This paper presents an approach to obtain stationary shooter localization in a Cartesian plane using a moving array of microphones. A single array is embedded in an unmanned aerial vehicle (UAV), a quadcopter, to explore benefits provided by its intrinsic mobility. We propose a model which is based on geometrical acoustics and that assumes the gunshot signals as being buried in strong noise generated by propellers. Generalized cross correlation algorithms are used to estimate the Direction of Arrival (DoA) of the impulsive gunshot signals and, finally, bearings-only target motion analysis techniques are applied to estimate potential shooters localization from the noisy DoAs.
Unmanned aerial vehicles (UAV) are growing in popularity, and recent technological advances are fostering the development of new applications for these devices. This paper discusses the use of aerial drones as a platform for deploying a gunshot surveillance system based on an array of microphones. Notwithstanding the difficulties associated with the inherent additive noise from the rotating propellers, this application brings an important advantage: the possibility of estimating the shooter position solely based on the muzzle blast sound, with the support of a digital map of the terrain. This work focuses on direction-of-arrival (DoA) estimation methods applied to audio signals obtained from a microphone array aboard a flying drone. We investigate preprocessing and different DoA estimation techniques in order to obtain the setup that performs better for the application at hand. We use a combination of simulated and actual gunshot signals recorded using a microphone array mounted on a UAV. One of the key insights resulting from the field recordings is the importance of drone positioning, whereby all gunshots recorded in a region outside a cone open from the gun muzzle presented a hit rate close to 96%. Based on experimental results, we claim that reliable bearing estimates can be achieved using a microphone array mounted on a drone.
Estimating the direction of arrival (DoA) of an audio signal from an aerial platform gives way to estimating the source localization. This paper addresses the problem of airborne shooter localization using a microphone array mounted on a drone. In this scenario, the noise of the propellers poses a great level of difficulty in estimating the DoA of the gunshot signals owing to low levels of SNR. This, combined with the fact that a moving drone records multiple gunshots at different positions, have discouraged the use of drones for shooter localization. Based on real gunshot signals recorded at a shooting site, we explore the advantages and limitations of using a drone for the task of audio surveillance and gunshot detection and localization.
Since the first electronic computers hit the market in the 1950's, governments have been amongst the biggest users of Information Technology (IT) worldwide. Therefore, it is in the general public's best interests that government officials are provided with concepts, methods and tools that help them to optimise the results yielded by IT investments. This paper presents a method that identifies the best implementation order for a portfolio of IT projects that has been broken down into a large number of subprojects. The method builds on previous proposals by providing a framework that properly considers the intangible benefits that are a matter of common concern in the public sector.
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