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ISSN 2415-3400 (Online)
ISSN 1028-821X (Print)

Usage of phase portraits in analysis of doppler signals reflected from drone rotors

Pashchenko, RE, Ivanov, VK, Tsyupak, DO
Organization: 

 

O. Ya. Usikov Institute for Radiophysics and Electronics of the National Academy of Sciences of Ukraine
12, Proskura st., Kharkov, 61085, Ukraine

E-mail:  r.paschenko@i.ua , ivanov@ire.kharkov.ua , tsyupak87@mail.ru

https://doi.org/10.15407/rej2020.04.018
Language: ukranian
Abstract: 

 

Subject and Purpose. Comparative analysis of the shapes of phase portraits of Doppler signals (DS) reflected from drone rotating rotors is given with regard to different time delays. The shapes of DS phase portraits are examined for various rotational velocities and numbers of the rotating rotors.

Methods and Methodology. A method using phase portraits is suggested for analysis of Doppler signals reflected from drone rotating rotors. The method determines the number of the rotors and estimates their rotational velocities.

Results. It has been found that the shape of the phase portrait of the baseline signal is practically independent of the time delay and can be described as an occasional movement of the image point following the phase trajectory in the center of the phase portrait. The appearance of characteristic regions on the periphery of the phase portrait allows separating baseline and sounding signals. It has been shown that the shape of the DS phase portrait of the rotating rotor during the flight movement of the drone depends on the time delay value. With a larger delay, phase portraits similar in shape appear at regular intervals equal to the Doppler signal period. With a larger rotational velocity of the rotor, the rate of similar phase portrait appearance diminishes.

Conclusion. During the sounding of drone rotating rotors, the shapes of DS phase portraits depend on the value of time delay. With a larger number of rotors in the drone flight, the periodic change character as to the DS phase portrait shape remains unchanged. In this case, DS phase portrait shapes differ substantially for different numbers of drone rotors

Keywords: Doppler signal, drone, phase portrait, pseudophase plane

Manuscript submitted  13.07.2020
Radiofiz. elektron. 2020, 25(4): 18-29
Full text (PDF)

References: 

 

 

1. RC multicopters. Different types of multicopters. LIVEJOURNAL. Available at: https://rcdrone.livejournal.com/5115.html (in Russian)
 
2. Asmakov S. Modern multicopters: variety of models and problem of choice. KomputerPress. Available at: https://compress.ru/post/20160601-m-copters-choice (in Russian)
 
3. Pashchenko, R.E., Ivanov, V.K., Tsyupak, D.O., Levadniy, U.V., 2019. Frequency-temporal analysis radar reflections from multirotor drone. Radiofiz. Elektron., 24(4), pp. 35-45. (in Russian). DOI: https://doi.org/10.15407/rej2019.04.035
 
4. Berge, P., Pomo, I., Vidal, K., 1991. Order in chaos. About deterministic approach to turbulence. Translated from French. Moscow: Mir Publ. (in Russian).
 
5. Malineckij, G.G., Potapov, A.B., 2002. Modern problems of nonlinear dynamics. Moscow: Editorial URSS Publ. (in Russian).
 
6. Mun, F., 1990. Chaotic vibrations: Introductory course for researches workers and engineers. Translated from English. Moscow: Mir Publ. (in Russian).
 
7. Takens, F., 1981. Detecting strange attractors in turbulence. In: Dynamical Systems and Turbulence. Lecture Notes in Mathematics. Ed. by D.A. Rand L.S. Young. Heidelberg: Springer-Verlag, pp. 366-381. DOI: https://doi.org/10.1007/BFb0091924
 
8. Paschenko, R.E., Kortunov, V.I., Tsyupak, D.O., Bardanova, O.A., 2013. Recognition Drone of multi-rotor type with the use phase portraits. Science and Technology of the Air Force of Ukraine, 4(13), pp. 68-72 (in Russian).
 
9. Pashchenko, R.E., Tsyupak, D.O., Ratajchuk, I.A., Bardanova, O.A., 2015. Analysis form of phase portraits at a change time delay for recognition Drone of multi-rotor type. In: Yu.V. Stasev, ed. 2015. Information Processing Systems. Kharkiv: Ivan Kozhedub Kharkiv National Air Force University (KNAFU) Publ. 1(126), pp. 44-49 (in Russian).
 
10. K-Band CW Transceiver IPS-154. Available at: https://www.innosent.de/en/radar-systems/product-finder/
 
11. FRACTAN. Program for the calculation of cross-correlation dimension and cross-correlation entropy on the temporal information row. Available at: http://www.iki.rssi.ru/magbase/RESULT/ APPENDIX/fractan.boom.ru/soft.htm