Author: Kyle O'Keefe

ION Alberta Virtual Meeting – Tuesday April 14, 2026

We are excited to announce our next Alberta ION lecture series, where Dr. Daniele Borio will present their work on GNSS meta signal processing. This will be a virtually hosted meeting.

Title: GNSS Meta-Signals: from Signal Processing to Carrier Phase Ambiguity Resolution

Author: Daniele Borio, European Commission, Joint Research Centre

Dr. Daniele Borio is a scientific technical officer in the “Land and Climate” unit of the European Commission (EC) Joint Research Centre (JRC), Ispra, Italy.
His research interests include the fields of digital signal processing, location and navigation with specific emphasis on Global Navigation Satellite Systems (GNSS).
Dr. Borio has been developing the theory and practice of GNSS meta-signals and his contributions were recognized by the US Institute of Navigation (ION) with the 2025 Thurlow Award and the 2023 Burka Award. Since 2013, Dr. Borio has been contributing to the ESA/JRC International Summer School on GNSS, providing lectures on GNSS threats and organizing labs on GNSS signal processing. The research activities of Dr. Borio led to the publication of more than 70 peer-reviewed journal papers and more than 100 conference papers.

From January 2008 to September 2010, Dr Borio was a senior research associate at the University of Calgary, Canada. He received the M.S. degree in Communications Engineering from Politecnico di Torino, Italy, the M.S. in Electronics Engineering from ENSERG/INPG of Grenoble, France, and his PhD in electrical engineering from Politecnico di Torino in April 2008.

Abstract:

A Global Navigation Satellite System (GNSS) meta-signal is obtained when at least two GNSS signal components, broadcast on different frequencies, are jointly processed as a single entity. This concept was originally introduced for wide-band signals such as the Galileo Alternative Binary Offset Carrier (AltBOC), which is obtained by combining the E5a and E5b signals. These components can be processed independently using standard methods already available for legacy signals such as the GPS L1 Coarse/Acquisition (C/A) modulation and used to generate independent measurements for both the E5a (1176.45 MHz) and E5b (1207.14 MHz) frequencies.

While independent processing offers implementation efficiency, it does not fully exploit the benefits of a wide-band modulation such as the AltBOC, which enables highly accurate pseudorange estimation. A possibility is to combine measurements from the E5a and E5b components and reconstruct in a synthetic way, i.e. without requiring modifications at the receiver signal processing stage, AltBOC carrier phases and pseudoranges. This presentation demonstrates that this approach is strictly related to the narrow- and wide-lane dual-frequency combinations, which need to be formed for the meta-signal measurement reconstruction. Specifically, integer cycle ambiguities must to be solved for the wide-lane carrier phase combination, which is then used for the reconstruction of AltBOC pseudoranges. Ambiguity resolution is performed using the Hatch-Melbourne-Wübbena combination, which naturally arises in the synthetic meta-signal paradigm. The narrow-lane dual-frequency carrier phase combination is used for the reconstruction of AltBOC carrier phase measurements through a half-cycle ambiguity resolution process. This reveals that narrow- and wide-lane carrier ambiguities are strictly related through parity constraints, a relationship that can be leveraged in traditional carrier phase processing.

When considering high-order meta-signals, which involve components from more than two frequencies, concepts developed in the context of Three-Carrier Ambiguity Resolution (TCAR) and Four Carrier Ambiguity Resolution (FCAR) emerge. In these approaches, carrier integer ambiguities are solved in a cascaded way considering linear combinations with progressively decreasing wavelengths. For instance, extra-wide-lane combinations are introduced to facilitate single-epoch ambiguity resolution. These concepts naturally emerge when dealing with meta-signals with more than two components. This presentation reviews the synthetic meta-signal approach and reinterprets standard concepts such as linear measurement combinations with respect to the properties of GNSS meta-signals. For instance the concept of signal subcarrier, already emerging with the Binary Carrier Offset (BOC) modulation, is linked with the wide-lane carrier combination. This illustrates once more how the different GNSS receiver processing levels are strictly intertwined, showing that concepts arising in one domain, for instance at the signal processing stage, find their counterpart in other domains, such as the measurement processing stage.

