Accurate position and velocity estimation i.e. navigation is an integral part of the operation of any autonomous vehicle. Advent of Global Positioning System (GPS)\Global Navigation Satellite System (GNSS) technologies have radically changed the navigation process. Satellite Navigation and Sensor Fusion course aims to make students familiar with satellite navigation, GPS\GNSS, GPS signal structure, acquisition and position computation techniques from GPS data. This course also focuses on fusion of GNSS and other available sensor observations to estimate accurate position and velocity, which is widely used for navigation of autonomous vehicles.
The course will start with an introduction to radio navigation and GPS. After discussing GPS signal structure and positioning algorithms, various position error sources will be introduced and various error correction techniques will be explained. GPS signal acquisition and receiver structure are also within the scope of this course. Effects of interference, weak signal on GPS positioning will also be discussed. New GPS signals and other global and regional satellite navigation systems will be also introduced to the students.
Inertial Measurement Unit (IMU) will be introduced in the second part of the course. The Kalman Filter (which is almost ubiquitous in navigation applications) and its non-linear variants will be discussed. Finally usage of GNSS and IMU observations in Kalman Filter framework to estimate accurate position and velocity will be explained.
Describe basics of satellite navigation
Summerize GPS signal structure, acquistion and receiver mechanism, Explain position computation methods and various position correction techniques.
Explain mechanism and dynamics of IMU (accelerometer and gyroscope), determine position and velocity using IMU observation, discuss limitations of IMU
Implement sensor fusion algorithms to estimate position and velocity using GNSS and IMU observations
Discuss recent developments in satellite navigation