ISSN: 2168-9792
+44-77-2385-9429
Ahmed Radi, Sameh Nassar, Naser El Sheimy and A Sesay
University of Calgary, Canada
Scientific Tracks Abstracts: J Aeronaut Aerospace Eng
The integration of Global Navigation Satellite System (GNSS) with Inertial Navigation Systems (INS) has become a standard
approach, for the last two decades, for accurate navigation and, hence, has been implemented widely in various applications.
Such a complementary integrated architecture has been used for providing navigational information in many different fields
such as mapping/surveying applications, autonomous driving, unmanned ground vehicles (UGVs), and unmanned aerial
vehicles (UAVs), where accurate position and orientation information is required. In order to achieve high positioning accuracy,
a precise analysis of both GNSS positioning solutions and inertial sensors error, and their quantitative models, is highly needed.
This paper investigates the Generalized Method of Wavelet Moments (GMWM) method for stochastic modelling of 1) lowcost
GNSS receiver signal and 2) low-cost MEMS-based inertial sensors. Different datasets (including GNSS and raw inertial
data) were collected using an all-in-one sensor system, namely MTi-G-710, where GNSS data was obtained and then processed
in single point positioning (SPP) mode where position errors are expressed in the local-level frame (LLF) of reference. GMWM
were used in identifying and characterizing the different latent stochastic process and their related coefficients for both GNSS
position residual signals and inertial sensors ones as well where precise stochastic models have been built. The test results
showed that for low-cost GNSS receivers, a white noise process alone is not sufficient for accurate position residual signals��?
modeling. The results also stressed out that the GNSS error signal models are complicated where the corresponding error
model structures were represented as a sum of random walk and more than one 1st order Gauss-Markov (GM) processes, as
an indication of correlated noise existence between consecutive observations. Moreover, results emphasize that a 1st order GM
process, which is usually considered, is not always well fitted with the behaviour of low-cost inertial sensor errors where a more
complicated model structure need to be considered to improve the overall navigation results. In addition, the results showed
that the GMWM is an efficient framework for estimating the parameters of composite stochastic processes with the advantage
of correlated noise identification and characterization.
Recent Publications
1. A Radi, S Nassar and N El Sheimy (2018) Stochastic error modeling of smartphone inertial sensors for navigation in
varying dynamic conditions. Gyroscopy and Navigation. 9(1):76-95.
Ahmed Radi, PhD candidate in the Mobile Multi-Sensor Systems Research Group in the Department of Geomatics Engineering, University of Calgary (UofC), Canada. He got his B.Sc. (2007) and M.Sc. (2014) degrees in Electrical Engineering at Military Technical Collage, Egypt. Prior to joining the UofC, Ahmed held the position of senior researcher at the Technical Researches Center, Egypt. He published 8 papers in academic journals and conference proceedings. Ahmed received Faculty of Graduate Studies scholarship award, UofC, 2018. His research area is related to the non-linear error modeling of low-cost MEMS inertial sensors, multisensors integration, IMU calibration, wavelet analysis, estimation techniques, Kalman filter, adaptive integrated navigation algorithms.