اخبار و اعلانات
آمار
| تعداد نشریات | 34 |
| تعداد شماره‌ها | 1,289 |
| تعداد مقالات | 9,294 |
| تعداد مشاهده مقاله | 8,800,386 |
| تعداد دریافت فایل اصل مقاله | 5,339,878 |
روشی کارآمد برای تصویربرداری از اهداف چندگانه در ارتفاع پست در حضور کلاتر دریا با استفاده از رادار روزنه مصنوعی معکوس(ISAR) | ||
| رادار | ||
| مقاله 5، دوره 8، شماره 2 - شماره پیاپی 24، دی 1399، صفحه 47-59 اصل مقاله (804.58 K) | ||
| نوع مقاله: مقاله پژوهشی | ||
| نویسندگان | ||
| علی جبار رشیدی* 1؛ رضا محمدی2 | ||
| 1دانشیار، دانشگاه صنعتی مالک اشتر، تهران، ایران | ||
| 2کارشناسی ارشد، دانشگاه صنعتی مالک اشتر، تهران، ایران | ||
| تاریخ دریافت: 17 آذر 1399، تاریخ بازنگری: 11 بهمن 1399، تاریخ پذیرش: 30 بهمن 1399 | ||
| چکیده | ||
| تصویربرداری با استفاده از رادار روزنه مصنوعی معکوس (ISAR) از اهداف چندگانه یکی از مسائل مهم و چالش برانگیز در تصویربرداری راداری محسوب می‌شود. همچنین آشکارسازی و تصویربرداری از اهداف در ارتفاع پست و نزدیک سطح به دلیل وجود کلاتر سطوح نیز از موضوعات تحقیقاتی در این حوزه است. اگر اهداف هوایی در محیط دریا درارتفاع کم در حال پرواز باشند، کلاتر دریا نیز بر سیگنال‌های دریافتی رادار از این اهداف تاثیر زیادی گذاشته و بر دشواری موجود در تصویربرداری ISAR خواهد افزود. در این مقاله که از نتایج یک پروژه تحقیقاتی استخراج شده، الگوریتمی کارآمد ارائه شده است که بتوان با وجود چالشهای پیش گفته، تصویربرداری ISAR از اهداف چندگانه در ارتفاع پست را در حضور کلاتر دریا انجام داد. در این الگوریتم با عنوان گروه‌بندی اهداف، مبتنی بر پردازش وفقی فضا-داپلر (SDAP) از اثر کلاتر دریا کاسته و سپس با جبران‌سازی حرکت انتقالی اهداف به‌صورت گروهی، تصویر آن‌ها تشکیل می شود. خوشه‌بندی(گروه بندی) اهداف مبتنی بر شباهت پارامترهای حرکت انتقالی و تشکیل تصویر هرگروه در یک قاب از مهمترین بخشهای این الگوریتم می باشند. نتایج پیاده سازی نرم افزاری و شبیه‌سازی نشان می‌دهد که می‌توان به طور موثری با استفاده از روش SDAP اثر کلاتر دریا را کاهش و با الگوریتم ارائه شده تصویر گروهی اهداف در خوشه‌ها با موفقیت تشکیل داد. این نتیجه در مسائل کاربردی از جمله آشکارسازی و شناسایی اهداف هوایی در محیط دریا بسیار حائز اهمیت می‌باشد. | ||
| کلیدواژه‌ها | ||
| رادار روزنه مصنوعی معکوس(ISAR)؛ اهداف چندگانه؛ SDAP؛ کلاتر دریا | ||
| عنوان مقاله [English] | ||
| An Efficient Mehod for Low Altitude Multi-Target Imaging in Presence of Sea Clutter by the Inverse Synthetic Aperture Radar (ISAR) | ||
| نویسندگان [English] | ||
| Ali Jabar Rashidi1؛ Reza Mohammadi2 | ||
| 1Associate Professor, Malek Ashtar University of Technology, Tehran, Iran | ||
| 2M.Sc., Malek Ashtar University of Technology, Tehran, Iran | ||
| چکیده [English] | ||
| Multi-target ISAR imaging is one of the most important and challenging issues in radar imaging. Detection and imaging of targets at low altitudes and near the surface due to the presence of surface clutter is also a research topic in this area. If air targets are flying at low altitudes in the sea environment, the sea clutter will greatly affect the radar signals received from these targets and this problem will increase the difficulty of ISAR imaging. In this paper, that extracted from the results of a research project, an efficient algorithm is presented that can perform ISAR imaging of multiple targets at low altitude in the presence of sea clutter, despite the aforementioned challenges. In this algorithm, called target grouping, based on space-doppler adaptive processing (SDAP), the effect of sea clutter is reduced and then by compensating the translational motion of each