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

Authors

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

References

Перечень ссылок

Кошкин Р.П. Беспилотные авиационные системы. - М. : Стратегические приоритеты, 2016. - 676 с.

Kartashov V.M., Oleynikov V.N, Sheyko S.A., Babkin S.I., Koryttsev I.V., Zubkov O.V. Peculiarities of small unmanned aerial vehicles detection and recognition. Telecommunications and Radio Engineering (English translation of Elektrosvyaz and Radiotekhnika). Volume 78, Issue 9, 2019, Pages 771-781.

Kartashov V.M., Oleynikov V.N, Sheyko S.A., Babkin S.I., Koryttsev I.V., Zubkov O.V., Anokhin M.A. Information characteristics of sound radiation of small unmanned aerial vehicles. Telecommunications and Radio Engineering (English translation of Elektrosvyaz and Radiotekhnika), V.77 (10), 2018, pp. 915-924.

Даник Ю.В., Бугаєв М.В. Аналіз ефективності виявлення тактичних безпілотних літальних апаратів за пасивними та активними засобами спостереження // Збірник наукових праць ЖВІ ДУТ. Інформаційні системи. Випуск 10. - 2015. - С.5-20.

Мошков П.А., Беляев И.В., Остриков Н.Н. Экспериментальное исследование акустических характеристик беспилотного летательного аппарата в заглушенной камере АК-2 // XI Интерн. научная конф. по беспилотной авиации "ГидроавиаСалон-2016", Геленджик, 23-24 сентября 2016 года. Тез. док. - М. : ЦАГИ, 2016. - С. 45.

Marino L. Experimental analysis of UAV-propellers noise // 16th AIAA/CEAS Aeroacoustics Conference. University «La Sapienza», Rome, Italy. - American Institute of Aeronautics and Astronautics, 2010. - P. 1-14.

Kozeruk S.A., Korzhyk A.V. Identification of small aircraft by acoustic radiation. Visnyk NTUU KPI Series Radiotekhnika Radiobuduvannia, 2019, Iss. 76, pp. 15-20.

Kartashov, V., Oleynikov, V., Koryttsev, I., Zubkov, O., Babkin S., Sheiko, S. Processing and Recognition of Small Unmanned Vehicles Sound Signals. International Scientific-Practical Conference on Problems of Infocommunications Science and Technology, PIC S. and T. 2018 – Proceedings 31 January 2019, Pages 392-396.

Pham T. TTCP AG-6: Acousting detection and tracking of UAVs / T.Pham, N.Srour // U.S. Army Research Laboratory. Proc. of SPIE. - 2004. - Vol. 54. - P. 24–29.

Zelnio A.M. Detection of small aircraft using an acoustic array. Thesis. B.S. – Electrical Engineering, Wright State University, 2007. - 55 p.

A. Bernardini, F. Mangiatordi, E.Pallotti, L. Capodiferro; F. Ugo Bordoni. Drone detection by acoustic signature identification. - Electronic Imaging, Imaging and Multimedia Analytics in a Web and Mobile World. 2017. - pp. 60-64.

Oleynikov V. N., Zubkov O. V., Kartashov V. M., Korytsev I. V., Babkin S. I., Sheiko S. A. Investigation of detection and recognition efficiency of small unmanned aerial vehicles on their acoustic radiation. Telecommunications and Radio Engineering (English translation of Elektrosvyaz and Radiotekhnika) Volume 78, Issue 9, 2019, Pages 759-770.

Олейников В.Н., О.В. Зубков О.В., Карташов В.М., Корытцев И.В., Бабкин С.И., Шейко С.А., Селезнев И.С. Экспериментальная оценка эффективности алгоритмов определения направления беспилотных летательных аппаратов на основе акустического излучения// Радиотехника. Всеукр. межв. научн.- техн. сб. - 199. - 2019, p. 29-37.