Please contact a member of the executive if you would like to attend and did not receive the meeting link through the mailing list

Date: Tuesday, April 14, 2026

Time: Meeting will open at 11:45am, presentations to begin shortly after 12:00

Cost: Free

ION Alberta In-person Meeting – Thursday, 23 October 2025

Topic: Student Papers from ION GNSS+ 2025

Speaker 1: Paul Dobre

Title: Machine Learning Model Uncertainty in GNSS Positioning

Abstract: GNSS positioning methods such as Kalman filters, factor graph optimization, and weighted least squares (WLS) have recently been complemented by machine learning (ML) models aimed at improving positioning accuracy and robustness. ML has been applied to GNSS for signal classification, anomaly detection, environmental inference, and position correction. However, in challenging conditions—such as urban canyons or unfamiliar scenarios—ML models face epistemic (model) and aleatoric(data) uncertainty, which can result in overconfident yet incorrect predictions that compromise system integrity. This work proposes an uncertainty-aware ML framework to enhance GNSS positioning error estimation by quantifying and incorporating both epistemic and aleatoric uncertainty into the model output. A spatial transformer is used to predict GNSS positioning error based on satellite-specific observation features. The model output includes both a positioning error estimate and its associated uncertainty, which enables more reliable integration with traditional GNSS solutions. Uncertainty is quantified through a model ensemble approach that aggregates predictions from multiple models to estimate uncertainty. The benefits of incorporating uncertainty include better anomaly handling, increased model interpretability, improved retraining strategies via active learning, and the ability to fall back to traditional methods in high-uncertainty situations. The preliminary experimental results demonstrate that incorporating uncertainty improves positioning interpretability and robustness compared to a standard ML enhanced GNSS pipeline through effectively identifying out-of-distribution scenarios and high input noise and guiding system fallback decisions

Bio: Paul is currently a M.Sc. student at Intelligent Navigation and Mapping Lab at University of Calgary. His main research interests are machine learning in GNSS and autonomous driving

Speaker 2: Shichuang Nie

Title: Mamba Based GNSS Cycle Slip Detection for the Single Frequency Receiver

Abstract: Cycle slips remain a dominant error source in carrier-phase Global Navigation Satellite System (GNSS) positioning and can severely impair accuracy if they are not detected and repaired. Although robust algorithms exist for dual-frequency receivers, low-cost single-frequency units still rely primarily on Doppler-aided techniques, whose performance declines at low sampling rates (e.g., 1 Hz) and whose quality-control mechanisms are rudimentary. This study investigates whether Mamba—a recent sequence-modeling architecture that offers transformer-level contextual capacity with linear complexity—can ameliorate these limitations. We train Mamba to (i) assess the validity of Doppler-aided cycle-slip detections and (ii) estimate potential repair integer. Tests on real GPS L1 data show that the model markedly improves the reliability assessment of Doppler-derived decisions; however, its slip-correction predictions are limited, owing to the weak statistical connection between the input features and the integer nature of cycle slips. These findings highlight both the promise of modern deep-learning models for quality monitoring in low-cost GNSS and the need for richer feature representations to achieve complete cycle-slip correction.

Bio: Shichuang is currently a Ph.D. student at Intelligent Navigation and Mapping Lab at University of Calgary. He obtained his bachelor’s degree at Wuhan University in 2023. His main research interests are deep learning in GNSS, multi-sensor integration and autonomous driving.

Location: Room 207 – Engineering Block G (ENG), University of Calgary Campus

Date: Thursday October 23, 2025

Time: Doors will open at 11:45am, presentation beginning shortly after noon
Cost: $20 non-members, $18 members, $15 grad students, undergrad students $10, includes a light lunch and refreshments. All proceeds go towards two annual scholarships for students attending the University of Calgary

ION Alberta Virtual Meeting – Thursday May 29 2025

Title: GNSS Interference Detection and Localization using ADS-B Data: An Automated Pipeline for Global Coverage

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Abstract: The Global Navigation Satellite System (GNSS) plays a critical role in aviation safety, enabling precise navigation and collision avoidance. However, its vulnerability to radio frequency interference (RFI), including jamming and spoofing, poses severe threats, particularly during critical flight phases such as approach and landing. This thesis introduces a novel and scalable approach for the rapid detection and localization of GNSS interference events using ADS-B data. By leveraging wide-area, crowd-sourced observations from aircraft and integrating a suite of published methodologies, we developed a fully automated pipeline to provide continuous, global GNSS interference monitoring.

