Research Roundup: Mitigating GNSS Interference


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GNSS researchers are presenting hundreds of papers at the Institute of Navigation (ION) GNSS+ Conference 2022, taking place September 19-23 in Denver, Colorado, and virtually. The next five articles focus on GNSS receiver technology and interference mitigation. Articles will be available at


Conference Presentation: Sept. 23, 1:50 p.m.; Session F6

The increasing reliance of critical and life-safety systems on GNSS makes the ability to quickly detect and locate the presence of GNSS interference events increasingly important. Ground-based GNSS jammer detection can be used to detect local interference sources. However, this approach is limited by line of sight, so applying it to large areas is costly and time consuming.

A complementary technique is to use airborne GNSS receiver data provided by Automatic Dependent Surveillance-Broadcast (ADS-B). Because these receivers are at altitude, their lines of sight can cover a wide area. The downside is that ADS-B was not designed for this purpose and the messages contain limited information for interference assessment.

The authors have developed and will demonstrate an algorithm for real-time detection and localization of GNSS interference sources using ADS-B transmissions on the 1090 MHz radio frequency channel (Mode S ES). They demonstrate this capability using recorded ADS-B transmissions from known interference events.

Zixi Liu, Sherman Lo, Todd Walter, Juan Blanch, Stanford University; “Real-time detection and localization of the GNSS interference source.”


Presentation at the conference: September 23, 4:04 p.m.; Session F6

Even low-level interference can be catastrophic for systems that rely on GNSS. It can prevent GNSS signals from reaching the user (interference or jamming) or give false signals, resulting in incorrect position and time solution (spoofing). The ability to quickly and confidently detect and locate interference could help mitigate this threat. Furthermore, if the system could also provide information characterizing the interference, it could help law enforcement not only interdict, but also prosecute the threat.

Building a consumer-level commercial off-the-shelf (COTS) GNSS monitor would also make it cost-effective for widespread use. This article describes the development and field testing of a system providing this capability.
The monitor uses the u-blox F9, an inexpensive commercial receiver offering multi-constellation and dual-frequency position and time solutions, as well as powerful interference detection measurements. Initial analysis of the receiver’s measurement capabilities determined that it offers many useful features for assessing the operational environment in a geographic region. Receiver performance and output are characterized under different jamming and spoofing scenarios.

Different receivers and antennas may react differently depending on hardware and software configurations and offer the user different interference rejection techniques and detection measures. Therefore, it is important to fully understand the behavior of the receiver. Another way to test the behavior is to examine its performance under nominal conditions in various scenarios and locations, as shown in this article.

Benon Gattis, Dennis Akos, University of Colorado at Boulder; Yu-Hsuan Chen, Sherman Lo, Todd Walter, Stanford University; “Tests and Measurements from a Global Navigation Satellite System (GNSS) Monitoring System”.


Presentation of the conference: virtual; Session F6

With the availability of RAW GNSS measurements on Android smartphones, GNSS interference detection using modern handsets has become a realistic possibility for crowdsourcing, especially with the inclusion of Automatic Gain Control (AGC) in Android 8 ( Oreo).

While crowdsourced jamming detection — and knowing if your smartphone is prone to jamming or spoofing — is valuable, locating the source of interference can be even more important. This work explores the feasibility of crowdsourcing interference source localization with modern Android smartphones.

The work has three objectives:

  • Investigate the location of a civilian L1 GPS jammer using a network of smartphones
  • Investigate how best to address current barriers to such localization
  • To estimate the accuracy of this type of positioning.

An important part of this work is to investigate the differences in GNSS data reported by various Android smartphones. The smartphones in this study were specifically selected by the GNSS chipset manufacturer to allow the authors to examine the performance of their GNSS receivers under the same circumstances. Three parameters have been specifically studied as measures of received interference power: the carrier-to-noise ratio (C/N0), the AGC and the number of satellites tracked.

The selected smartphones were subjected to a series of tests to examine how these three parameters vary according to changing conditions. These tests include subjecting the smartphones to a real jammer in a controlled laboratory setting and investigating the impact of the position and orientation of the smartphone (GNSS antenna) on C/N0 and AGC. Using the data collected during these tests, several interference geolocation strategies will be discussed.

The authors also investigate whether consumer smartphone interference localization (COTS) is currently accurate enough for this use. Shortcomings in smartphone GNSS hardware can be addressed by using smarter positioning strategies, such as using more handsets. Alternatively, it may require a hardware upgrade and standardization.

Søren Skaarup Larsen, Daniel Haugård Olesen, Anna BO Jensen, Lars Stenseng, Technical University of Denmark, DTU Space; “Evaluation of RFI geolocation using modern Android smartphones.”


Presentation at the conference: Sept. 21, 4 p.m.; Session F2

Multipath mitigation with machine learning relies on offline training with an exhaustive number of labeled observations. Current super-resolution correlation methods, which include MULTIPLE SIGnal Classification, operate online by testing and choosing from a large number of candidate signal hypotheses.

A new MUSIC method is presented which reduces numerical complexity and is applied to the processing of L5 correlation vectors to reduce multipath by identifying the first path. The rank of this estimator is examined under static and dynamic conditions in various signal environments. A higher rank allows more signal paths to be identified.

This method is also complementary with various L5 signal tracking methods such as open and closed loop tracking.

Paul McBurney, Norman Krasner, Florean Curticapean, Miguel Ribot, Mahdi Maaref and Lionel Garin, OneNav; “Application of Super-Resolution Correlation to Multipath Attenuation in an L5 Channel.”


Presentation of the conference: Sept. 22, 11:03 a.m.: Session F3

One of the easiest ways to increase the anti-jamming and anti-spoofing (AJ/AS) performance of GNSS is to increase the number of controlled reception pattern antenna array elements (CRPA ). However, this increases the size, cost, complexity, and required processing power of the overall system. To counter this constraint, the researchers applied a new development in antenna hardware design to GNSS threat mitigation techniques. This improved the performance of the CRPA without increasing the footprint. The work improves AJ/AS performance without adding additional elements and serves as a proof of concept of the application of an adaptive spacing virtual network created with multimodal elements to GNSS AJ/AS.

New breakthroughs in antenna array research extend the case of non-uniform element excitation to individual element positions. Using multimodal antennas as elements, it has been demonstrated that the phase centers of the elements, or perceived locations, can be adjusted with purely electronic means. When applied to each element of an antenna array, this achieves a reconfigurable array.

This research extends the concept of a virtual network with adaptive inter-element spacing in GNSS AJ/AJ methods. A new way to integrate a virtual network into a GNSS application is explored and incorporated into current space-time adaptive processing (STAP) algorithms.

Gabriel Wiggins and Scott Martin, Auburn University; “Applications of a Virtual Antenna Array to GNSS Threat Mitigation: First Results.”


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