Acoustic Detection and Tracking of a Class I UAS with a Small Tetrahedral Microphone Array

2014 ◽  
Author(s):  
Minas Benyamin ◽  
Geoffrey H. Goldman
Keyword(s):  
Microphone Array ◽  
Class I ◽  
Acoustic Detection ◽  
Detection And Tracking

Seismo-acoustic detection and tracking of small aircrafts

2018 ◽  
Vol 144 (3) ◽  
pp. 1855-1855
Author(s):  
Joydeep Bhattacharyya ◽  
Christian G. Reiff ◽  
Leng Sim ◽  
W. C. Kirkpatrick Alberts
Keyword(s):  
Acoustic Detection ◽  
Detection And Tracking

Acoustic detection and tracking of UAS targets

2016 ◽  
Vol 139 (4) ◽  
pp. 2020-2020
Author(s):  
Christian G. Reiff
Keyword(s):  
Acoustic Detection ◽  
Detection And Tracking

scholarly journals Development of an Acoustic System for UAV Detection

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4870 ◽  
Author(s):  
Cătălin Dumitrescu ◽  
Marius Minea ◽  
Ilona Mădălina Costea ◽  
Ionut Cosmin Chiva ◽  
Augustin Semenescu
Keyword(s):  
Neural Networks ◽  
Adaptive Filters ◽  
Microphone Array ◽  
Acoustic System ◽  
Detection And Tracking ◽  
Acoustic Signal Analysis ◽  
Remotely Piloted Aircraft ◽  
Mems Microphones ◽  
Cohen Class ◽  
Functional Components

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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