Control Systems and Computers, N4, 2020, Article 4
https://doi.org/10.15407/csc.2020.04.035
Control Systems and Computers, 2020, Issue 4 (288), pp. 35-43.
УДК 534.6:519
Volkov Oleksandr Ye., Head of the Department, International Research and Training Center for Information Technologies and Systems NAS and MES of Ukraine, Glushkovave., 40, Kyiv, 03187, Ukraine,alexvolk@ukr.net,
Taranuha Volodimir Yu., Ph.D.(Physics and Math.), Senior Research Associate, International Research and Training Center for Information Technologies and Systems NAS and MES of Ukraine, Glushkovave., 40, Kyiv, 03187, Ukraine, dep185@irtc.org.ua,
Linder Yaroslav M., Ph.D.(Physics and Math.), Senior Research Associate, International Research and Training Center for Information Technologies and Systems NAS and MES of Ukraine, Glushkovave., 40, Kyiv, 03187, Ukraine, dep185@irtc.org.ua,
Volosheniuk Dmytro O., Research Associate, International Research and Training Center for Information Technologies and Systems NAS and MES of Ukraine, Glushkovave., 40, Kyiv, 03187, Ukraine,dep185@irtc.org.ua,
Sieriebriakov Artem K., Ph.D. candidate, Leading Engineer, International Research and Training Center for Information Technologies and Systems NAS and MES of Ukraine, Glushkovave., 40, Kyiv, 03187, Ukraine,sier.artem1002@outlook.com,
TECHNOLOGY OF ACOUSTIC MONITORING, DETECTION AND LOCALIZATION OF OBJECTS IN THE CONTROLLED AREA
Introduction. The problem of sound source localization is based on the solution of two subtasks: determination of the direction onto the sound source and estimation of the distance to it. In practice, the precise distance to the sound source is often unknown, which motivates its estimation with the help of other input parameters, such as the incoming sound intensity and its time of arrival.
The solution of such a problem according to the mentioned criteria becomes dominant, when alternative methods of object localization are limited or too complex, for example, in the case of restricted visibility.
Tasks concerned with the audio signal recording and estimation are usually solved with the help of the group of sensors, or microphones, connected together, in a quantity from 1 to 6.
Such an approach allows us to apply the algorithm of sound localization for monitoring movable or immovable objects with possible restricted visibility.
Purpose. This article suggests a method of sound source distance estimation based on impulse signal recording and its further processing with the help of amplitude and frequency algorithms. Each algorithm allows us to obtain accurate enough estimation of the distance, even in the case sound signal is distorted with the noise or echoes.
Methods. Distance determination was conducted experimentally with the help of incoming sound intensity estimation and estimation of its frequency changes over distance. The audio signal was recorded with the help of a group of 4 interconnected microphones.
Results. An algorithm for determining the distance to the sound source based on amplitude and frequency characteristics was developed. The algorithm is implemented in the Matlab software environment.
Conclusion. The algorithm of sound source distance estimation involves performing digital low-pass filtering of the recorded signal. Due to this filtering, this approach allows us to calculate the distance to the sound source with a relatively small error.
The frequency component of the algorithm was developed because for certain types of sound sources their spatial orientation is critical, which affects the intensity of the observed sound signal, which in turn can mislead the amplitude algorithm and cause significant regular errors (> 30%).
In general, the proposed method is suitable for fairly accurate determination of the distance to the sound sources in the conditions of restricted visibility and noises.
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Keywords: acoustic signal, sound source, sound localization, sound intensity, distance determination algorithm, restricted visibility, cross-correlation function, impulse signal, Fourier transform, multi-channel recording, performance index, relative error, normal distribution, low-pass filtering.
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Received 29.07.2020