target group, its image is formed. Target clustering (grouping) based on the similarity of translational motion parameters and image formation of each group in a frame are the most important parts of this algorithm. The results of software implementation and simulation show that it is possible to effectively reduce the effect of sea clutter using the SDAP method and successfully form a group image of targets in clusters with the proposed algorithm. This result is very important in practical issues such as detection and identification of air targets in the sea environment. | ||
| کلیدواژه‌ها [English] | ||
| Inverse Synthetic Aperture Radar (ISAR), Multi-Target, SDAP, Sea Clutter | ||
| مراجع | ||
|
[1] G. G. Choi, S. H. Park, H. T. Kim, and K. T. Kim, “ISAR imaging of multiple targets based on particle swarm optimization and hough transform,” J. Electromagn. Waves Appl., vol. 23, no. 14–15, pp. 1825–1834, 2009.## [2] X. Dong, Y. Zhang, X. Gu, and W. Zhai, “ISAR imaging of multiple targets based on sparse representations,” in 2015 IEEE International Conference on Microwaves, Communications, Antennas and Electronic Systems (COMCAS), pp. 1–4, 2015.## [3] Shi Jun, Zhang Xiaoling, and Huang Shuwei, “Multi-target ISAR imaging method,” in Proceedings. 2005 IEEE International Geoscience and Remote Sensing Symposium, 2005. IGARSS ’05., vol. 7, pp. 4745–4748, 2005.## [4] S.-H. Park, H.-T. Kim, and K.-T. Kim, “Segmentation of ISAR images of targets moving in formation,” IEEE Trans. Geosci. Remote Sens., vol. 48, no. 4, pp. 2099–2108, 2009.## [5] J.-H. Jung, K.-T. Kim, S.-H. Kim, and S.-H. Park, “An efficient ISAR imaging method for multiple targets,” Prog. Electromagn. Res., vol. 146, pp. 133–142, 2014.## [6] L. Liu, F. Zhou, M. Tao, and Z. Zhang, “A novel method for multi-targets ISAR imaging based on particle swarm optimization and modified CLEAN technique,” IEEE Sens. J., vol. 16, no. 1, pp. 97–108, 2015.## [7] D. Xiao, F. Su, and J. Wu, “A method of ISAR imaging for multiple targets,” in 2012 IEEE 11th International Conference on Signal Processing, 2012, vol. 3, pp. 2011–2015.## [8] D. Xiao, F. Su, and J. Wu, “Multi-target ISAR imaging based on image segmentation and short-time Fourier transform,” in 2012 5th international congress on image and signal processing, pp. 1832–1836, 2012.## [9] J. Zhao, Y.-Q. Zhang, X. Wang, S. Wang, and F. Shang, “A novel method for ISAR imaging of multiple maneuvering targets,” Prog. Electromagn. Res., vol. 81, pp. 43–54, 2019.## [10] J. Chen, H. Xiao, H. Fan, and Z. Song, “ISAR imaging of multiple moving targets using signals separation,” in Proceedings 2013 International Conference on Mechatronic Sciences, Electric Engineering and Computer (MEC), pp. 1156–1159, 2013.## [11] J. Chen, H. Xiao, Z. Song, and H. Fan, “Simultaneous ISAR imaging of group targets flying in formation,” Chinese J. Aeronaut., vol. 27, no. 6, pp. 1554–1561, 2014.## [12] C. Jian-Wen, J. Ke-Peng, and W. Jun, “ISAR imaging of multiple targets based on FrFT-CLEAN,”, 2009 International Conference on Microwave Technology and Computational Electromagnetics, Beijing, pp. 137-140, 2009.## [13] Y. Li, Y. Fu, X. Li, and L. Le-Wei, “ISAR imaging of multiple targets using particle imaging of multiple targets using particle swarm optimisation-adaptive joint time frequency approach,” IET Signal Process., vol. 4, no. 4, pp. 343–351, 2010.