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    <title>Research in Progress (RIP)</title>
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    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
    <docs>http://blogs.law.harvard.edu/tech/rss</docs>
    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
    <image>
      <title>Research in Progress (RIP)</title>
      <url>https://rip.trb.org/Images/PageHeader-wTitle-RIP.jpg</url>
      <link>https://rip.trb.org/</link>
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    <item>
      <title>Ensuring PNT Resiience</title>
      <link>https://rip.trb.org/View/2676001</link>
      <description><![CDATA[With CARMEN+ support the research team has characterized the timing properties of modulation from the Starlink constellation in order to assess its suitability for providing opportunistic pseudorange-based positioning, navigation, and timing (PNT) as a backup to Global Navigation Satellite System (GNSS). With the same purpose, the team has also uncovered key features of the OneWeb signal structure and has demodulated its data for the first time. The findings have indicated that opportunistic pseudorange-based PNT is not feasible using Starlink signals without aiding from a network of ground receivers. But given such a network, the team has achieved 10-meter-level positioning and 30-ns timing using Starlink signals. The next phase will extend this project along several lines: (i) characterize the modulation timing stability of OneWeb as the team has done with Starlink, (ii) deploy a network of 2 or 3 reference stations so that all ephemeris and transmission time modeling errors may be eliminated, (iii) employ super-resolution techniques to more precisely estimate modulation (e.g., Starlink frame) time of arrival, and (iv) analyze the pattern of assigned beams and side beams from Starlink satellites to predict how many unique satellites would typically be available for a PNT solution, and with what dilution of precision. For these studies, the team will capture and analyze broadband Starlink, OneWeb, and Kuiper data with their own RF equipment from multiple stations. The team believes that the outcome of this work will be of great importance, namely, a backup PNT system with global reach, decimeter positioning, nanosecond timing, inherent signal authentication (via cross-checking unpredictable broadband payload data and against a reference network), and improved resistance to jamming compared to traditional GNSS. Furthermore, the team aims to transfer this technology to their project partners for commercialization.]]></description>
      <pubDate>Mon, 02 Mar 2026 19:15:21 GMT</pubDate>
      <guid>https://rip.trb.org/View/2676001</guid>
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    <item>
      <title>PNT Protection through Interference Monitoring and Robust Backups to GNSS </title>
      <link>https://rip.trb.org/View/2458990</link>
      <description><![CDATA[Central to civil infrastructure (e.g., transportation, communications) is the problem of robust and secure position, navigation and timing (PNT). This is especially true of urban environments, which offer more challenges, such as interference and multipath, when compared to open-sky conditions. As the only positioning system that currently offers absolutely-referenced meter-level accuracy with global coverage, the Global Navigation Satellite System (GNSS) will no doubt play a significant role in civil infrastructure going forward. For example, if strengthened against jamming and spoofing, carrier-phase-differential GNSS (CDGNSS), coupled with low-cost inertial sensing, will be nearly sufficient for position, velocity, and timing (PVT) needs of urban air mobility (UAM). But nearly sufficient is insufficient: it is not enough for a UAM PVT solution to offer decimeter-accurate positioning with 99% availability, or even 99.9% availability. UAM will demand that its navigation systems offer dm-accurate positioning with integrity risk on the order of 10e-7 for a meter-level alert limit and availability with several more 9s than 99.9%