This system improves situational awareness, enhances safety, and supports the timely identification and mitigation of interference sources. The pipeline includes two core components: global RFI awareness and local RFI onset monitoring. Global detection and localization employ changepoint detection, DBSCAN clustering, CNNs, and nonlinear least squares localization. The onset component provides near real-time alerts through a Bayesian algorithm that enables continuous online updates. Operating 24/7, the system detects hundreds of global GNSS interference events daily and visualizes them at https://rfi.stanford.edu/. It is capable of identifying events within 5 minutes of onset, achieving real-world localization accuracy within a 10 km radius at a 95% confidence level. The website provides rapid and reliable surveillance of global GNSS interference.

Speaker: Zixi Liu is a PhD candidate at Stanford University in the GPS Lab. She received her M.S. degree in Aeronautics and Astronautics from Stanford University in 2020. Her research focuses on GNSS interference detection and localization. Her work lies in the intersection of statistics, optimization, and machine learning.

Location: Virtual Zoom Meeting
Please contact a member of the executive for the link.

Date: Thursday, May 29, 2025

Time: Meeting will open at 11:30am, presentation beginning at noon

Cost: Free

ION Alberta In-person Meeting – Thursday, 7 November 2024

We will have two speakers:

Meeting Theme: Smartphone Positioning

Rhea Zambra
Title: Smartphone HD Map Updates Using Monocular-Inertial ORB-SLAM3 and Gaussian Splatting

Abstract: Gaussian splatting has emerged as a state-of-the-art 3D representation technique due to its high-fidelity and fast rendering capabilities. While it has been successfully integrated into light detection and ranging (LiDAR) and depth-enabled simultaneous localization and mapping (SLAM) algorithms, its potential for accurate outdoor 3D mapping using smartphone data remains underexplored. Pre-built high definition (HD) maps are vital for autonomous vehicles but are costly to maintain, motivating research in decentralized, smartphone-enabled HD map update systems. Existing solutions lack direct 3D-to-3D point cloud comparison, which could offer more robust updates by bypassing segmentation-based object detection. In this paper, we present a novel post-processing pipeline that generates dense, accurate, and near-scale HD maps from smartphone data, enabling updates to existing LiDAR and multi-sensor generated base maps. Our approach uses monocular-inertial ORB-SLAM3 to recover a scaled camera trajectory, which uses loop-closure and keyframe selection to alleviate drift in the localization and point cloud reconstruction. The ORB-SLAM3 keyframes are then used to initialize a 3D Gaussian Splatting render of the scene, which densifies the point cloud using the images, and is then scaled by the monocular-inertial camera trajectory. The camera and IMU data are collected using an iPhone 14 Pro Max, at an outdoor loop at the University of Calgary that spans 158 meters. Both sensors are observed using the SensorLogger application, and the camera-IMU calibration is performed through Kalibr. This system results in a successful closed-loop 3D Gaussian render, producing a point cloud with 8.70% scale error and 0.493m root mean square (RMS) value for iterative closest point (ICP) when referenced to a LiDAR-IMU base map, showing the potential of smartphones in visual-inertial HD mapping. Additionally, the registration of a parked car demonstrates the system’s capability for accurate map updates when aligned with a LiDAR-based reference map.

Biography: Rhea Joyce Zambra is a M.Sc. student in the Department of Geomatics Engineering at the University of Calgary. She is part of the Intelligent Navigation and Mapping Lab, specializing in mapping and with research interest in the use of smartphones for accurate 3D reconstruction.

