Probability and Statistics Seminar   RSS

11/10/2016, 11:00 — 12:00 — Room P3.10, Mathematics Building
, Institute for Systems and Robotics

Distributed and robust network localization

Signal processing over networks has been a broad and hot topic in the last few years. In most applications networks of agents typically rely on known node positions, even if the main goal of the network is not localization. Also, mobile agents need localization for, e.g., motion planning, or formation control, where GPS might not be an option. Also, real-world conditions imply noisy environments, and the network real-time operation calls for fast and reliable estimation of the agents’ locations. So, galvanized by the compelling applications researchers have dedicated a great amount of work to finding the nodes in networks. With the growing network sizes of devices constrained in energy expenditure and computation power, the need for simple, fast, and distributed algorithms for network localization spurred this work. Here, we approach the problem starting from minimal data collection, aggregating only range measurements and a few landmark positions. We explore tailored solutions recurring to the optimization and probability tools that can leverage performance under noisy and unstructured environments. Thus, the contributions are, mainly:
  • Distributed localization algorithms characterized for their simplicity but also strong guarantees;
  • Analyses of convergence, iteration complexity, and optimality bounds for the designed procedures;
  • Novel majorization approaches which are tailored to the specific problem structure.