This proposal’s work in PNT protection is best viewed as part of a comprehensive navigation and timing solution concept called deep-layered navigation (DLN) in which synergistic but independent navigation systems are layered to increase accuracy and robustness. DLN is the navigation analog of the “defense in depth” concept in information security, where multiple layers of security controls and checkpoints are emplaced throughout a system such that even when some layers are breached, security is maintained. Likewise, in the safety-of-life UAM navigation context, multiple layers of navigation and timing systems, all interoperable and mutually-reinforcing but substantially independent, are an essential defense against the whims of Mother Nature and the foibles of human nature.
At DLN's core sits redundant inertial navigation, which is virtually impervious to radio frequency (RF) interference, poor weather, signal blockage, and data ambiguity. The outermost layer---the default navigation system and first line of defense---is a specialized variant of inertially-aided CDGNSS, recently developed in the UT Radionavigation Laboratory that has been substantially secured against spoofing and substantially hardened against the multipath and signal blockage conditions of the urban ground vehicle environment, which can be considered a worst-case realization of the urban air vehicle environment. But despite its coupling with inertial sensing, the technique developed in the UT RNL cannot tolerate extended GNSS outages. A secondary source of absolute PVT is required to bound the growth of position errors.
Fusion of GNSS and signals of opportunity (SoPs) has been explored by the CARMEN+ team with promising results. For a safety-of-life application like UAM, SoPs will be viewed less favorably than a dedicated system such as GNSS. But SoP availability, ample bandwidth, and strong received power nonetheless make them an appealing component of any deep-layered navigation and timing strategy. Compared to terrestrially-sources SoPs, SoPs from LEO mega-constellations such as SpaceX's Starlink, Amazon's Kuiper, and OneWeb enjoy the advantage of vastly broader---indeed, nearly global---coverage. The downlink signals from these constellations are also carried on higher frequencies (Ku-band for Starlink and OneWeb; Ka-band for Kuiper), whose short wavelengths permit use of small phased-array receiver antennas capable of forming narrow (few degrees) beams toward their serving satellites. Narrow beamforming makes such systems naturally resilient to radio interference.
The research team proposes to develop novel signal capture, processing, and estimation techniques that will enable extraction of navigation and timing measurements from broadband mega-LEO signals of opportunity. The research team envisions broadband LEO-based PNT as a novel and useful component in a comprehensive deep-layered navigation and timing system for civil transportation and infrastructure. GNSS receivers in Low Earth Orbit (LEO) are a proven asset for detecting, classifying, and geolocating terrestrial GNSS interference that can be a danger to civil aviation, maritime, or ground vehicle traffic. Emitter geolocation from LEO offers worldwide coverage with a frequent refresh rate, making it possible to maintain a common operating picture of terrestrial sources of interference. The research team proposes to further develop and demonstrate techniques whereby GNSS interference signals captured by a single satellite or by multiple satellites can be used to characterize and geolocate the signals’ source. The research team will pursue single-satellite, multi-satellite-indirect, and multi- satellite-direct techniques to obtain geolocation solutions optimized for context and performance. The research team's focus in this next year will be on single-satellite geolocation solutions.
]]></description>
      <pubDate>Thu, 21 Nov 2024 17:26:16 GMT</pubDate>
      <guid>https://rip.trb.org/View/2458990</guid>
    </item>
    <item>
      <title>Railway Right of Way Monitoring and Early Warning System (RailMEWS) Based on Satellite and Aerial Imagery</title>
      <link>https://rip.trb.org/View/2353427</link>
      <description><![CDATA[Description: In this Phase-I, one-year project the research team proposes to conduct feasibility studies and provide recommendations for the development of a Railway Right of Way Monitoring and Early Warning System (RailMEWS). The feasibility study will answer the following questions:

