Optimal Sensor Placement for UAV Localization in Critical Areas


Enrico Tronci, Marco Esposito, Toni Mancini

Presentation title

Optimal Sensor Placement for UAV Localization in Critical Areas

Authors

Enrico Tronci, Marco Esposito, Toni Mancini

Institution(s)

Sapienza University of Rome

Presentation type

Presentation of a research group from one or more scientific institutions

Abstract

Unmanned Aerial Vehicles (UAVs) present many threats for critical areas such as airports, power plants and military facilities. A small commercial drone may, for instance, carry explosive payloads, disturb radio communications or cause collision damage.

Finding a deployment of sensors that maximises a given set of performance metrics is a difficult task: realistic environments often present many obstacles (buildings, mountains, etc.) distributed over a large area, and the number of possible deployments is extremely high.

We show how state exploration methods (namely, model checking) typically used for simulation based verification of Cyber Physical Systems can be leveraged to successfully address such a problem.

We present a new approach to the modelling of the problem that exploits convex geometry to allow efficient evaluation of the quality of sensors deployments.

More specifically, we use convex geometry to allow efficient evaluation of the quality of sensors deployments and AI based Local Search and Trust Region methods, to optimise the deployment of a given set of sensors with respect to a set of user-defined performance metrics.

The feasibility and the efficacy of the approach are illustrated in two case studies of real-world critical areas, namely the Leonardo da Vinci International Airport of Fiumicino and the Vienna International Centre.

In our extensive experimental campaign, we optimised the coverage and the overall cost of the deployment of direction-finding antennas.

Our experimental results results show that our algorithm scales very well over realistic scenarios and a large number of sensors.