Detection-Recognition of Unmanned Aerial Vehicles using the Composed Auto-Regression Model of their Acoustic Radiation

Authors

  • V. A. Tikhonov Kharkiv National University of Radio Electronics, Ukraine
  • V. M. Kartashov Kharkiv National University of Radio Electronics, Ukraine
  • V. M. Oleinikov Kharkiv National University of Radio Electronics, Ukraine
  • V. I. Leonidov Kharkiv National University of Radio Electronics, Ukraine
  • L. P. Timoshenko Kharkiv National University of Radio Electronics, Ukraine
  • I. S. Seleznev Kharkiv National University of Radio Electronics, Ukraine
  • М. V. Rybnikov Kharkiv National University of Radio Electronics, Ukraine

DOI:

https://doi.org/10.20535/RADAP.2020.81.38-46

Keywords:

unmanned aerial vehicle, acoustic signal, autoregressive model, power spectral density, detection, recognition

Abstract

When solving the actual task of detecting by its own acoustic emission (AE) unmanned aerial vehicles (UAVs), making a potential threat to various areas of human activity, it becomes necessary to distinguish its signal from all other acoustic noises. The application of the autoregression model, widely used in practice, is complicated by the need to use significantly high orders of the model, since the distinguishing features of the UAV acoustic signal that differ it from other signals are located in the low-frequency region of the spectrum. The article proposes the use of a composite autoregression model that adequately describes the correlation properties of a signal at significant time intervals and provides an increase in spectral resolution in the low-frequency region. Experimental studies carried out on using the proposed mathematical model show significant differences in the spectral power density (SPD) of UAVs AE from SPD of various sources’ noise, which improve the quality characteristics of the UAV detection-recognition problem. A simplified procedure is proposed for determining frequencies of SPD peaks of a long-term autoregression model without spectrum calculation, which is advisable to use when working in real time.

Author Biographies

V. A. Tikhonov, Kharkiv National University of Radio Electronics

Професор кафедри медіаінженерії та інформаційних радіоелектронних систем, доктор фізико-математичних наук

V. M. Kartashov, Kharkiv National University of Radio Electronics

Завідувач кафедри медіаінженерії та інформаційних радіоелектронних систем, доктор технічних наук, професор

V. M. Oleinikov, Kharkiv National University of Radio Electronics

Професор кафедри медіаінженерії та інформаційних радіоелектронних систем , кандидат технічних наук, доцент

V. I. Leonidov, Kharkiv National University of Radio Electronics

Провідний науковий співробітник кафедри біомедичної інженерії, кандидат технічних наук

L. P. Timoshenko, Kharkiv National University of Radio Electronics

Професор кафедри медіаінженерії та інформаційних радіоелектронних систем , кандидат технічних наук, доцент

I. S. Seleznev, Kharkiv National University of Radio Electronics

Інженер та аспірант кафедри медіаінженерії та інформаційних радіоелектронних систем

М. V. Rybnikov, Kharkiv National University of Radio Electronics

студент кафедри медіаінженерії та інформаційних радіоелектронних систем

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Published

2020-06-30

Issue

81

Section

Theory and Practice of Radio Measurements

License

Copyright (c) 2020 V. A. Tikhonov, V. M. Kartashov, V. M. Oleinikov, V. I. Leonidov, L. P. Timoshenko, I. S. Seleznev, М. V. Rybnikov

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