After an almost 3-year hiatus, the ION Alberta Section will be meeting in person on Thursday October 27 and Room EN B112 at the University of Calgary. We are going to attempt to run a hybrid meeting, with attendees in person and on Zoom. If you would like to attend by Zoom, please contact one of the section officers for the Zoom link.
In person ION Alberta meetings include a light lunch. Admission ($20 non-members, $18 members, $15 students) is collected at the door and is used to fund two scholarships that the Alberta Section donates annually to the University of Calgary. For the last three years, we have not had any revenue from lunch meetings, so we hope to organize more regular in-person presentations moving forward.
For our first in-person meeting, three University of Calgary graduate students are going to present summaries of their papers from ION GNSS+ that was held in Denver in September. The three speakers and their abstracts are:
Improving Smartphone PPP and RTK Performance Using Time-Differenced Carrier Phase Observations
Speaker: Farzaneh Zangenehnejad is a Ph.D. student at University of Calgary, Canada. She is working on the smartphone positioning. Her
fields of interests are precise point positioning and smartphone positioning.
Abstract: The availability of GNSS carrier-phase measurements from smartphones and tablets has made accurate positioning possible with smartphones. However, it requires the development of advanced positioning algorithms to process the GNSS measurements from smartphones. In this research, we propose the use of time-differenced carrier-phase (TDCP) observations, instead of raw Doppler observations to improve precise point positioning (PPP) and real-time kinematic (RTK) performance with smartphones. Although the GNSS Doppler observations can contribute to the velocity estimation of a moving object and subsequently the positioning solutions, the current Doppler observations from the Android smartphones are found biased with respect to the carrier-phase observations. This would affect the positioning performance in PPP and RTK. Our research results demonstrate that a positioning algorithm introducing velocity vector estimates from time-differenced carrier-phase observations as weighted constraints along with the GNSS pseudorange and carrier-phase observations can improve the positioning performance of both PPP and RTK with smartphones. Implemented into a constrained Kalman Filter for both PPP and RTK methods, the positioning models along with mathematical equations and their positioning performance have be assessed using the training datasets from the “Google Smartphone Decimeter Challenge, 2021 and 2022”. An improvement on the RMS of horizontal positioning is achieved when employing the TDCP observations in both kinematic tests. A significant improvement in 50th percentile error, the maximum absolute of the horizontal error and the positioning performance in the beginning epochs is also confirmed using the proposed method.
SIR Particle Filter in Float Solution with Map-Aiding Algorithm
Speaker: Rene Manzano is a Ph.D. student in the Department of Geomatics Engineering at the University of Calgary. His research is about the performance assessment of non-linear sampling estimation methods such as the Particle Filter carrier phase GNSS applications.
Abstract: A map-aiding algorithm is added to the estimation of the full geometry-based float solution for differential carrier phase positioning of a land vehicle in order to assess the effect of map matching on the convergence of the position and ambiguity states. As this constraint leads to highly non-Gaussian posterior densities, it is implemented within the framework of Bayesian theory, using a Sequential Importance Resampling (SIR) Particle Filter (PF). This PF estimates the user position, velocity, and acceleration states, as well as the float ambiguities using L1 GPS carrier phase and pseudorange observations. The position accuracy of the Particle Filter solution with and without the map-aiding constraint is compared to the typically used Extended Kalman Filter (EKF). The proposed algorithm is tested in four different segments of a larger land vehicle data set, showing how the position convergence improves when adding digital road map information within the first thirty seconds of initializing the PF in different scenarios that include driving in a straight line, turning, and changing lanes.
Gyroscope Drift Estimation of a GPS/MEMS-INS Smartphone Sensor Integration Navigation System for Kayaking
Speaker: Kelly Harke is a MSc student in the Department of Geomatics Engineering at the University of Calgary. Her research is about investigating kayak-specific motion constraints for an Android smartphone GPS/INS navigation solution.
Abstract: In 2016, Google released an application programming interface on Android smartphones which allowed users to access the raw Global Positioning System (GPS) measurements. This recent accessibility of raw GPS measurements coupled with the rising demand for handheld navigation and tracking for consumer applications has created an opportunity to test the capabilities of a smartphone tracking system for river kayaking. However, GPS-only navigation can be intermittent or unreliable due to satellite signal outages in environments such as river canyons, therefore an inertial navigation system (INS) can be integrated to provide a continuous solution during these outages. In this paper, a system to continuously track a kayak through a river using a low-cost, smartphone GPS/INS based positioning system was developed. Low-cost inertial sensors, such as those found in a smartphone, are incapable of providing an accurate solution for a long period of time during GPS outages due to the accumulation of sensor errors. To overcome this, kayak-specific motion constraints are proposed that estimate the gyroscope bias, pitch and roll angles, and accelerometer bias with the assumption that the kayak’s pitch and roll angles are centered around zero, cyclic in nature, and return to the same orientation at regular time intervals. To evaluate this proposed solution, multiple field datasets were collected using a Google Pixel 4 that was fixed to the deck of a kayak. The experimental results indicate an improvement in the navigation performance verifying that the proposed kayak-specific motion constraints increased the accuracy of the solution during GPS outages.