Use of Acoustic Signature for Detection, Recognition and Direction Finding of Small Unmanned Aerial Vehicles

被引:2
|
作者
Kartashov, Vladimir [1 ]
Oleynikov, Vladimir [1 ]
Koryttsev, Igor [1 ]
Sheiko, Sergiy [1 ]
Zubkov, Oleh [1 ]
Babkin, Stanislav [1 ]
Selieznov, Ivan [1 ]
机构
[1] Kharkiv Natl Univ Radio Elect, Dept Media Engn & Informat Radio Elect Syst, Kharkiv, Ukraine
关键词
acoustic emission; DJI Phantom; Mel-Frequency cepstral coefficients method; quadcopter; UAV; AIRCRAFT;
D O I
10.1109/TCSET49122.2020.235458
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Unmanned aerial vehicles can fly in an autonomic flight mode without any active radio control or passive radio navigation and they are almost invisible for radars. Only engines and propellers acoustic emission (AE) may give lots of information for their detection, recognition and location. This paper discusses the experimental results of testing the classical detection and direction finding methods by Bartlett, Capeon and cross-correlation function method with the use of microphone array. Experiments include physical investigations of the UAV acoustic emission presented with two signal models - harmonic signal and broadband signal and for two conditions, namely, indoor and in the open area. Detection and recognition the UAV on a noisy background by spectral signs give the same quality as the Mel-Frequency cepstral coefficients method, but don't need to make acoustic portraits. The cross-correlation function method gives the best results in direction finding of the UAV.
引用
收藏
页码:377 / 380
页数:4
相关论文
共 50 条
  • [31] Analysis and optimization of path finding algorithm for unmanned aerial vehicles
    Vijaya J.
    Thangaraj M.
    International Journal of Information Technology, 2024, 16 (6) : 3973 - 3981
  • [32] Detection of Dugongs from Unmanned Aerial Vehicles
    Maire, Frederic
    Mejias, Luis
    Hodgson, Amanda
    Duclos, Gwenael
    2013 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS), 2013, : 2750 - 2756
  • [33] Detection Based Tracking of Unmanned Aerial Vehicles
    Uzun, Bedirhan
    Eker, Onur
    Saribas, Hasan
    Cevikalp, Hakan
    2019 27TH SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE (SIU), 2019,
  • [34] Multispectral Detection of Commercial Unmanned Aerial Vehicles
    Farlik, Jan
    Kratky, Miroslav
    Casar, Josef
    Stary, Vadim
    SENSORS, 2019, 19 (07)
  • [35] Unsupervised anomaly detection in unmanned aerial vehicles
    Khan, Samir
    Liew, Chun Fui
    Yairi, Takehisa
    McWilliam, Richard
    APPLIED SOFT COMPUTING, 2019, 83
  • [36] The use of Unmanned Aerial Vehicles (UAVs) to sample the blow microbiome of small cetaceans
    Centelleghe, Cinzia
    Carraro, Lisa
    Gonzalvo, Joan
    Rosso, Massimiliano
    Esposti, Erika
    Gili, Claudia
    Bonato, Marco
    Pedrotti, Davide
    Cardazzo, Barbara
    Povinelli, Michele
    Mazzariol, Sandro
    PLOS ONE, 2020, 15 (07):
  • [37] Attitude Detection and Localization for Unmanned Aerial Vehicles
    Jean, Jong-Hann
    Liu, Bo-Syun
    Chang, Po-Zong
    Kuo, Li-Chuan
    SMART SCIENCE, 2016, 4 (04) : 196 - 202
  • [38] Detection of Landing Areas for Unmanned Aerial Vehicles
    Mukadam, Kausar
    Sinh, Aishwarya
    Karani, Ruhina
    2016 INTERNATIONAL CONFERENCE ON COMPUTING COMMUNICATION CONTROL AND AUTOMATION (ICCUBEA), 2016,
  • [39] KITE: Automatic speech recognition for unmanned aerial vehicles
    Onea, Dan
    Cucu, Horia
    INTERSPEECH 2019, 2019, : 2998 - 3002
  • [40] Implementation of Audio Recognition System for Unmanned Aerial Vehicles
    Solis, Edgar R.
    Shashev, Dmitriy, V
    Shidlovskiy, Stanislav, V
    INTERNATIONAL SIBERIAN CONFERENCE ON CONTROL AND COMMUNICATIONS (SIBCON 2021 ), 2021,