Multi-sensing Data Fusion: Target tracking via particle filtering


Alessandro Contro

In this thesis Multisensing Data Fusion is firstly introduced, with a focus on perception and the concepts that are the base of this work, like the mathematical tools that make it possible. Particle filters are one class of these tools that allow a computer to perform fusion of numerical information that is perceived from real environment by sensors. For this reason they are described and state of the art mathematical formulas and algorithms for particle filtering are also presented. At the core of this project, a simple piece of software has been developed in order to test these tools in practice. More specifically, a Target Tracking Simulator software is presented where a virtual trackable object can freely move in a 2-dimensional simulated environment and distributed sensor agents, dispersed in the same environment, should be able to perceive the object through a state-dependent measurement affected by additive Gaussian noise. Each sensor employs particle filtering along with communication with other neighboring sensors in order to update the perceived state of the object and track it as it moves in the environment. 

Thesis

Multi-sensing Data Fusion: Target tracking via particle filtering

— author

Alessandro Contro

Supervision

— supervisor

Andrea Omicini

— co-supervisor

Giovanni Ciatto

Sort

— cycle

second-cycle thesis

— status

completed thesis

— language

wgb.gif

Dates

— available since

01/04/2018

— activity started

20/04/2018

— degree date

18/10/2018

IDs & URLs

— AMS Laurea

16835

Files

PDF

Partita IVA: 01131710376 - Copyright © 2008-2022 APICe@DISI Research Group - PRIVACY