Simulating of radar signals of unmanned aerial vehicles for non-Gaussian distributions of complex amplitudes

The structure and functioning algorithms of the simulator of radar signals reflected from small-sized unmanned aerial vehicles (UAVs) have been developed. A specific feature of the simulator is the ability to generate arbitrarily correlated signal implementations of the radar input influence for the cases where the random amplitude of the reflected signal (RS) has a Rayleigh, Nakagami, Weibull or lognormal distribution. Analytical expressions are presented for calculating the parameters of the probability density function, as well as generating samples of the random amplitude of the RS with a given distribution law from the samples of the initial implementations of the Gaussian process. Provision is made for normalizing the RS average power to the average value of the modeled target RCS and a specified correlation time for fluctuations of RS complex amplitudes is ensured. The parameters of the generated RS implementations correspond to the values of real radar characteristics of small-sized UAVs obtained experimentally. The application of the proposed simulator is to analyze the effectiveness of newly developed and known UAV detection algorithms using the mathematical modeling method.

1. Shirman Y. D. Computer simulation of aerial target radar scattering, recognition, detection, and tracking. London, 2002. 294 p.

2. Gavrilov K. Yu., Kamensky I. V., Kirdyashkin V. V., Linnikov O. N. Modeling and processing of radar signals in Matlab: textbook. Moscow, 2020. 264 p. (in Russian).

3. Solonar A. S., Yarmolik S. N., Khramenkov A. S., Mikhalkovsky A. A., Khmarsky P. A. The object designer of program modelling complex of radar-tracking signals. Doklady BGUIR, 2014, no. 6 (84), pp. 60–66 (in Russian).

4. Kostromitsky S. M., Nefedov D. S., Khramenkov A. S., Chigryai V. G. Statistical models of radar cross section fluctuations of small-sized unmanned aerial vehicles. Vestnik Koncerna VKO «Almaz – Antey» = Journal of «Almaz – Antey» Air and Space Defence Corporation, 2023, no. 3, pp. 24–36 (in Russian).

5. Ezuma M., Anjinappa C. K., Funderburk M., Guvenc I. Radar cross section based statistical recognition of UAVs at microwave frequencies. IEEE Transactions on Aerospace and Electronic Systems, 2022, vol. 58, no. 1, pp. 27–46. https://doi.org/10.1109/taes.2021.3096875

6. Guay R., Drolet G., Bray J. R. Measurement and modelling of the dynamic radar cross-section of an unmanned aerial vehicle. IET Radar, Sonar and Navigation, 2017, vol. 11, no. 7, pp. 1155–1160. https://doi.org/10.1049/iet-rsn.2016.0520

7. Markow J., Balleri A., Catherall A. Statistical analysis of in-flight drone signatures. IET Radar, Sonar and Navigation, 2022, vol. 16, no. 11, pp. 1737–1751. https://doi.org/10.1049/rsn2.12293

8. Pieraccini M., Miccinesi L., Rojhani N. RCS measurements and ISAR images of small UAVs. IEEE Aerospace and Electronic Systems Magazine, 2017, vol. 32, no. 9, pp. 28–32. https://doi.org/10.1109/maes.2017.160167

9. Rosamila M., Aubry A., Ballery A., Carotenuto V., De Maio A. RCS measurements of UAVs and their statistical analysis. IEEE 9th International Workshop on Metrology for AeroSpace. Pisa, 2022, pp. 179–184. https://doi.org/10.1109/metroaerospace54187.2022.9856394

10. Shnidman D. A. Radar detection probabilities and their calculation. IEEE Transactions on Aerospace and Electronic Systems, 1995, vol. 31, № 3, pp. 928–950. https://doi.org/10.1109/7.395246

11. Farina A., Russo A., Scannapieco F., Barbarossa S. Theory of radar detection in coherent Weibull clutter. IEE Proceedings F (Communications, Radar and Signal Processing), 1987, vol. 134, no. 2, pp. 174–190. https://doi.org/10.1049/ip-f-1.1987.0034

12. Fedorchenko V. A. Theory of multidimensional distributions. Moscow, 2003. 576 p. (in Russian).

13. Okhrimenko A. E. Fundamentals of radar and electronic warfare. Moscow, 1983. 456 p. (in Russian).

14. Kostromitsky S. M., Nefedov D. S. Radar characteristics of micro-UAV. Vestnik Koncerna VKO «Almaz – Antey» = Journal of «Almaz – Antey» Air and Space Defence Corporation, 2023, no. 3. pp. 12–23 (in Russian).

15. Bykov V. V. Digital modeling in statistical radio engineering. Moscow, 1971. 328 p. (in Russian).

16. Levin L. Methods of solving technical problems using analog computers. Moscow, 1966. 414 p. (in Russian).

17. Dyatko A. A. Kostromitsky S. M., Shumsky P. N. Modeling of stationary random processes with given characteristics. Trudy Belorusskogo gosudarstvennogo tekhnologicheskogo universiteta. Seriya VI. Fiziko-matematicheskie nauki i informatika = Proceedings of the Belarusian State Technological University. Series VI. Physical and Mathematical Sciences and Computer Science, 2006, iss. XIV, pp. 144–146 (in Russian).

18. Richards М. А., Scheer J. A., Holm W. A. Principles of modern radar: basic principles. Edison, 2010. 924 p. https://doi.org/10.1049/sbra021e

19. Vadzinsky R. N. Handbook of probability distributions. Saint Petersburg, 2001. 295 p. (in Russian).

20. Levin B. R. Theoretical foundations of statistical radio engineering. Moscow, 1989. 656 p. (in Russian).