Бокс Дж., Дженкинс Г. Анализ временных рядов: Пер. с англ. - М. : Мир, 1974. - Вып.1. - 406 с.

Шпилевский Э.К. Оптимальная классификация наблюдений случайных процессов // Статистический проблемы управления. - Вильнюс: ИМиК АН ЛССР. - 1971. - № 1. - С. 61–75.

Шпилевский Э.К. Принципы динамической классификации стохастических процессов и систем // Статистические проблемы управления. - Вильнюс: ИМиК АН ЛССР. - 1973. - № 28. - 139 с.

Атамукас М.С., Гродинкас Г. Динамическое распознавание в технической диагностике электродвигателей~// Статистические проблемы управления. - Вильнюс: ИМиК АН ЛССР. - 1982. - № 56. - С. 65–92.

Конюхов В., Шепель В., Шпилевский Э.К. Распознавание состояния авиационного ГТД методом динамической классификации процессов, протекающих в двигателе //Статистические проблемы управления. - Вильнюс ИМиК АН ЛССР. - 1982. - № 56. - С. 112–121.

Конюхов В., Малинаускас З., Шепель В. Обнаружение быстро развивающихся неисправностей авиационных газотурбинных двигателей //Статистические проблемы управления. - Вильнюс: ИМиК АН ЛССР. - 1983. - № 61. - С. 29–35.

Омельченко В.А., Безрук В.М., Коваленко Н.П. Распознавание заданных радиосигналов при наличии неизвестных сигналов на авторегрессионной основе // Радиотехника. - 2001. - № 123. - С. 195–199.

Дробахин О.О. Автоматизация процесса распознавания сигналов дефектоскопа на основе модели линейного предсказания // Дефектоскопия. - 1985. - № 10. - С. 64–67.

Рамишвили Г.С. Автоматическое распознавание говорящего по голосу. - М. : Радио и связь, 1981. - 224 с.

Rabiner L.R, Levinson S.E. Isolated and Connected Word Recognition – Theory and Selected Applications // IEEE Transactions on Communications. - 1981. - Vol. Com–29, № 5. - P. 621–659.

Ли У. Методы автоматического распознавания речи: Пер. с англ. - М. : Мир, 1983. - 328 с.

Шпилевский Э.К. Опознавание динамических систем в обстановке помех // Автоматика и телемеханика. - 1974. - № 12. - С. 60–71.

Тихонов В.А., Нетребенко К.В. Параметрическая оценка спектров третьего порядка смеси негауссова сигнала и гауссовой коррелированной помехи // Радиоэлектроника (Изв. вузов). - 2005. - №2. - С. 35–43.

Тихонов В.А., Нетребенко К.В. Оценивание спектра негауссова сигнала на фоне гауссова белого шума с помощью кумулянтных функций // Радиоэлектроника и информатика. - 2004. - №4. - С. 10–14.

Марпл С. Л. Цифровой спектральный анализ и его приложения. - М. : Мир, 1990. - 584 с.

Тихонов В.А., Филь И.О. Модель составных векторних случайных процессов в задаче распознавания сигналов //Радиотехника. Всеукр.межвед.науч.-техн. Сб. - Вып. 178/2014. - С. 93-96.

V.А. Тikhonov, К.V. Netrebenko, and I.О. Fil. Correlation Analysis of Compound Vector Random Processes// Telecommunications and Radio Engineering, Begell House Inc., New York City, USA, 2015, Vol. 74, #13, pp. 1167 – 1173.

Кармалита В.А. Цифровая обработка случайных колебаний. - М: Машиностроение, 1986. - 80 с.

Tykhonov V. A., Kudriavtseva N. V., Chmelar P. Factorization of speech signals parametric spectra using multiplicative linear prediction models. Proceedings Elmar. 57th International Symposium ELMAR-2015, Zadar, 28-30 September 2015.

{References

Koshkin R. P. (2016) Unmanned aircraft systems Moscow: Strategic Priorities, 676 p. (In Russ.)

