Robust Localization Methods
Master thesis, Proseminar, Seminar paper, Bachelor thesis
Location-based service is used in a great variety of contexts, such as search and rescue operations, navigation and tracking, and location-based mobile advertising. The position information, as a key component of location-based services, needs to be acquired from different positioning systems: the existing global satellite navigation systems and local positioning systems. The target's position can be inferred from the position-related measurements, such as time-of-arrival. Such an inference usually relies on a statistical measurement model that characterizes the statistical relation between the measurements and the position-related measure, such as distance.
The assumed statistical model may deviate from the true underlying one, due to e.g. multipath propagation and non-line-of-sight (NLOS) propagation. Even worse, knowledge about the underlying statistical measurement model may sometimes be difficult to obtain in many applications. Our aim is to develop robust localization methods without assuming that perfect knowledge about the statistical measurement model is given. Students participating in this project will learn about: fundamentals of source localization, computational methods for source localiaztion, Bayesian estimation and sampling methods, and convex optimization methods.
Expected gain of knowledge
Depending on the student’s knowledge and interest, an individual topic for a student project can be discussed.