How can we use drones and satellites to monitor the railway infrastructure?
What infrastructure components can be monitored effectively and what are the potential limitations of a RailMEWS in each case?
What railway Infrastructure Monitoring Systems (IMS) are available today for integration with satellite and drone data?
What are the desired functions and design parameters of an Early Warning System? (e.g. connectivity to the signaling system, real-time vs. centralized processing, etc)
What is the incremental investment needed to develop the RailMEWS system?
Intellectual Merit: The team proposes to process information obtained from commercially available satellite and aerial imagery and combine with information obtained by conventional monitoring systems to develop maps that show the kinematic behavior of the railway infrastructure at the terrain, sub-structure and track scales

Broader Impacts: The proposed work will set the foundations for the development and implementation of a RailMEWS to the railway network, its expansion to other transportation modes and its implementation to the transportation network statewide and beyond. This research will set the framework for larger research projects with diverse research partners and will be ex-tended in future studies to monitor the roadway network, ports, and inland ports simultaneously and will be integrated with their respective signaling systems.]]></description>
      <pubDate>Mon, 25 Mar 2024 15:46:14 GMT</pubDate>
      <guid>https://rip.trb.org/View/2353427</guid>
    </item>
    <item>
      <title>Towards GNSS-less Navigation: Exploiting Terrestrial and LEO Satellite Signals of Opportunity</title>
      <link>https://rip.trb.org/View/2321512</link>
      <description><![CDATA[Today’s vehicular navigation systems extract position information from a suite of diverse and complementary onboard sensors. For example, a global navigation satellite system (GNSS) receiver provides stable absolute position information and an inertial measurement unit (IMU) and other dead reckoning sensors (e.g., wheel encoders) provide short-term accurate information. After prolonged periods of GNSS signal unavailability, the position solution degrades to unsafe levels as error-corrupted dead reckoning information is integrated without correction from an absolute position information source. Vehicle-mounted sensors (e.g., cameras or lidar) can reduce IMU drift during GNSS unavailability by tracking features in the environment (e.g., walls, light poles, trees, etc.) and then inferring the vehicle’s relative motion with respect to the features via a simultaneous localization and mapping (SLAM) framework. However, after extended periods of time without GNSS aiding corrections, the vehicles’ position estimate will still drift due to the accumulation of sensor errors (e.g., camera scale factor and lidar range errors due to dust and water particles). Over the past decade, signals of opportunity (SOPs); such as AM/FM radio, cellular, digital television, and low Earth orbit (LEO) satellite signals; have been studied and demonstrated as an effective backup or alternative source of absolute positioning information, providing corrections to an inertial navigation system (INS) in the absence of GNSS signals. SOPs possess several desirable characteristics for vehicular navigation: (1) available in most environments of interest; (2) difficult to jam all SOPs, since their signals are scattered throughout the spectrum; (3) produce low geometric dilution of precision, since their transmitters are geometrically diverse; (4) signal reception with carrier-to-noise ratio that is often tens of decibels (dBs) higher than that of GNSS signals; (5) free to use with SOP navigation receivers that do not require network subscriptions; and (6) no deployment cost, since their infrastructure is already operational and maintained by service providers. This project will study the achievable performance of GNSS-less navigation with SOPs, with a focus on cellular 5G and LEO. The study will compare the performance as a function of: (1) number of utilized transmitters (terrestrial 5G alone, LEO alone, and a fusion of both); (2) differential versus non-differential frameworks; (3) fusion with other onboard sensors; and (4) sensitivity to model mismatch.
]]></description>
      <pubDate>Thu, 11 Jan 2024 16:07:04 GMT</pubDate>
      <guid>https://rip.trb.org/View/2321512</guid>
    </item>
    <item>
      <title>GNSS Interference: Situational Awareness and LEO Backup</title>
      <link>https://rip.trb.org/View/2301351</link>
      <description><![CDATA[Global Navigation Satellite System (GNSS) receivers in Low Earth Orbit (LEO) are a proven asset for detecting, classifying, and geolocating terrestrial GNSS interference that can be a danger to civil aviation, maritime, or ground vehicle traffic. Emitter geolocation from LEO offers worldwide coverage with a frequent refresh rate, making it possible to maintain a common operating picture of terrestrial sources of interference. The research team proposes to further develop and demonstrate techniques whereby GNSS interference signals captured by a single satellite or by multiple satellites can be used to characterize and geolocate the signals’ source. In particular, the team will pursue so-called direct techniques that yield geolocation solutions even in cluttered (many interference sources) environments. A particular challenge the team seek to overcome is making direct techniques more efficient to enable “quick scan” geolocation.

A secondary thrust of this project will be to further study the possibility of using signals from mega-constellations of LEO satellites designed for broadband communications as a backup to GNSS for PNT. The team has already identified structures in Starlink signals that will be useful for measuring the time of arrival (TOA) of Starlink frames. But whether the team can form pseudo range measurements that are useful for PNT remains an open question whose answer depends on the timing properties of the Starlink signals. The team will seek to elucidate these timing properties. Moreover, the team will seek to answer the following questions: (1) From how many unique SVs, on average, can a user receive assigned or side beams strong enough for accurate TOA measurements? (2) Is there a simple pattern that can be used to predict channel-beam-SV assignments? (3) What is the range of absolute received power into a hemispherical antenna for an assigned or side beam?
]]></description>
      <pubDate>Mon, 04 Dec 2023 17:27:56 GMT</pubDate>
      <guid>https://rip.trb.org/View/2301351</guid>
    </item>
    <item>
      <title>Resilience and Validation of GNSS PNT Solutions</title>
      <link>https://rip.trb.org/View/1986928</link>
      <description><![CDATA[The study team will take a proactive approach to assess vulnerabilities in specific case studies. They will also develop new PNT sensing techniques for HATS that are both robust in the face of unusual natural or accidental events and secure against deliberate attack. The team's insights will then be generalized, leading to the development of broad PNT protocols, procedures, and recommendations for standards. In addition, to investigate possible mitigation strategies in the event of a PNT lapse, much insight can be gained by creating a set of hypothetical scenarios in a traffic assignment model that simulate and characterize the effects of possible attacks. The measured severity of problems in each scenario can then inform an estimation of risk and the best types of mitigation. For example, scenario results can highlight the positive and negative aspects of broadly safeguarding selected traffic corridors or urban regions instead of finely limiting possible damage to individual intersections. ++++++++++

