Trajectory signal modelling in the aperture synthesis radar based on optical images of the Earth surface


DOI: 10.34759/trd-2021-118-12

Аuthors

Gavrilov K. Y.1*, Kamensky K. V.2, Malyutina O. A.1

1. Moscow Aviation Institute (National Research University), 4, Volokolamskoe shosse, Moscow, А-80, GSP-3, 125993, Russia
2. NPO Energomash named after academician V.P. Glushko, 1, Burdenko str., Khimki, 141400, Russia

*e-mail: gvrk61@mail.ru

Abstract

The article deals with the development and research of algorithms for computer simulation of the signal trajectory in the aperture synthesis radar. It substantiates such modelling relevance, which is stipulated by the necessity to obtain radar images of one and the sane scene under various probing conditions. The radar images obtained thereby may be employed to analyze various synthesizing algorithms, trajectory instabilities, equipment errors and other factors affecting the quality of the resulting synthesized image.

These are the methods for direct and reverse forming of the trajectory signal. The first group of methods, in its turn, can use numerical methods of electrodynamics, or methods of geometric optics. The most appropriate approach is based on the methods of geometric optics, when optical images are used to form the amplitudes and phases of the trajectory signals of the probed scene, i.e. aerial photographs, photos from space, and etc. Based on this approach, trajectory signals and radar images synthesized on their basis were obtained when a continuous signal with linear frequency modulation was used as a probing signal.

The article presents the examples of the trajectory signals computer simulation and their corresponding synthesized radar images with high resolution (above 0.5 m). The trajectory signal simulation method based on optical images may lead to the synthesized images distortions, which appear in the form of alternating dark and light bands located horizontally and vertically. The conditions under which such distortions occur, as well as methods for their elimination, were determined. The reasons for the distortions appearance in the form of bands are, firstly, the discreteness of the reflectors’ location point in the optical image, and, secondly, the interference of the radio signals reflected from a group of closely located reflectors. In literature, the second phenomenon is called the speckle effect.

To eliminate distortion of the images synthesized on the basis of the described method for the trajectory signal simulation, the article proposes to adding a random phase component to the signals of point reflectors. This technique allows to forming the trajectory signals close to real signals.

The article also provides examples of the application of trajectory signal modeling to analyze the distortions of synthesized radar images in the presence of trajectory instabilities with different amplitudes.

Keywords:

aperture synthesis radar, trajectory signal, radar image, optical image, computer simulation

References

  1. Aguasca A., Aveco-Herrera R., Broquetas A., Mallorqui Jordi J., Fabregas X. ARBRES: Light-Weight CW/FM SAR Sensors for Small UAVs, Sensors, 2013, vol. 13 (3), pp. 3204 — 3216. DOI:10.3390/s13030320400

  2. Allan J., Collins M.J. Sarsim: A Digital Sar Signal Simulation System, In Proceedings of the Remote Sensing & Photogrammetry Society, RSPSoc, Newcastle upon Tyne, UK, 11–14 September 2007.

  3. Batet O., Dios F., Comeron A., Agishev R. Intensity-modulated linear-frequency-modulated continuous-wave lidar for distributed media: fundamentals of technique, Applied Optics, 2010, vol. 49, no. 17, pp. 3369 — 3379. DOI:10.1364/AO.49.003369

  4. Chang Wenge, Tian Haishan, Gu Chengfei. FMCW SAR: From design to realization, Geoscience and Remote Sensing Symposium (IGARSS), 2016 IEEE International, Beijing, China, July 10–15, 2016. DOI: 10.1109/IGARSS.2016.7729284

  5. Franceschetti G., Migliaccio M., Riccio D., Schirinzi G. SARAS: A Synthetic Aperture Radar (SAR) Raw Signal Simulator, IEEE Transactions on Geoscience and Remote Sensing, 1992, vol. 30, no. 1, pp. 110 — 123.

  6. Franceschetti G, Iodice A, Riccio D. Efficient simulation of airborne SAR raw data of extended scenes, IEEE Trans Geoscience and Remote Sensing, 2006, vol. 44 (10), pp. 2851 — 2860. DOI:10.1109/TGRS.2006.875786

  7. Guo Yuhua, Liu Qinhuo, Zhong Bo, Yang Xiaoyuan. Efficient SAR Raw Data Simulation including Trajectory Deviations and Antenna Pointing Errors, Progress In Electromagnetics Research B, 2017, vol. 72, pp. 111 — 128. DOI:10.2528/PIERB16102102

  8. Khwaja A.S., Ferro-Famil L., Pottier E. SAR Raw Data Simulation in the Frequency Domain, Proceedings of the 3rd European Radar Conference, Manchester, UK, September 2006, pp. 277 — 280. DOI:10.1109/EURAD.2006.280328

  9. Kulpa K., Samczynski P., Malanowski M., Gromek A., Gromek D., Gwarek W., Salski B., Tanski G. An advanced SAR simulator of three-dimensional structures combining geometrical optics and full-wave electromagnetic methods, IEEE Transactions on Geoscience and Remote Sensing, 2014, vol. 52, no. 1, pp. 776 — 784. DOI:10.1109/TGRS.2013.2283267

