Development of an Acoustic System for UAV Detection
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 an...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-08-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/17/4870 |
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| author | Cătălin Dumitrescu Marius Minea Ilona Mădălina Costea Ionut Cosmin Chiva Augustin Semenescu |
| author_facet | Cătălin Dumitrescu Marius Minea Ilona Mădălina Costea Ionut Cosmin Chiva Augustin Semenescu |
| author_sort | Cătălin Dumitrescu |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T16:44:50Z |
| format | Article |
| id | doaj.art-57a0e49097ea412cbf33361ed594e5c0 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T16:44:50Z |
| publishDate | 2020-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-57a0e49097ea412cbf33361ed594e5c02023-11-20T11:43:20ZengMDPI AGSensors1424-82202020-08-012017487010.3390/s20174870Development of an Acoustic System for UAV DetectionCătălin Dumitrescu0Marius Minea1Ilona Mădălina Costea2Ionut Cosmin Chiva3Augustin Semenescu4Department Telematics and Electronics for Transports, University “Politehnica” of Bucharest, 060042 Bucharest, RomaniaDepartment Telematics and Electronics for Transports, University “Politehnica” of Bucharest, 060042 Bucharest, RomaniaDepartment Telematics and Electronics for Transports, University “Politehnica” of Bucharest, 060042 Bucharest, RomaniaDepartment Telematics and Electronics for Transports, University “Politehnica” of Bucharest, 060042 Bucharest, RomaniaDepartment Engineering and Management for Transports, University “Politehnica” of Bucharest, 060042 Bucharest, RomaniaThe 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.https://www.mdpi.com/1424-8220/20/17/4870microphone arrayconcurrent neural networkssensorsmicrosystemsdrone detection |
| spellingShingle | Cătălin Dumitrescu Marius Minea Ilona Mădălina Costea Ionut Cosmin Chiva Augustin Semenescu Development of an Acoustic System for UAV Detection Sensors microphone array concurrent neural networks sensors microsystems drone detection |
| title | Development of an Acoustic System for UAV Detection |
| title_full | Development of an Acoustic System for UAV Detection |
| title_fullStr | Development of an Acoustic System for UAV Detection |
| title_full_unstemmed | Development of an Acoustic System for UAV Detection |
| title_short | Development of an Acoustic System for UAV Detection |
| title_sort | development of an acoustic system for uav detection |
| topic | microphone array concurrent neural networks sensors microsystems drone detection |
| url | https://www.mdpi.com/1424-8220/20/17/4870 |
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