(Task 3.3) Low-Cost Ground Vehicle Anti-Spoofing: 
The study team proposes to develop a technique that exploits road roughness, MEMS IMUs, and carrier phase GNSS to achieve low-cost anti-GNSS-spoofing appropriate for mass-market ground vehicles. The technique will continuously monitor a detection statistic that measures the consistency between the millimeter-precise received GNSS carrier phase measurements and accelerometer measurements. Spoofing is declared when the detection statistic crosses a constant-false-alarm-rate threshold.  The results will be a low-cost GNSS spoofing detection technique that enables a ground vehicle to reliably and promptly detect code-phase-aligned GNSS spoofing despite multipath, signal blockage, and other urban effects. ++++++++++

(Task 3.5) Physics-Based Anomaly Detection: 
The study team proposes to evaluate existing cyber-layer defense PBAD strategies and also develop new ones for PNT threats to HAVs by (1) identifying promising data sources in HAVs (e.g., sensing data and related actuators) that can potentially correlate with the PNT inputs, (2) understanding their correlations and constructing physical invariants, (3) designing online anomaly detection algorithms corresponding to the identified physical invariants, and (4) evaluating the performance in real-world HAVs.  The team will quantify the safety and mobility consequences (e.g., increased crashing rate and string stability) from real-world or simulation-based experiments for each class of PNT threats and design and implementation of the mitigation methods leveraging the PBAD method. ++++++++++

(Task 3.6) Machine Learning PNT Model Security: 
Some PNT sensors used in HAVs (e.g., lidars and cameras) predominately rely on deep neural networks (DNNs). The study team proposes to perform risk analysis and identify mitigation methods for security issues at the DNN model level. Specifically, in the recent race between DNN attacks and defenses, numerous defense/mitigation techniques have been proposed, e.g., model ensembling, randomization, image transformation, and adversarial training. The team plans to first explore domain-specific adaptions of existing defense strategies in other application domains to 10the DNN models used in PNT algorithms and then leverage the insights to design effective solutions specific to PNT threat scenarios.  The team will quantify the safety and mobility risks from real-world or simulation-based experiments under deep neural network (DNN) based PNT model attacks, and design and implementation of mitigation methods, such as model architecture changes and adding model input sanitization. ++++++++++

(Task 4.4) Receiver Data Validation by PNT Solutions from Other Sources:	
Data to create or contribute to an alternative PNT solution that can be used to validate a GPS-based solution may come from different sensors, deployed on different platforms and acquired at different times. Sensors, such as radar, lidar, vision camera, etc., on an HAV provide data in real-time. In contrast, an HD map represents sensor data acquired earlier and structured for some purpose. Connected HAVs may share both their PNT solutions and sensor data. The signals used to observe the environment and then create a PNT solution may come from infrastructure dedicated to positioning, such as GPS or from other sources of natural and man-made ones. Examples of man-made SoPs include cellular and LEO satellites. Natural signals include Earth gravity, magnetic field, and surface-based signals used in terrain-based navigation.  Other CARMEN project will provide discussion on many of these alternative PNT solutions. In this work the study team will combine both the theoretical and experimental results into a performance assessment/validation document. Note that the reliability and vulnerability of the various sensor streams may not be considered in the analysis here due to resource limitations.
]]></description>
      <pubDate>Wed, 29 Jun 2022 14:41:35 GMT</pubDate>
      <guid>https://rip.trb.org/View/1986928</guid>
    </item>
    <item>
      <title>PNT with Signals of Opportunity and Real-World Jammed and Spoofed Environments</title>
      <link>https://rip.trb.org/View/1986929</link>
      <description><![CDATA[(Task 3.4) Robust and Accurate Ground and Aerial HAV PNT with signals of opportunity (SOPs): 

