Stevens Drone Detection Acoustic System and Experiments in Acoustics UAV Tracking

Sedunov, Alexander; Haddad, Darren; Salloum, Hady; Sutin, Alexander; Sedunov, Nikolay; Yakubovskiy, Alexander

doi:10.1109/hst47167.2019.9032916

Cited by 30 publications

(17 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…From the analysis of the frequency spectrum of the quadcopter's sound, we attempted to extract the sound, which is different from the standard signature extracted from the frequency spectral analysis, which uses the detection of harmonics and their ratios [16]- [20]. The principal idea was to attempt to detect the difference of close frequency components.…”

Section: Algorithmmentioning

confidence: 99%

Analysis of a Quadcopter’s Acoustic Signature in Different Flight Regimes

et al. 2020

View full text Add to dashboard Cite

show abstract

Section: Algorithmmentioning

confidence: 99%

Analysis of a Quadcopter’s Acoustic Signature in Different Flight Regimes

et al. 2020

View full text Add to dashboard Cite

show abstract

“…For the air-to-land category, the static nature of the acquisition hardware implies that only a certain space is monitored for UAVs and, since acoustic signals lose energy through the air quite rapidly, these methods have a relatively small sensing range [ 83 ]. A way to work around this is by using a large number of microphones spread over a larger area and connected through wireless protocols, as carried out in [ 85 ]. However, the cost of doing this is considerable and may even be impractical in some scenarios.…”

Section: Discussion: Next Steps Of Audio With Uavsmentioning

confidence: 99%

“…For example, the work of [ 84 ] employed a low-cost strategy to locate and detect broadband sources, such as noise-making UAVs. More recently, the work of [ 85 ] used a 16- and a 40-microphone array spread over several microphone nodes, estimated the direction of arrival of the UAV, and classified it via its acoustic signature.…”

Section: On-ground Auditory Perception Of Uavs (Air To Land)mentioning

confidence: 99%

A Review on Auditory Perception for Unmanned Aerial Vehicles

Martínez-Carranza

Rascón

2020

Sensors

View full text Add to dashboard Cite

Although a significant amount of work has been carried out for visual perception in the context of unmanned aerial vehicles (UAVs), not so much has been done regarding auditory perception. The latter can complement the observation of the environment that surrounds a UAV by providing additional information that can be used to detect, classify, and localize audio sources of interest. Motivated by the usefulness of auditory perception for UAVs, we present a literature review that discusses the audio techniques and microphone configurations reported in the literature. A categorization of techniques is proposed based on the role a UAV plays in the auditory perception (is it the one being perceived or is it the perceiver?), as well as a set of objectives that are more popularly aimed to be accomplished in the current literature (detection, classification, and localization). This literature review aims to provide a concise landscape of the most relevant works on auditory perception in the context of UAVs to date and provides insights into future avenues of research as a guide to those who are beginning to work in this field.

show abstract

“…Sedunov et al [37] proposed an acoustic system for detecting, tracking, and classifying UAVs according to their propeller noise patterns. Their system included three sensor array nodes: each node comprised 15 microphones with 100 ± 20 m intervals.…”

Section: Related Workmentioning

confidence: 99%

Prototype Development of Cross-Shaped Microphone Array System for Drone Localization Based on Delay-and-Sum Beamforming in GNSS-Denied Areas

Madokoro

Yamamoto

Watanabe

et al. 2021

Drones

View full text Add to dashboard Cite

Drones equipped with a global navigation satellite system (GNSS) receiver for absolute localization provide high-precision autonomous flight and hovering. However, the GNSS signal reception sensitivity is considerably lower in areas such as those between high-rise buildings, under bridges, and in tunnels. This paper presents a drone localization method based on acoustic information using a microphone array in GNSS-denied areas. Our originally developed microphone array system comprised 32 microphones installed in a cross-shaped configuration. Using drones of two different sizes and weights, we obtained an original acoustic outdoor benchmark dataset at 24 points. The experimentally obtained results revealed that the localization error values were lower for 0∘ and ±45∘ than for ±90∘. Moreover, we demonstrated the relative accuracy for acceptable ranges of tolerance for the obtained localization error values.

show abstract

Stevens Drone Detection Acoustic System and Experiments in Acoustics UAV Tracking

Cited by 30 publications

References 6 publications

Analysis of a Quadcopter’s Acoustic Signature in Different Flight Regimes

Analysis of a Quadcopter’s Acoustic Signature in Different Flight Regimes

A Review on Auditory Perception for Unmanned Aerial Vehicles

Prototype Development of Cross-Shaped Microphone Array System for Drone Localization Based on Delay-and-Sum Beamforming in GNSS-Denied Areas

Contact Info

Product

Resources

About