Three students from the University of Calgary presented their papers from the 2023 ION GNSS+ Conference held this past September.
Robust Wavelet Variance-Based Stochastic Analysis of Instantaneous Code Phase-Based Oscillator Instability
Chrysostomos Minaretzis, Yiran Luo, University of Calgary; Stéphane Guerrier, University of Geneva; Naser El-Sheimy, Michael Sideris, University of Calgary
Speaker: Chrysostomos Minaretzis received an Integrated M.Sc. degree on Rural and Surveying Engineering from the Aristotle University of Thessaloniki, in 2018. He recently got his Ph.D. on Geomatics Engineering from the University of Calgary, where he was a member of the Mobile Multi-Sensor Systems (MMSS) Research Group under the supervision of Prof. Naser El-Sheimy. His research interests include robust stochastic modeling, inertial navigation, and nonlinear estimation for multi-sensor integration with application on land vehicle navigation.
Abstract: Modeling a Global Navigation Satellite System (GNSS) receiver clock instability is crucial to advancing GNSS-based navigation systems, especially regarding low-cost devices. This work proposes a way to obtain an insight to the drift that characterizes Local Oscillators (LOs) of GNSS receivers by using a raw GNSS baseband signal, namely the instantaneous code phases and then model its random behaviour using a state-of-the-art stochastic modeling framework, the Generalized Method of Wavelet Moments (GMWM). First, a GNSS Software-Defined Radio (SDR) is used to process three types of Intermediate-Frequency (IF) GPS signals (i.e., L1 C/A, L5 data, and L5 pilot) and generate the respective code phase measurements, which are synthesized by using the LO from a GNSS frontend. Then, a pre-processor computes clock bias errors by subtracting the code phases from their references. Finally, the single-difference results of the pre-processor outputs, meaning the first order change increments of the previously computed clock bias errors, are inputted into the Robust GMWM (RGMWM) framework, which offers a stochastic modeling solution that is partially protected by the influence of outliers in the data at hand. The suggested methodology was tested by carrying out real-world experiments under open sky conditions, which validated the effectiveness of the RGMWM in stochastically modeling the LO instability using the instantaneous code phase of GNSS signals. In addition, a new type of sinusoidal noise has been identified in the input data computed from the GPS L1 C/A tracking and quantified via the RGMWM, while the L5 signals appeared to be unaffected by such noise type. This finding has the potential to contribute to developing more advanced GNSS receivers by leveraging the more reliable model in either the baseband processors or navigators in the future.
Tightly Integrated Smartphone GNSS and Visual odometry for Enhanced Urban Pedestrian Positioning
Yang Jiang, Yan Zhang, Zhitao Lyu, Shuai Guo, Yang Gao, Department of Geomatics Engineering, University of Calgary
Speaker: Yang River Jiang is a Ph.D. student in the Department of Geomatics Engineering at the University of Calgary, Canada. His current research focuses on smartphone precise positioning with GNSS, MEMS, and cameras.
Abstract: Precise smartphone-based positioning service is challenging in dense urban areas due to significant multipath effects in GNSS signals received by smartphone devices. The raw GNSS measurements will be contaminated by non-line-of-sight (NLOS) signals, severely deteriorating the smartphone positioning accuracy. Many methods have been proposed to mitigate the GNSS NLOS problem, including 3D mapping-aided GNSS, RAIM, and machine learning-based methods. But these methods have limitations such as the need for 3D city models or external devices, high false-alarm chances, and training processes. In this study, we have developed a new approach to improve smartphone positioning accuracy in dense urban areas by coupling the smartphone GNSS and camera sensors, which are already available in most smartphones. Wholly based on themselves, the proposed method tightly integrates GNSS pseudorange, carrier-phase and Doppler measurements, and a visual odometry (VO). The GNSS measurements undergo preprocessing, DD normal equations, and velocity estimations. The smartphone images are processed using a KLT optical flow method, where GNSS velocities are applied to estimate the coordinate rotation and scale between them based on a sliding-window least-squares scheme using Horn’s method. Importantly, a quad-tree-based outlier searching (QTOS) algorithm is applied to ensure the healthiness of estimation processes throughout the integration. The data from DD GNSS normal equations, GNSS velocities, and VO velocities are input to an FGO algorithm for final positioning estimations. A field test in the dense urban area of Calgary showed an improvement of 25% in horizontal accuracy and a reduction of velocity estimation error by 30%, where the chance of positioning outliers (> 30 m) is significantly reduced by 76%. Therefore, the proposed method provides an effective solution for precise smartphone positioning in dense urban areas without the need for external data sources or training.
Baseline Spoofing Detection for Aircraft with Standard Navigation Hardware
Michael Blois, University of Calgary; John Studenny, CMC Electronics; Kyle O’Keefe, Baoyu Liu, University of Calgary
Speaker: Michael Blois is a PhD student at University of Calgary. He received his Bachelor of Science degree in Geomatics Engineering at University in 2011 and his Master of Applied Science in Aerospace Engineering at Carleton University in 2019. His research interests are in GNSS spoofing in an aerospace context.
Abstract: The spoofing detection technique presented uses the known baseline separation between GNSS antennas as the truth reference and compares it to calculated antenna baseline separation. The technique is based on the fact that the observed satellite time delay (phase information) is different at each antenna whereas a single antenna spoofer will provide exactly the same phase information at each antenna but slightly time delayed. When satellite data is used, the calculated antenna separation is a close match to the known baseline separation; when spoofer data is used, the calculated antennas separation collapses to zero.
This technique is based on a known antenna separation and this known separation allows the computation of thresholds for false spoofing and missed spoofing. The consequence is that spoofing detection performance can be reliably quantified. Further, the desired spoofing performance will in-turn specify the minimum antenna baseline separation.
The antenna separation can be calculated using either the pseudorange for a code phase solution or the carrier phase for RealTime-Kinematic (RTK) solution. Any off-the-shelf receiver-antenna can be used provided that it produces the data that enables the computation of a baseline solution. For the same spoofing detection performance, RTK allows for much shorter antenna baselines than a code phase solution.
This spoofing detection technique does not require any specialized hardware, a pair off-the-shelf receiver-antenna is adequate. The experiment used a pair of NovAtel receiver-antennas with an off-the-shelf RTK software. The RTK software did not edit, screen or select “the most favorable data”, all data were used from every sample instant as it were used in-flight. The spoofer was a GNSS signal repeater; however, this technique applies equally to a highly sophisticated spoofer. A truck was used to simulate an aircraft. The baseline solution separation (no spoofing) and baseline collapse (spoofing) performance correlates with theory.