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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.

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A multimission radar (MMR) is employed on a wide range of civilian and military missions. The accuracy of the direction of arrival (DoA) estimation of MMR systems is an important issue when locating targets. In this work, a new approach to DoA estimation based on Graph Signal Processing (GSP) is applied to data from a multimission radar. A comparison of the GSP is carried out with classic DoA estimation algorithms, including Delay and Sum, Capon, and Multiple Signal Classification (MUSIC). A short aircraft trajectory is considered as a reference for estimating parameters such as DoA in azimuth, range and radial velocity, both for simulated and real-life signals. DoA in elevation is estimated for simulated data, considering the MMR architecture and target characteristics. Simulation results have shown that the proposed method achieves estimations with competitive accuracy in comparison to classical DoA estimation methods.

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