Bayesian estimation of GNSS positioning through particle filters
Particle Filters (PF) are Sequential Monte Carlo (SMC) methods aim at solving for filtering problems in signal processing applications as well as in Bayesian statistical inference. The use of particle filter was limited in the past due to the high computational complexity of the proposed algorithms but it has become newly appealing nowadays in a number of applications. Satellite navigation can benefits from PFbased solutions thanks to the high accuracy reachable by means of such a technique. PF are also implemented in the solution of Simultaneous Localization and Mapping (SLAM) problem, thus turning in a modern solution for complex localization and navigation applications.
GNSS-SLAM – Simultaneous Localization and Mapping applied to networked GNSS receivers
Simultaneous localization and mapping (SLAM) can be exploited in many application fields such as robotics, navigation, intelligent transport systems, planetary exploration and many others. A network of GNSS receivers could aim at cooperatively solving unknown positions through SLAM and shareable ranging measurements or feeding collaborative navigation algorithm for improved positioning and navigation performance. Popular approximate solution methods include the particle filter, extended Kalman filter, Covariance intersection, and GraphSLAM. The thesis will concern the development of a MATLAB SLAM library for the processing of satellite-based and inter-users ranging measurements for improved navigation.