Noise immunity of images autofocusing algorithm by entropy minimum at complex background situation

Аuthors

Likhachev V. P.^*, Sidorenko S. V.^**

Air force academy named after professor N.E. Zhukovskii and Y.A. Gagarin, Voronezh, Russia

*e-mail: lvp_home@mail.ru
**e-mail: sidor-vire@rambler.ru

Abstract

Modern SAR ensure a longer range, compared to optical systems, a faster (within a few seconds) radar image of a large land area receiving with a sub-meter resolution, as well as independence of the images quality from weather conditions and natural illumination state of the scenery.

Comparatively small weight-size indices of modern SAR allowed install them on unmanned aerial vehicles (UAVs) of small class, which application reduces significantly the operation costs and maintenance of a carrier. However, application of such UAVs as SAR carriers is associated with significant instabilities in the trajectory and flight speed due to atmospheric turbulence.

To form a high-quality radar image with given resolution in time scale close to the real one, it is necessary to obtain accurate information on the carrier motion parameters and, primarily, its flight speed.

Placing a high-precision inertial navigation system on a small class UAV is impossible, and application of navigation receivers under interference conditions does not ensure the required accuracy in estimating the SAR carrier speed while the radar image formation. To compensate the speed estimation error, various radar image autofocusing algorithms are employed, such as the radar image autofocusing with regard to the minimum entropy function. It does not require the presence of powerful point-like reflectors in the field of vision. However, to evaluate the efficiency, for example, in solving the problems of correcting navigation errors by the radar image in conditions of a large noise / signal ratio q, additional investigations are required.

The relevance of the article is determined by the need to form a radar image with a specified resolution in a time scale close to the real one, by a small SAR installed on the UAV, which lacks the capability to compensate for trajectory instabilities from information from the inertial navigation system, etc. In the presence of noise-masking interference and background reflections, it is necessary to determine the boundaries of the stable radar image autofocus algorithm functioning at minimum entropy.

The article deals with the operation of a radar station with digital SAR with the RLI autofocusing for a minimum of the entropy function in conditions of noise-masking interference and complex background situation.

Keywords:

radar with digital synthetic aperture antenna, entropy, radar images, autofocusing, unmanned aerial vehicle

References

1. Kupryashkin I.F., Likhachev V.P. Kosmicheskaya radiolokatsionnaya s«emka zemnoi poverkhnosti v usloviyakh pomekh (Space radar mapping under interference conditions), Voronezh, Nauchnaya kniga, 2014, 460 p.

2. Verba V.S., Neronskii L.B., Osipov I.G., Turuk V.E. Radiolokatsionnye sistemy zemleobzora kosmicheskogo bazirovaniya (Space based radar systems for Earth observance), Moscow, Radiotekhnika, 2010, 675 p.

3. Michael Israel Duersch, BYU micro-SAR: A very small, low-power LFM-CW Synthetic Aperture Radar Brigham Young University. All Theses and Dissertations, 12 March 2004, pp. 728.

4. Margaret Cheney and Brett Borden. Fundamentals of Radar Imaging, Society for Industrial and Applied Mathematics (SIAM), 2009, pp. 63-66.

5. Palubinskas G., Meyer F., Runge H., Reinartz P., Scheiber R., Bamler R. Estimation of along–track velocity of road vehicles in SAR data, Proc. of SPIE, Bruges, October 2005, vol. 5982, pp. 1 – 9.

6. Zhong Lu, Oh-Ig Kwoun, Russel Rykhus. Interferometric Synthetic Aperture Radar: Its Past, Present and Future, Photogrammetric Engineering & Remote Sensing, 2007, vol. 73, issue 3, pp. 217 – 221.

7. Evan C. Zaugg. Generalized Image Formation for Pulsed and LFM-CW Synthetic Aperture Radar, Ph.D. Dissertation, Brigham Young University, Provo, Uztah, 2010, 176 p.

8. Antipov V.N., Goryainov V.T., Kulin A.N. et al. Radiolokatsionnye stantsii s tsifrovym sintezirovaniem apertury antenny (Radar stations with digital synthesis of aerial aperture), Moscow, Radio i svyaz’, 1988, 304 c.

9. Jarly’kov M.S. Sputnikovye radionavigatsionnye sistemy (Satellite Radio Navigation Systems), Moscow, Radiotekhnika, 2013, vol. 1. 190 p, vol. 2. 180 p.

10. Nauchno-tekhnicheskaya informatsiya VINITI, 2002, no. 3, pp. 66 – 70.

11. Bogomolov A.V., Kupryashkin I.F., Likhachev V.P., Ryazantsev L.B. Trudy XXIX Vserossiiskogo simpoziuma “Radiolokatsionnoe issledovanie prirodnykh sred” Saint-Petersburg, VKA imeni A.F. Mozhaiskogo, 2015, 711 p.

12. Bolkunov A.A., Ryazantsev L.B., Sidorenko S.V. Voennaya mysl’, 2017, no. 9, pp. 70 – 74.

13. Likhachev V.P., Ryazantsev L.B., Cherednikov I.Yu. Voennaya mysl’, 2016, no. 3, pp. 24 – 28.

14. Zhuravlev A.V. Novye sposoby obespecheniya elektromagnitnoi sovmestimosti tekhniki radiopodavleniya i apparatury potrebitelei GNSS (New electromagnetic compatibility methods for radio countermeasures technique and consumers equipment), Voronezh, Nauchnaya kniga, 2017, 152 p.

15. Romanov A.S., Turlykov P. Yu. Trudy MAI, 2013, no. 56, available at: http:/trudi.mai.ru/eng/published.php?ID=66445

16. Likhachev V.P., Semenov V.V., Veselkov A.A. Antenny, 2017, no. 12 (244), pp. 31 – 37.

17. Kondratenkov G.S. Aviatsionnye sistemy radiovideniya (Aircraft radio vision systems), Moscow, Radiotekhnika, 2015, 648 p.

18. Kupryashkin I.F., Likhachev V.P., Mubarak N Kh. Antenny, 2007, no. 4, pp. 39 – 43.

19. Shkol’nyi L.A., Tolstov E.F., Detkov A.N. et al. Radiolokatsionnye sistemy vozdushnoi razvedki, deshifrirovanie radiolokatsionnykh izobrazhenii (Air reconnaissance radar systems, radar images decoding), Moscow, VVIA im. Prof. N.E. Zhukovskogo, 2008, 531 p.

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