Kartashov V. M., Oleynikov V. N., Sheyko S. A., Babkin S. I., Korytsev I. V. and Zubkov O. V. (2019) Peculiarities of small unmanned aerial vehicles detection and recognition. Telecommunications and Radio Engineering, Vol. 78, Iss. 9, pp. 771-781. DOI: 10.1615/telecomradeng.v78.i9.30

Kartashov V. M., Oleynikov V. N., Sheiko S. A., Babkin S. I., Korytsev I. V., Zubkov O. V. and Anokhin M. A. (2018) Information characteristics of sound radiation of small unmanned aerial vehicles. Telecommunications and Radio Engineering, Vol. 77, Iss. 10, pp. 915-924. DOI: 10.1615/telecomradeng.v77.i10.70

Danyk Yu. V. and Bugaev M. V. (2015) Analysis of the effectiveness of detection of tactical unmanned aerial vehicles by passive and active means of observation, Zbirnyk naukovykh prats' ZHVI DUT. Informatsiyni systemy''15 Iss. 10, pp. 5-20.

Moshkov P. A., Belyaev I. and Ostrikov N. N. (2016) Experimental study of acoustic characteristics of a pilotless aircraft in a drowned AK-2 chamber, HydroaviaSalon-2016 Moscow: TsAGI, p. 45.

Marino L. (2010) Experimental analysis of UAV-propellers noise, 16th AIAA/CEAS Aeroacoustics Conference, University «La Sapienza», pp. 1-14.

Kozeruk S. O. and Korzhyk O. V. (2019) Detection Small Aircraft by Acoustic Radiation. Visnyk NTUU KPI Seriia - Radiotekhnika Radioaparatobuduvannia, Iss. 76, pp. 15-20. DOI: 10.20535/radap.2019.76.15-20

Kartashov V., Oleynikov V., Koryttsev I., Zubkov O., Babkin S. and Sheiko S. (2018) Processing and Recognition of Small Unmanned Vehicles Sound Signals. 2018 International Scientific-Practical Conference Problems of Infocommunications. Science and Technology (PIC S&T), pp. 392-396. DOI: 10.1109/infocommst.2018.8632162

Pham T. and Srour N. (2004) TTCP AG-6: acoustic detection and tracking of UAVs. Proc. SPIE 5417, Unattended/Unmanned Ground, Ocean, and Air Sensor Technologies and Applications VI. DOI: 10.1117/12.548194

Zelnio A. M. (2007) Detection of small aircraft using an acoustic array Thesis. B.S., Wright State University, 55 p.

Bernardini A., Mangiatordi F., Pallotti E. and Capodiferro L. (2017) Drone detection by acoustic signature identification. Electronic Imaging, Vol. 2017, Iss. 10, pp. 60-64. DOI: 10.2352/issn.2470-1173.2017.10.imawm-168

Oleynikov V. N., Zubkov O. V., Kartashov V. M., Korytsev I. V., Babkin S. I. and Sheiko S. A. (2019) INVESTIGATION OF DETECTION AND RECOGNITION EFFICIENCY OF SMALL UNMANNED AERIAL VEHICLES ON THEIR ACOUSTIC RADIATION. Telecommunications and Radio Engineering, Vol. 78, Iss. 9, pp. 759-770. DOI: 10.1615/telecomradeng.v78.i9.20

Oleynikov V. N., Zubkov O. V., Kartashov V. M., Korytsev I. V., Babkin S. I., Sheiko S. A. and Seleznev I.S. (2019) Experimental evaluation of effectiveness of algorithms for direction findingof unmanned aerial vehicles based on acoustic radiation. Radiotekhnika Iss. 199, p. 29-37.

Boxing J. and Jenkins G. (1974) Analysis of time series Moskow, Mir, Iss. 1, 406 p.

Shpilevsky E. K. (1971) Optimal classification of observations of random processes, Statistical control problems No. 1, pp. 61–75.

