Particle swarm guidance system for autonomous unmanned aerial vehicles in an air defence role

This source preferred by Keith Phalp

Authors: Banks, A., Vincent, J. and Phalp, K.T.

Journal: Journal of the Royal Institute of Navigation

Volume: 61

Pages: 9-29

ISSN: 0373-4633

DOI: 10.1017/S0373463307004444

This work investigates the utilisation of Particle Swarm Optimisation (PSO) for the non-deterministic navigation of Unmanned Aerial Vehicles (UAVs), allowing them to work cooperatively toward the goal of protecting a wide area against airborne attack. To negate the PSO's inherent weakness in dynamic environments, a neighbourhood scheme is proposed that not only enables the efficient interception of targets several times faster than the UAVs but also facilitates the maintenance of effective airspace coverage. Empirical results suggest that these techniques may indeed be of use in autonomous navigation systems for UAVs in air defence roles

This data was imported from Scopus:

Authors: Banks, A., Vincent, J. and Phalp, K.

Journal: Journal of Navigation

Volume: 61

Issue: 1

Pages: 9-29

eISSN: 1469-7785

ISSN: 0373-4633

DOI: 10.1017/S0373463307004444

This work investigates the utilisation of Particle Swarm Optimisation (PSO) for the non-deterministic navigation of Unmanned Aerial Vehicles (UAVs), allowing them to work cooperatively toward the goal of protecting a wide area against airborne attack. To negate the PSO's inherent weakness in dynamic environments, a neighbourhood scheme is proposed that not only enables the efficient interception of targets several times faster than the UAVs but also facilitates the maintenance of effective airspace coverage. Empirical results suggest that these techniques may indeed be of use in autonomous navigation systems for UAVs in air defence roles. © 2007 The Royal Institute of Navigation.

This data was imported from Web of Science (Lite):

Authors: Banks, A., Vincent, J. and Phalp, K.

Journal: JOURNAL OF NAVIGATION

Volume: 61

Issue: 1

Pages: 9-29

eISSN: 1469-7785

ISSN: 0373-4633

DOI: 10.1017/S0373463307004444

The data on this page was last updated at 04:59 on September 22, 2018.