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. SAINATI, R. A.: CAD of Microstrip Antennas for Wireless Applications, Artech House, Norwood, MA, 1996. KIROV, G. S.—MIHAYLOVA, D. P.: Circularly Polarized Aperture Coupled Microstrip Antenna with Resonant Slots and a Screen, Radioengineering 19 No. 1 (2010), 111-116. POZAR, D. M.—SCHAUBERT, D. H.: Microstrip Antennas, IEEE Press, 1995.

References 1. Callow, H. J. Signal Processing for Synthetic Aperture Sonar Image Enhancement. – University of Canterbury, Christchurch, New Zealand, 2003, Vol. 4 , pp. 32-34. 2. Namler, R., H. Runge. A Novel Processing SAR Focusing Algorithm Based on Chirp Scaling. – In: Proc. of IGARSS’92, Clear Lake, TX, May 1992, pp. 372-375. 3. Peng, S.-Y. The Key Technology Research on Missile-Bornesynthetic Aperture Radar Imaging. – National University of Defense Technology, 2011. 4. Wu, Y., H.-J. Song, J. Peng. Chirp Scaling Imaging Algorithm of SAR in High Squint Mode

R eferences [1] ZHANG, L.—DUAN, J.—QIAO, Z. J.—XING, M. D.—BAO, Z. : Phase Adjustment and ISAR Imaging of Maneuvering Targets with Sparse Apertures, IEEE Transactions on Aerospace and Electronic System 50 No. 3 (2014), 1955–1973. [2] XU, G.—XING, M. D.—XIA, X. G.—CHEN, Q. Q.—ZHANG, L.—BAO, Z. : High-Resolution Inverse Synthetic Aperture Radar Imaging and Scaling with Sparse Aperture, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 8 No. 8 (2015), 4010–4027. [3] DONOHO, D. L. : Compressed Sensing, IEEE Transactions on

Mismatch Harmonic Signal Recovery via Alternating Convex Search”, IEEE Signal Processing Letters , vol. 21, no. 8, 2014, pp. 1007–1011. [8] Y. Wang, B. Zhao and Y. C. Jiang, “Approach for Hig-Resolution Inverse Synthetic Aperture Radar Imaging of Ship Target with Complex Motion”, IET Signal Processing , vol. 7, no. 2, 2013, pp. 146–157.

Aperture Radar (EUSAR), 2010, pp. 1-4. 4. Maslikowski, L., K. Kulpa. Bistatic Quasi-Passive Noise SAR Experiment. – In: Proc. of 11th International Radar Symposium (IRS), 2010, pp. 1-3. 5. Lukin, K. A., A. A. Mogyla, V. P. Palamarchuk, P. L. Vyplavin, O. V. Zemlyaniy, Y. A. Shiyan, M. Zaets. Ka-Band Bistatic Ground-Based Noise Waveform SAR for Short-Range Applications. – Radar, Sonar & Navigation, IET, Vol. 2 , 2008, Issue 4, pp. 233-243. 6. Tarchi, D., K. Lukin, J. Fortuny-Guasch, A. Mogyla, P. Vyplavin, A. Sieber. SAR Imaging with Noise Radar. – IEEE Transactions

aperture admittance of a rectangular waveguide radiating into a lossy halfspace. Technical Report 1691-1, Ohio State University, Columbus, Ohio. [4] Ganchev, S.I., Bakhtiari, S., Zoughi, R. (1992). A novel numerical technique for dielectric measurement of generally lossy dielectrics. IEEE Transactions on Instrumentation and Measurement, 41 (3), 361-365. [5] Bakhtiari, S., Ganchev, S.I., Zoughi, R. (1993). Openended rectangular waveguide for nondestructive thickness measurement and variation detection of lossy dielectric slabs backed by a conducting plate. IEEE

-31. 4. Zeljkovic, V., Q. Li, R. Vincelette, C. Tameze., F. Liu. Automatic Algorithm for Inverse Synthetic Aperture Radar Images Recognition and Classification. - IET Radar, Sonar & Navig., Vol. 4, 2010, No 1, pp. 96-109. 5. Park, S.-H. Automatic Recognition of ISAR Images of Multiple Targets and ATR Results. - Progress in Electromagnetics Research, 2014, No 61, pp. 43-54. 6. Zeljkovic, V. Algorithms for ISAR Image Recognition and Classification. - Proc. of IGI Global (Disseminator of Knowledge), Ch. 10, 2014. 7. Rice, F., T. Cooke, D. Gibbin s. Model Based ISAR Ship

References 1. Alpers, W. and Huhnerfuss H. (1989). The damping of ocean waves by surface films: A new look at an old problem, Journal of Geophysical Research , 94, 6251-6265. 2. Brekke C., A. Solberg (2005) Oil spill detection by satellite remote sensing, Remote Sensing of Environment . 95, 1-13. 3. Espedal, H.A., Johannessen, O.M. (2000). Detection of oil spills near offshore installations using synthetic aperture radar (SAR), International Journal of Remote Sensing . 21 (11), 2141-2144. 4. Espedal H, Wahl T., (1999) Satellite SAR oil spill detection using


Synthetic aperture radars (SAR) allow to obtain high resolution terrain images comparable with the resolution of optical methods. Radar imaging is independent on the weather conditions and the daylight. The process of analysis of the SAR images consists primarily of identifying of interesting objects. The ability to determine their geographical coordinates can increase usability of the solution from a user point of view. The paper presents a georeferencing method of the radar terrain images. The presented images were obtained from the SAR system installed on board an Unmanned Aerial Vehicle (UAV). The system was developed within a project under acronym WATSAR realized by the Military University of Technology and WB Electronics S.A. The source of the navigation data was an INS/GNSS system integrated by the Kalman filter with a feed-backward correction loop. The paper presents the terrain images obtained during flight tests and results of selected objects georeferencing with an assessment of the accuracy of the method.


Our vision in the twilight or dark is strongly affected by the intraocular light scattering (straylight). Of especial importance is to assess this phenomenon in view of the night driving. The authors have studied the spectral dependence of retinal stray-light and estimated the possibility to reduce it with yellow filters and small apertures. For the measurements the direct compensation flicker method was used. The results show that this spectral dependence is close to Rayleigh's scattering (∝λ-4). As could be expected from the known data, the yellow filter should reduce retinal straylight, especially for blue light. However, in the experiments this scattering was not removed with such a filter but instead slightly increased. The optical apertures reduced light scattering in the eye, especially for red color.