Adaptive noise cancellation using LabVIEW

Dumitrescu, Cătălin; Moise, Ilona Madalina; Soare, B.; Dumitru, N.

doi:10.1109/siitme.2013.6743660

Cited by 1 publication

(1 citation statement)

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“…Lin Shi et al deal with this challenge and present an approach to recognize drones via sounds emitted by their propellers. In their paper, the authors declare that experimental results validate the feasibility and effectiveness of their proposed method for UAV detection based on sound signature recognition [ 24 ]. Similar work is described in papers [ 25 ] and [ 26 ].…”

Section: Related Workmentioning

confidence: 99%

Development of an Acoustic System for UAV Detection

Dumitrescu

Minea

Costea

et al. 2020

Sensors

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The purpose of this paper is to investigate the possibility of developing and using an intelligent, flexible, and reliable acoustic system, designed to discover, locate, and transmit the position of unmanned aerial vehicles (UAVs). Such an application is very useful for monitoring sensitive areas and land territories subject to privacy. The software functional components of the proposed detection and location algorithm were developed employing acoustic signal analysis and concurrent neural networks (CoNNs). An analysis of the detection and tracking performance for remotely piloted aircraft systems (RPASs), measured with a dedicated spiral microphone array with MEMS microphones, was also performed. The detection and tracking algorithms were implemented based on spectrograms decomposition and adaptive filters. In this research, spectrograms with Cohen class decomposition, log-Mel spectrograms, harmonic-percussive source separation and raw audio waveforms of the audio sample, collected from the spiral microphone array—as an input to the Concurrent Neural Networks were used, in order to determine and classify the number of detected drones in the perimeter of interest.

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Section: Related Workmentioning

confidence: 99%