Abstract and keywords
Abstract (English):
The paper describes the current version (v.1.1) of the algorithm for automatic classification of signals received by ISTP SB RAS decameter coherent scatter radars. The algorithm is a self-learning neural network that determines the type of scattered signals from the results of physical modeling of radio wave propagation, using radar data and international reference models of the ionosphere and geomagnetic field. According to MAGW and EKB ISTP SB RAS radar data for 2021, the algorithm self-learns to classify scattered signals into initially unknown classes based on physically interpreted parameters of radio wave propagation and data measured by the radar, with 15 frequently observed out of 20 possible hidden classes identified, 14 of which can be interpreted from a physical point of view. To demonstrate the operation of the algorithm, we present the first statistical analysis of observations of signals assigned by the algorithm to classes which we interpret as scattering by meteor trails and scattering with the sporadic E layer respectively. Through a statistical analysis of EKB and MAGW radar data during 2021–2022, we demonstrate the range-altitude characteristics of signals of these types. A correlation is shown between the hourly average numbers of observations of both classes, as well as between the hourly average line-of-sight velocities obtained for both classes. The results obtained make it possible to interpret these classes as a meteor echo and sporadic scattering respectively, and to use radar data to study the interaction between the neutral atmosphere (studied from meteor scattering data) and the lower ionosphere (studied from observations of sporadic scattering). Currently, this classification algorithm works in ISTP SB RAS radars in automatic mode.

machine learning, signal classification, coherent scatter radars, meteor echo, sporadic scattering
Publication text (PDF): Read Download

1. Arnold V.I. On functions of three variables. American Mathematical Society Translations. Ser. 2. 1963, vol. 28, pp. 51–54. (Translation of Dokl. Akad. Nauk SSSR. 1957, vol. 114, iss. 4, pp. 679–681).

2. Berngardt O.I., Kurkin V.I., Kushnarev D.S., Grkovich K.V., Fedorov R.R., Orlov A.I., Kharchenko V.V. ISTP SB RAS decameter radars. Solar-Terr. Phys. 2020, vol. 6, iss. 2, pp. 63–73. DOI: 10.12737/stp-62202006.

3. Berngardt O.I., Kusonsky O.A., Poddelsky A.I., Oinats A.V. Self-trained artificial neural network for physical classification of ionospheric radar data. Adv. Space Res. 2022, vol. 70, iss. 10, pp. 2905–2919. DOI: 10.1016/j.asr.2022.07.054. (In print).

4. Bilitza D., Altadill D., Zhang Y., Mertens C., Truhlik V., Richards Ph., et al. The International Reference Ionosphere 2012 — a model of international collaboration. J. Space Weather Space Climate. 2014, vol. 4, id. A07, 12 p. DOI: 10.1051/swsc/2014004.

5. Blanchard G.T., Sundeen S., Baker K.B. Probabilistic identification of high-frequency radar backscatter from the ground and ionosphere based on spectral characteristics. Radio Sci. 2009, vol. 44, iss. 5, RS5012. DOI: 10.1029/2009rs004141.

6. Chisham G., Freeman M.P. A reassessment of SuperDARN meteor echoes from the upper mesosphere and lower thermosphere. J. Atmos. Solar-Terr. Phys. 2013, vol. 102, pp. 207–221. DOI: 10.1016/j.jastp.2013.05.018.

7. Dempster A.P., Laird N.M., Rubin D.B. Maximum likelihood from incomplete data via the EM algorithm. J. Royal Statistical Society: Ser. B (Methodological). 1977, vol. 39, no. 1, pp. 1–22. DOI: 10.1111/j.2517-6161.1977.tb01600.x.

8. Fedorov R.R., Berngardt O.I. Monitoring observations of meteor echo at the EKB ISTP SB RAS radar: algorithms, validation, statistics. Solar-Terr. Phys. 2021, vol. 7, no. 1, pp. 47–58. DOI: 10.12737/stp-71202107.

9. Ginzburg V.L. The Propagation of Electromagnetic Waves in Plasmas. Pergamon Press, 1970, 615 p.

10. Goodfellow I., Bengio Y., Courville A. Deep Learning (Adaptive Computation and Machine Learning Ser). MIT Press, 2016, 800 p.

11. Lavygin I.A., Berngardt O.I., Lebedev V.P., Grkovich K.V. Identifying ground scatter and ionospheric scatter signals by using their fine structure at Ekaterinburg decametre coherent radar. IET Radar, Sonar and Navigation. 2020, vol. 14, iss. 1, pp. 167–176. DOI: 10.1049/iet-rsn.2019.0192.

12. Malhotra A., Mathews J.D., Urbina J. Effect of meteor ionization on sporadic-E observed at Jicamarca. Geophys. Res. Lett. 2008, vol. 35, iss. 15. DOI: 10.1029/2008GL034661.

13. Nishitani N., Ruohoniemi J.M., Lester M., Baker J.B.H., Koustov A.V., Shepherd S.G., et al. Review of the accomplishments of mid-latitude Super Dual Auroral Radar Network (SuperDARN) HF radars. Progress in Earth and Planetary Sci. 2019, vol. 6, iss. 1, p. 27. DOI: 10.1186/s40645-019-0270-5.

14. Ribeiro A.J., Ruohoniemi J.M., Baker J.B.H., Clausen S., de Larquier S., Greenwald R.A. A new approach for identifying ionospheric backscatter in midlatitude SuperDARN HF radar observations. Radio Sci. 2011, vol. 46, iss. 4, RS4011. DOI: 10.1029/2011RS004676.

15. Ribeiro A.J., Ruohoniemi J.M. Ponomarenko P.V., Clausen L.B.N., Baker J.B.H., Greenwald R.A., et al. A comparison of SuperDARN ACF fitting methods. Radio Sci. 2013, vol. 48, iss. 3, pp. 274–282. DOI: 1002/rds.20031.

16. Siwei Yu., Ma J. Deep learning for geophysics: current and future trends. Rev. Geophys. 2021, vol. 59, iss. 3, e2021RG000742. DOI: 10.1029/2021rg000742.

17. Thébault E., Finlay C.C., Beggan C.D., Alken P., Aubert J., Barrois O., et al. International Geomagnetic Reference Field: the 12th generation. Earth, Planets and Space. 2015, vol. 67, iss. 1, p. 79. DOI: 10.1186/s40623-015-0228-9.

18. Vander Plas J. Python Data Science Handbook: Essential Tools for Working with Data. O’Reilly Media, Inc., 2016, 548 p.

19. URL: (accessed October 12, 2022).

20. URL: (accessed October 12, 2022).

21. URL: (accessed October 12, 2022).

22. URL: (accessed October 12, 2022).

Login or Create
* Forgot password?