SPS Webinar: Cooperative Joint Network Localization and Multi-Target Tracking
Date: 7 December 2023
Time: 10:00 AM ET (New York Time)
Speaker(s): Dr. Mattia Brambilla
Original Article (Free to download - open access)
Abstract
Tracking multiple unknown targets using a network of mobile sensing agents is a challenging task for automated and connected systems with sensing and navigation functionalities. The scenario includes agents localizing themselves in the network and, at the same time, performing multitarget tracking in the presence of clutter, miss detection and data association uncertainty. In this webinar, an approach that jointly addresses the self-localization of agents and the detection and tracking of multiple targets is presented. It relies on a holistic and centralized framework exploiting graph theory and a message passing algorithm to describe the statistical relationships among agent states, target states, and observations. The presented solution is general enough to accommodate a full multistatic network configuration (with multiple transmitters and receivers) and to handle data association among observations and generating objects (either agents or targets). Simulation experiments and experimental analyses demonstrate the effectiveness of this joint approach that extracts valuable information from detected targets to improve the localization of agents.
Biography
Mattia Brambilla (Member, IEEE) received the B.Sc. and M.Sc. degrees in telecommunication engineering and the Ph.D. degree (cum laude) in information technology from the Politecnico di Milano, Milan, Italy, in 2015, 2017, and 2021, respectively. He was a visiting researcher with the NATO Centre for Maritime Research and Experimentation (CMRE), La Spezia, Italy, in 2019.
He joined the Faculty of Dipartimento di Elettronica, Informazione e Bioingegneria (DEIB), Politecnico di Milano, as a Research Fellow, in 2021. His research interests include signal processing, statistical learning, and data fusion for cooperative localization and communication, with focus on vehicular and industrial networks.
Dr. Brambilla is the recipient of the Best Student Paper Award at the 2018 IEEE Statistical Signal Processing Workshop, chair of special sessions, member of technical program committee at conferences, and reviewer for several scientific publications.
