The following three courses will be offered at UofC in the Summer and Fall terms. Two of them (ENGO 638 and ENGO 625) will be evening courses and may be of interest members of the Alberta Section.
Please see www.geomatics.ucalgary.ca/courses for more information.
ENGO 638 – GNSS Receiver Design
Tue, Wed & Thu evenings, from July 2 – August 14
This course provides a thorough knowledge and understanding of GNSS receiver design. Different aspects of GNSS including signal structures, propagations environments, essential tasks of a GNSS receiver, receiver structure and individual components are discussed in details. The course covers GNSS signal detection and parameter estimation, acquisition and tracking, signal monitoring, range and carrier phase measurements, and navigation message decoding. The course includes several computer labs and assignments to obtain an effective understanding of related topics and signal processing algorithms for GNSS applications through real GNSS data processing.
The course consists of: three regular assignments each covers worth 10% of the final mark (30% in total); two lab assignments each worth 15% where students modify provided Matlab code. A final exam makes up the last 30%.
ENGO 625 – Advanced GNSS Theory and Applications
Tue & Thu evenings, from September 9 – December 6
Overview of space positioning and navigation systems; concepts and general description. GNSS signal description. Receiver and antenna characteristics and capabilities; signal measurements indoor; GNSS error sources and biases; atmospheric delays, signal reflection and countermeasures. Mathematical models for static point and relative positioning. Kinematic single point and differential post mission and real time positioning, navigation and location. Augmentation methods. Land, marine, airborne and indoor applications. Case studies.
Course will consist of two labs, a research seminar and a two hour final exam.
ENGO 620 – Estimation for Navigation
Tue & Thu afternoons (2pm – 4pm), from September 9 – December 6
Overview of estimation fundamentals including stochastic processes; covariance matrices; auto-correlation functions; power spectral densities, and; error propagation. Review of least-squares estimation; summation of normals and sequential least-squares formulations, and; role of measurement geometry in least-squares position estimation. Constraints and implementations. Concept of Kalman filtering; relationship between Kalman filtering and least-squares; linear, linearized and extended Kalman filter formulations; system model formulation; process noise model determination; measurement models, and; effect of time-correlated measurements and possible remedies. Numerical stability issues in estimation and possible solutions. Statistical reliability in least-squares and Kalman filtering and related RAIM concepts. Introduction to other estimation techniques including unscented Kalman filters and particle filters. Application of above topics to relevant GNSS, GNSS/INS, GNSS receivers and other navigation estimation problems.
Course will consist of: two assignments worth a total of 65%; a comprehensive exam worth 35%, and; class participation worth 5%. Assignments are very hands-on and require programming in Matlab or C/C++.