The ASPIN Lab is at the forefront of exploiting terrestrial and low-earth orbit (LEO) satellite SOPs for robust and accurate PNT in GNSS-degraded and GNSS-denied environments. An accurate navigation solution in a global frame can be produced from signals of opportunity (SOPs) in a standalone fashion and an integrated fashion, aiding lidar and IMU. Towards this objective, the study team's proposed research is fourfold. First, the study team proposes to develop a comprehensive real-time kinematic (RTK) approach to exploiting cellular and LEO satellite signals for ground and aerial vehicle positioning, navigation, and timing (PNT). Second, the study team plans to assess SOP-derived PNT availability, continuity, timing stability, and performance over long periods of GNSS unavailability in different environments: open-sky, rural, urban, and deep urban. Third, the study team proposes to develop multi-signal, multi-sensor integrity monitoring frameworks, capable of handling multiple simultaneous faults. Fourth, the study team plans to develop GPS antispoofing mechanisms, utilizing the additional sensing modality SOPs provide.

The outcome of this research will be a detailed real-time kinematic (RTK)-based framework for exploiting cellular and LEO satellite SOPs for  ground and aerial vehicle PNT with HAV performance guarantees. ++++++++++

(Task 5) Validation in Real-world Jammed and Spoofed Environments: 

The study team plans to validate their research outcomes via (1) sophisticated, high-fidelity simulators (RF, GNSS, PNT sensors, vehicle, transportation networks) and (2) experimentally on ground vehicles, UAVs, and high altitude aircraft, where GPS/GNSS signals are subjected to “simulated” jamming and spoofing attacks. To achieve the most impactful outcomes, the team will validate their developed research methodologies and proposed standards in real-world jammed and spoofed environments. PI Kassas has signed a five-year EPA with the Department of the Air Force (2019–2024), allowing his research team access to conduct experiments at Edwards AFB, CA. The team plans to participate in a two-week long, biennial GPS jamming exercise, known as Developmental Test Navigation Festival (NAVFEST), with different jamming waveforms and jamming-to-signal ratio.

]]></description>
      <pubDate>Wed, 29 Jun 2022 14:36:53 GMT</pubDate>
      <guid>https://rip.trb.org/View/1986929</guid>
    </item>
    <item>
      <title>Test of COTS Satellite Communications Systems
</title>
      <link>https://rip.trb.org/View/1367924</link>
      <description><![CDATA[The objectives of this research project are to: (1) test of Satellite Communications Systems on-board Suborbital Platforms to provide low-cost data communications for Research Payloads, Payload Operators, and Space Vehicle Operators, and government agencies such as the Federal Aviation Administration (FAA) and National Aeronautics and Space Administration (NASA).  The satellite systems to be tested include, but are not limited to, Iridium, Globalstar, and Inmarsat; and (2) develop payloads, processing processes and facilities for university and government payloads in collaboration with New Mexico State University.
]]></description>
      <pubDate>Thu, 03 Sep 2015 10:19:56 GMT</pubDate>
      <guid>https://rip.trb.org/View/1367924</guid>
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    <item>
      <title>Route and Site Characterization Using Multi-Spectral Satellite Imagery</title>
      <link>https://rip.trb.org/View/1339691</link>
      <description><![CDATA[Transportation agencies spend enormous sums of money each year in assessing the appropriateness of alternative highway alignments or other transportation related construction sites, both from the standpoint of constructability and environmental impact. These assessments could be simplified and more focused if planners possessed a screening tool that could graphically illustrate surface and subsurface features such as potential wetland areas, springs and seeps, slope instabilities, etc. over a wide geographical area. The technology exists to create such a screening tool by analyzing the ground's reflectance and adsorption of the non-visible light spectrum. However, the interpretation of this spectral imagery must be calibrated to known conditions before it can be useful for the prediction of ground features. This calibration process has not been done for the State of Arkansas, but the data currently exists as a result of previous MBTC projects to do it for limited areas within the state. Once various important ground features are identified from spectral imagery they can be incorporated into a Geographic Information System (GIS) along with other cultural socio-economic and political attributes to create a robust planning tool for transportation related construction projects.]]></description>
      <pubDate>Thu, 01 Jan 2015 01:29:44 GMT</pubDate>
      <guid>https://rip.trb.org/View/1339691</guid>
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