ION Alberta In-person Meeting and AGM – Thursday, June 26, 2025

Excited to announce our next in person meeting, AGM and Elections. See complete details below.

AGM

This will be a chance to go over finances, activities over the past year and elect a new board. The schedule for the AGM is below and the technical presentation will begin immediately afterwards.

Call to order (12:05 pm)

Chair Report

Treasurer Report

Election of Officers

Announcements

Adjournment (12:30 pm)

Notice of Election

The election will select the board for 2025 to 2027 (2 year period). The following positions along with a brief description of duties are listed below.

Chair: Responsible for presiding over meetings, and staying informed on activies and conditions within the section, and providing reports on members and activities to the national office. The current chair is Dr. Paul Alves (Hexagon)

Vice Chair: Responsible for finding presenters and organizing meetings in addition to supporting the Chair as needed. The current Vice-Chair is Dr. Yang Gao (Univeristy of Calgary)

Secretary: Responsible for keeping an up-to-date list of active members, sending out meeting invitations and communicating with the national office. The current Secretary is Landon Urquhart (u-blox).

Treasurer: Responsible for all financial aspects of the section including collecting, and expending funds as directed by the Chair. The current Treasurer is Dr. Kyle O’Keefe (University of Calgary)

If you are interested in running for any of the above positions, please reach out to one of the current exec to be put in touch with the nomination committee head: Kendrick Lao

Presentation

Precise Vehicle Maneuver Analysis using Smartphones

Author:

Yang Jiang (River) is currently a Senior GNSS Scientist with Fugro and a Research Associate at the University of Calgary. He received his Ph.D. degree in Geomatics Engineering at the University of Calgary in 2025, with research focusing on enhancing GNSS positioning performance with mass-market devices. He received the B.Eng. degree in Navigation Engineering from Wuhan University in 2018, and the M.Sc. degree in Geomatics Engineering from the University of Calgary in 2020.

Abstract:

This study proposes an innovative methodology for accurate vehicle maneuver analysis using time-differenced GNSS carrier-phase (TDCP) measurements from smartphones, combined with a vehicle motion estimation approach based on factor-graph optimization (FGO). By leveraging the centimeter-level precision of TDCP, the method achieves accurate vehicle displacement measurements, while the FGO framework enhances the estimation of vehicle speed, acceleration, pitch and heading angles, as well as angular velocity. Extensive experiments involving multiple vehicles and drivers validate the method’s effectiveness in vehicle motion estimation, maneuver identification, and risk classification. The results demonstrate a reduction in speed and angular velocity estimation errors by up to 47.28% and 66.95%, respectively, compared to traditional GNSS solutions. Furthermore, classification accuracy for high-risk driving behaviors improves by 27.37%, making the method suitable for applications such as usage-based insurance (UBI), fleet management, and autonomous driving.

Location: 

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

Date: Thursday, June 26, 2025

Time: Meeting will open at 11:30am, presentation to begin  at 12:30

Cost: $20 non-members, $18 members, $15 graduate students, $10 undergraduate students, 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, 28 November 2024

Topic: Alternatives to Kalman Filters

Speaker 1: Christian Phillips

Title: A Deep Learning Approach for the Classification of Multipath Ranging Errors in Challenging Urban Environments

Abstract: Distortion to the correlation function caused by multipath and non-line-of-sight signals can result in pseudorange errors on the order of several tens of meters in urban canyon environments. To address this problem, a deep learning approach for classifying multipath ranging error from a global navigation satellite systems (GNSS) receiver correlation function is presented. This approach uses a one-dimensional convolutional neural network, suitable for embedded applications, to classify the magnitude of pseudorange error associated with correlation functions. The network is trained and tested on live GNSS data collected in a challenging urban environment, and the capability of the model to remove high error measurements for a least-squares positioning solution is explored. The network has proven to be effective at detecting measurements with high multipath ranging error, and the removal of detected measurements reduced positioning error by up to 80%.

Bio: Christian Phillips is a graduate student in the Department of Geomatics Engineering at the University of Calgary. His research focuses on leveraging artificial intelligence to improve the performance of GNSS receivers in challenging operational environments. He is also a Software Developer at Hexagon’s Autonomy and Positioning division. He received his B.Sc. degree from the University of Manitoba in 2022.

Speaker 2: Ilyar Asl Sabbaghian Hokmabadi

Title: Computationally Efficient Particle Filtering for Fusing Angle of Arrival Beacons and IMU Measurements in Indoor Localization Applications

Abstract: In the recent past, beacons have emerged as a promising technology that meets the accuracy and reliability requirements of indoor localization. Due to the challenges regarding the loss of line-of-sight, indoor beacons often cannot provide a consistent performance throughout the navigation in standalone mode. Thus, the fusion of beacons with other sensors, such as inertial measurement units (IMU), has become an important topic for researchers. In recent decades, many estimation techniques have been proposed to achieve such sensor fusion. Among these, the Kalman filter family of estimators are ubiquitous due to their low computational cost. However, these classic estimators require an assumption of Gaussian distribution for the state variables (e.g., position, velocity, and attitude). Unfortunately, this simplistic assumption is not met in real-life scenarios. This research proposes an alternative approach based on particle filtering to fuse angle of arrival (AoA) beacon observations and inertial measurements. First, the theoretical background for reducing the dimensionality of state variables using AoA beacons is shown. This dimensionality reduction will contribute to reducing the computational cost of the particle filter. Second, it is shown that a low-cost and cm-level positioning can be achieved using only two beacons with the help of the proposed particle filter.

Bio: Ilyar Asl Sabbaghian Hokmabadi received his M.Sc. in Geomatics Engineering from the University of Calgary in 2018. Later, he received his Ph.D. degree from the University of Calgary in 2023. During his Ph.D., he developed many localization solutions using mobile and handheld systems. He has published and contributed to different areas, including indoor mapping using ultrasonic sensors, accurate 3D reconstruction using monocular cameras, and multisensory positioning solutions in indoor environments. Currently, Ilyar is an algorithm designer at Profound Positioning Inc., where his responsibilities include exploring state-of-the-art deep learning and advanced optimization methods to achieve a system-wide calibration of different types of sensors.

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

Date: Thursday November 28, 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 – 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