Range based Algorithms for Precise Localization of Terrestrial Objects using a Drone


In this paper we propose two algorithms, called DIR and OMNI, for precisely localizing terrestrial objects, or more simply sensors, using a drone. DIR is based on the observation that, by using directional antennas, it is possible to precisely localize terrestrial sensors just applying a single trilateration. We extend this approach to the case of a regular omnidirectional antenna and formulate the OMNI algorithm. Both DIR and OMNI plan a static path for the drone over the deployment area, which includes a set of waypoints where distance measurements between the drone and the sensors are taken. Differently from previously proposed best-effort approaches, our algorithms prove that a guaranteed precision can be achieved by considering a set of waypoints, for each sensor, that are at a distance above a certain threshold and that surround the sensor with a certain layout. We perform extensive simulations to validate the performance of our algorithms. Results show that both approaches provide a comparable localization precision, but DIR exhibits a shorter path compared to OMNI, being able to exploit the directional antennas.


Computer Science

Research Center/Lab(s)

Intelligent Systems Center

Second Research Center/Lab

Center for High Performance Computing Research


The work has been partially supported by GEO-SAFE (H2020-691161), "RISE" Fondazione CR-PG (code 2016.0104.021), "Project Static Path Planning for Drones to Secure Localization" granted by Fondo Ricerca di Base 2015 University of Perugia , the NATO Science for Peace and Security grant G4936, NSF grant CNS-1545050, and "GNCS-INdAM".

Keywords and Phrases

Directive antennas; Drones; Ultra-wideband (UWB); Best effort; Directional Antenna; Extensive simulations; Localization precision; Range-based; Static paths; Terrestrial localization; Trilateration; Omnidirectional antennas; IR-UWB

International Standard Serial Number (ISSN)


Document Type

Article - Journal

Document Version


File Type





© 2018 Elsevier, All rights reserved.

Publication Date

01 Aug 2018