  10. Li Wei, Zhang Houxiang, Hildre Hans Petter. A real-time UAV INSAR raw signal simulator for HWIL simulation system, Proceedings 28th European Conference on Modelling and Simulation, Brescia, Italy, 2014, pp. 94 — 100. DOI:10.7148/2014-0094

  11. Mori A., De Vita F. A Time-Domain Raw Signal Simulator for Interferometric SAR, IEEE Transactions on Geoscience and Remote Sensing, 2004, vol. 42, no. 9, pp. 1811 — 1817. DOI:10.1109/TGRS.2004.832242

  12. Navneet S., Ashish Roy, Bhattacharya C. Image Generation Algorithms for FMCW-SAR at X-Band, 9-th International Radar Symposium, India, 2013, (IRSI-13). Bangalore.

  13. Schlutz M. Synthetic Aperture Radar Imaging Simulated in MATLAB, California Polytechnic State University, San Luis Obispo, California, 2009, 77 p.

  14. Sheng Hui, Wang Kaizhi, Liu Xingzhao, Li Jianjun. A fast RAW data simulator for the stripmap SAR based on CUDA via GPU, 2013 IEEE International Geoscience and Remote Sensing Symposium, 2013, pp. 915 — 918. DOI:10.1109/IGARSS.2013.6721309

  15. Shoalehvar A. Synthetic Aperture Radar (SAR) Raw Signal Simulation, San Luis Obispo, California, 2012. DOI:10.15368/THESES.2012.76

  16. Soumech. M. Synthetic Aperture Radar Signal Processing with MATLAB Algorithms, New York, John Wiley & Sons, Inc, 1999, 616 p.

  17. Weijie Xia, Jianjiang Zhou. A Raw Signal Simulator for Bistatic SAR, Chinese Journal of Aeronautics, 2009, no. 22 (4), pp. 434 — 443. DOI:10.1016/S1000-9361(08)60122-3

  18. Yang Liang, Yu Wei-Dong, Luo Yun-Hua, Zheng Shi-Chao. Efficient Strip-Mode SAR Raw Data simulator of extended scenes included moving targets, Progress In Electromagnetics Research B, 2013, vol. 53, pp. 187 — 203. DOI:10.2528/PIERB13050205

  19. Zaugg E., Edwards M., Long D., Stringham C. Developments in Compact High-Performance Synthetic Aperture Radar Systems for Use on Small Unmanned Aircraft, 2011 IEEE Aerospace Conference, Big Sky, MT, USA, March 5–12, 2011. DOI:10.1109/AERO.2011.5747414

  20. Zaugg E. Generalized Image Formation for Pulsed and LFM-CW Synthetic Aperture Radar, Brigham Young University, Provo, Utah, 2010, 161 p.

  21. Zhang Fan, Hu Chen, Li Wei, Hu Wei, Li Heng-Chao. Accelerating Time-Domain SAR Raw Data Simulation for Large Areas Using Multi-GPUs, IEEE Journal of Selected Topics in Apploed Earth Observations and Remote Sensing, 2014, vol. 7, no. 9, pp. 3956 — 3966. DOI:10.1109/JSTARS.2014.2330333

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

  23. Gavrilov K.Yu., Kamenskii K.V. Radiotekhnika, 2019, vol. 83, no. 11 (17), pp. 26 — 42. DOI: 10.18127/j00338486-201911(17)-03

  24. Gavrilov K.B., Kamenskii I.V., Kirdyashkin V.V., Linnikov O.N. Modelirovanie i obrabotka radiolokatsionnykh signalov v Matlab (Radar signals simulation and processing of in Matlab), Moscow, Radiotekhnika, 2020, 264 p.

  25. Gavrilov K.Yu., Kanashchenkov A.I., Nuzhdin V.M., Panyavina N.S. Informatsionno-izmeritel’nye i upravlyayushchie sistemy, 2018, vol. 16, no. 6, pp. 31 — 46.

  26. Gusev S.N., Sakhno I.V., Khubbiev R.V. Trudy MAI, 2019, no. 104. URL: http://trudymai.ru/eng/published.php?ID=102169

  27. Zanin K.A. Trudy MAI, 2017, no. 96. URL: http://trudymai.ru/eng/published.php?ID=85931

  28. Kamenskii K.V., Gavrilov K.Yu. 18-ya Mezhdunarodnaya konferentsiya «Aviatsiya i kosmonavtika — 2019»: tezisy dokladov, Moscow, Logotip, 2019. pp. 122 — 123.

  29. Kondratenkov G.S., Frolov A.Yu. Radiovidenie. Radiolokatsionnye sistemy distantsionnogo zondirovaniya Zemli (Radar systems for remote the Earth sensing), Moscow, Radiotekhnika, 2005, 368 p.

  30. Sentsov A.A., Nenashev V.A., Ivanov S.A., Turnetskaya E.L. Trudy MAI, 2021, no. 117. URL: http://trudymai.ru/eng/published.php?ID=156227. DOI: 10.34759/trd-2021-117-08

  31. Starovoitov E.I., Yurchik I.A. Trudy MAI, 2019, no. 108. URL: http://trudymai.ru/eng/published.php?ID=109500


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