Multi-sensing Data Fusion: Target tracking via particle filtering

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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.