Author: Landon Urquhart

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 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