Shpilevsky E. K. (1973) Principles of dynamic classification of stochastic processes and systems, Statistical control problems No. 28, 139 p.

Atamukas M. and Grodinkas G. (1982) Dynamic recognition in the technical diagnosis of electric motors, Statistical control problems No. 56, pp. 65–92.

Konyukhov V., Shepel V. and Shpilevsky E.K. (1982) Recognition of the state of an aircraft gas turbine engine by the method of dynamic classification of processes occurring in an engine, Statistical control problems No. 56, pp. 112–121.

Konyukhov V., Malinauskas Z. and Shepel V. (1983) Detection of rapidly developing malfunctions of aircraft gas turbine engines Statistical control problems No. 61, pp. 29–35.

Omelchenko V.A., Bezruk V.M. and Kovalenko N.P. (2001) Recognition of preset radio signals in the presence of unknown signals on an autoregressive basis // Radio Engineering No. 123, pp. 195-199.

Drobakhin O. O. (1985) Automation of the recognition process of flaw detector signals based on the linear prediction model Flaw detection, No. 10, pp. 64–67.

Ramishvili G. S. (1981) Automatic recognition of the speaker by voice, Moskow, Radio and communications, 224 p.

Rabiner L. and Levinson S. (1981) Isolated and Connected Word Recognition--Theory and Selected Applications. IEEE Transactions on Communications, Vol. 29, Iss. 5, pp. 621-659. DOI: 10.1109/tcom.1981.1095031

Lee W. (1983) Methods of automatic speech recognition Moskow, Mir, 328 p.

Shpilevsky E.K. (1974) Identification of dynamic systems in a jamming environment, Automation and Remote Control No. 12, pp. 60–71.

Tykhonov V.A., Kudriavtseva N.V., Netrebenko K.V. and Chmelar P. (2015) The non-Gaussian signals' spectra estimation against a background of Gaussian correlated interference. 2015 25th International Conference Radioelektronika (RADIOELEKTRONIKA), . DOI: 10.1109/radioelek.2015.7129067

Tikhonov V. A. and Netrebenko K. V. (2004) Spectrum estimation of a non-Gaussian signal against a background of Gaussian white noise using cumulant functions, Radioelectronics and Informatics No. 4, pp. 10-14.

Marpl S. L. (1990) Digital spectral analysis and its applications Moskow, Mir, 584 p.

Tikhonov V. A. and Fil I. O. (2014) A model of composite vector random processes in the signal recognition problem Radio engineering. Iss. 178, pp. 93-96.

Tikhonov V. A., Netrebenko K. V. and Fil I. (2015) CORRELATION ANALYSIS OF COMPOUND VECTOR RANDOM PROCESSES. Telecommunications and Radio Engineering, Vol. 74, Iss. 13, pp. 1167-1173. DOI: 10.1615/telecomradeng.v74.i13.40

Karmalita V.A. (1986) Digital random processing Moskow, Mechanical Engineering, 80 p.

Tykhonov V. A., Kudriavtseva N. V. and Chmelar P. (2015) Factorization of speech signals parametric spectra using multiplicative linear prediction models. 2015 57th International Symposium ELMAR (ELMAR). DOI: 10.1109/elmar.2015.7334504

Downloads

Published

2020-06-30

How to Cite

Тихонов , В. А., Карташов , В. М., Олейников , В. Н., Леонидов , В. И., Тимошенко , Л. П., Селезнев , И. С. and Рыбников , Н. В. (2020) “Detection-Recognition of Unmanned Aerial Vehicles using the Composed Auto-Regression Model of their Acoustic Radiation”, Visnyk NTUU KPI Seriia - Radiotekhnika Radioaparatobuduvannia, (81), pp. 38-46. doi: 10.20535/RADAP.2020.81.38-46.

Issue

No. 81 (2020)

Section

Theory and Practice of Radio Measurements

License

Authors who publish with this journal agree to the following terms:

  1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
  2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
  3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).