Naman Agarwal
Title: Application of Adaptive Kalman Filtering on Smartphone Positioning

Abstract: An Adaptive Kalman filter (AKF) is proposed which is used to estimate smartphone Global Navigation Satellite System (GNSS) pseudorange measurement variance. The filter is applied to stationary, bicycle and vehicle-based smartphone datasets collected in urban environments. The adaptive filter is compared to three other processing strategies: (i) conventional weighted least-squares, (ii) a velocity as random-walk Kalman filter (KF) for kinematic data or position as random-walk KF for static data, and (iii) an alternative KF implementation that uses Doppler to adapt process noise, all using a standard elevation and carrier-to-noise density ratio (C/N_0) measurement variance model. The adaptively estimated measurement variance is compared to the true error variance computed using the provided ground truth files and all four methods are evaluated in the position domain. The proposed AKF showed a horizontal positional accuracy improvement of 35.4%, 10.5%, and 27.3%, and a vertical positional accuracy improvement of 13.2%, 50.5%, and 59.6% for stationary, bicycle, and vehicle-based smartphone GNSS, respectively, compared to the second-best performing filter.

Biography: Naman Agarwal is a PhD student in the Department of Geomatics Engineering at the University of Calgary. He works in the PLAN lab under the supervision of Dr. Kyle O’Keefe. His main research area is “Precise Smartphone Positioning”.


Location: Room 207 – Engineering Block G (ENG), University of Calgary Campus

Date: Thursday November 7, 2024

Time: Doors will open at 11:30am, presentation beginning at noon
Cost: $20 non-members, $18 members, $15 grad students, undergrad students $10, includes a light lunch and refreshments. All proceeds go towards two annual scholarships for students attending the University of Calgary

ION Alberta In-person Meeting – New Date: Wednesday, 31 July 2024

Title: GNSS Authentication: System-Side Contributions to Anti-Spoofing

Speaker: Cillian O’Driscoll

Abstract:
Spoofing is a growing threat to Global Navigation Satellite Systems, and one that is becoming more prevalent with the changing geopolitical landscape. The vulnerability of GNSS to spoofing arises from a number of root causes. Firstly, civil GNSS signals have no protections against malicious regeneration: any sufficiently capable adversary can re-create perfectly valid GNSS signals conformant with their (publicly available) specifications. Secondly, GNSS signals are extremely weak, coming from tens of thousands of kilometres away, and so are easily overpowered by stronger signals generated on the ground. Thirdly, spoofing has traditionally been seen as the preserve of nation state actors, since the cost and complexity of building a functioning spoofer were both seen as beyond the scope of anyone less well-resourced. Unfortunately, this last assumption is certainly no longer valid, particularly given the widespread availability of low cost hardware capable of broadcasting arbitrary signal waveforms at RF frequencies, including those used by GNSS systems.

To improve robustness against spoofing attacks requires both system and receiver side efforts. In this talk, we will discuss the introduction of authentication concepts to GNSS signals and navigation messages as a mechanism for improving resilience against spoofing attacks. We will provide an introduction to the general concepts of authentication, how these concepts apply in the GNSS context, and the implications for both receiver manufacturers and downstream navigation product consumers. Finally, we will discuss in detail the authentication features being introduced in the Galileo system, in particular Open Service Navigation Message Authentication (OSNMA) and the Commercial Authentication Service (CAS), and also the proposed Chips and Message Robust Authentication (CHIMERA) scheme under consideration for inclusion in GPS.

Bio:
Cillian O’Driscoll received his M.Eng.Sc. and Ph.D. degrees from the Department of Electrical and Electronic Engineering, University College Cork, Ireland. Following this he spent four years as senior research engineer with the Position, Location and Navigation (PLAN) group at the Department of Geomatics Engineering in the University of Calgary.

He was with the European Commission from 2011 to 2013, first as a researcher at the Joint Research Centre in Italy, and later as a policy officer with the European GNSS Programmes Directorate in Brussels.

Since 2014 he has been working as an independent consultant in GNSS signal processing, working for clients including the European Commission and the European Space Agency as well as a number of commercial companies. Since 2017 he has been heavily involved in work on the Galileo authentication features.

Location: Room 207 – Engineering Block G (ENG), University of Calgary Campus

New Date: Friday, July 26, Wednesday, July 31, 2024

Time: Doors will open at 11:30am, presentation beginning at noon
Cost: $20 non-members, $18 members, $15 students, includes a light lunch and refreshments. All proceeds go towards two annual scholarships for students attending the University of Calgary