## [14] Y. Li, Y. Jia, Y. Fu, and X. Li, “Multiple moving targets ISAR imaging based on discrete match Fourier transformation,” in 2008 International Conference on Radar, pp. 48–53, 2008.## [15] F. Totir, E. Radoi, L. Anton, C. Ioana, A. Serbanescu, and S. Stankovic, “Advanced sea clutter models and their usefulness for target detection,” MTA Rev., vol. 18, no. 3, pp. 257–272, 2008.## [16] X. Hua, Y. Cheng, Y. Li, Y. Shi, H. Wang, and Y. Qin, “Target detection in sea clutter via weighted averaging filter on the Riemannian manifold,” Aerosp. Sci. Technol., vol. 70, pp. 47–54, 2017.## [17] A. Jayaprakash, G. R. Reddy, and N. Prasad, “Small target detection within sea clutter based on fractal analysis,” Procedia Technol., vol. 24, pp. 988–995, 2016.## [18] J. Liu, H. Meng, H. Zhang, and X. Wang, “Radar sea clutter suppression and target indication with a spatial tracking filter,” Tsinghua Sci. Technol., vol. 15, no. 2, pp. 228–234, 2010.## [19] X. Shen, Z. Song, Y. Zhu, and Q. Fu, “Fractal detector design and application in maritime target detection,” J. Syst. Eng. Electron., vol. 28, no. 1, pp. 27–35, 2017.## [20] J. Wang and X. Xu, “Simulation of correlated low-grazing-angle sea clutter based on phase retrieval,” IEEE Trans. Geosci. Remote Sens., vol. 53, no. 7, pp. 3917–3930, 2015.## [21] S. Bocquet, “Simulation of correlated Pareto distributed sea clutter,” in 2013 International Conference on Radar, pp. 258–261, 2013.## [22] J. Wang and X. Xu, “Simulation of Pareto distributed temporally and spatially correlated low grazing angle sea clutter,” in 2014 International Radar Conference, pp. 1–6, 2014.## [23] Y. Dong, L. Rosenberg, and G. V Weinberg, “Generating correlated gamma sequences for sea-clutter simulation,” 2012.## [24] A. M. Raynal and A. W. Doerry, “Doppler characteristics of sea clutter,” 2010.## [25] M. I. Skolnik, Introduction to radar systems, 3rd ed. New York: McGraw-Hill Education, 2001.## [26] K. Ward, R. Tough, and S. Watts, Sea Clutter: Scattering, the K Distribution and Radar Performance. Institution of Engineering and Technology, 2013.## [27] V. C. Chen, Inverse Synthetic Aperture Radar Imaging; Principles. Institution of Engineering and Technology, 2014.## [28] MathWorks, “Particle Swarm Optimization.” https://www.mathworks.com/help/gads/particleswarm.html.## [29] E. Weisstein, “Kronecker Product.” http://mathworld.wolfram.com/KroneckerProduct.html.## [30] M. Mozaffari and S. Samadi, “Sampling Rate Reduction and System Performance Improvement of FMCW Radar Using Dual Compressed Sensing Technique,” Radar, vol. 4, pp. 39–53, 2016.(In Persian)## [31] A. Bacci, D. Gray, M. Martorella, and F. Berizzi, “Space-Doppler processing for multichannel ISAR imaging of non-cooperative targets embedded in strong clutter,” in 2013 International Conference on Radar, pp. 43–47, 2013.## [32] S. Kemkemian, J.-F. Degurse, V. Corretja, and R. Cottron, “Sea clutter modelling for space-time processing,” in 2016 17th International Radar Symposium (IRS), pp. 1–6, 2016## [33] MathWorks, “CoSaMP and OMP for sparse recovery.” https://www.mathworks.com/matlabcentral/fileexchange/32402-cosamp-and-omp-for-sparse-recovery.## [34] D. Needell and J. A. Tropp, “CoSaMP: Iterative signal recovery from incomplete and inaccurate samples,” Appl. Comput. Harmon. Anal., vol. 26, no. 3, pp. 301–321, 2009##
| ||
|
آمار تعداد مشاهده مقاله: 760 تعداد دریافت فایل اصل مقاله: 415 |
||