an untrained user—a necessary step for drones to achieve mass-market success.

2)  Many studies, such as the Mesicopter project, have noted that existing techniques for both estimation and control are insufficient for the needs of everyday drone use.  Controllers may perform well in highly constrained environments but lab performance does not translate well to more realistic operating conditions.  Existing manners of performing position and attitude estimation are also insufficient for mass-market use, either due to inaccuracies in sensing or unreasonably high costs.  Machine Games will investigate and tackle both the control and estimation problems which stand in the way of everyday use.

3)  There exists a need for a defense system which can take down a rogue drone with limited risk to bystanders, infrastructure, or the system itself.  This lack was highlighted in January 2015 when a pilot lost control of a drone which crashed into the lawn of the White House.  At the time, no systems for downing rogue drones were in place and there is still a lack of a system which can safely remove UAV threats near dangerous or high-risk areas, such as airports.  Tactics for counter-UAV include spoofing, cannon-launched nets, and trained attack birds, though these solutions each have major flaws.  We hope to study methods for counter-UAV operations which can remove a rogue aircraft with limited risk to bystanders or infrastructure.

     Through the course of the life of the ARENA, we will investigate a variety of open questions in security and autonomy which stand in the way of mass-market adoption of UAVs.  Many of the challenges we investigate will have impacts outside of the field of drones; for example, questions of position determination are relevant to autonomous cars.  The solutions we develop to these three problems will help guide legislation to promote drone use without compromising safety, will drive entrepreneurs to invest into otherwise dangerous propositions, and will encourage the use of UAVs as a solution to commonplace problems.

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