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پردازش داده‌های راداری با استفاده از ترکیب روش‌های تجزیه‌وتحلیل مؤلفه اصلی و شبکه‌های عصبی خودسازمانده و رقمی‌ساز بردار یادگیر | ||
| پدافند الکترونیکی و سایبری | ||
| دوره 9، شماره 2 - شماره پیاپی 34، تیر 1400، صفحه 1-7 اصل مقاله (783.03 K) | ||
| نوع مقاله: مقاله پژوهشی | ||
| نویسندگان | ||
| سعید طلعتی1؛ محمد رضا حسنی آهنگر* 2 | ||
| 1دانشگاه علوم و فنون هوایی شهید ستاری | ||
| 2دانشیار، ریاست دانشگاه جامع امام حسین | ||
| تاریخ دریافت: 02 مهر 1398، تاریخ بازنگری: 15 آبان 1399، تاریخ پذیرش: 06 بهمن 1399 | ||
| چکیده | ||
| در سیستم‌های مخابراتی نظامی تکنیک‌های پیشرفته‌ای برای شنود و پردازش سیگنال‌های بلادرنگ بکار می‌رود که برای تصمیم‌گیری‌های مربوط به عملیات جنگ الکترونیک و سایر عملیات تاکتیکی حیاتی‌اند. امروزه ضرورت سیستم‌های هوشمند با تکنیک‌های پردازش سیگنال مدرن، به‌خوبی احساس می‌شود. وظیفه اصلی چنین سیستم‌هایی شناخت رادارهای موجود در محیط عملیاتی و طبقه‌بندی آن‌ها بر اساس آموخته‌های قبلی سیستم و انجام عملیات لازمه با سرعت‌بالا و بلادرنگ است بخصوص در مواردی که سیگنال دریافت شده مربوط به یک تهدید آنی مانند موشک است و باید سیستم‌های جنگ الکترونیک در کوتاه‌ترین زمان ممکن پاسخ لازم را به‌عنوان هشداردهنده بدهند. هدف‌هایی که به دنبال آن هستیم استفاده از نتایج این تحقیق در کلاسه‌بندی اطلاعات استخراج‌شده توسط سیستم‌های شنود راداری است که این امر بعد از مراحل انتخاب سیگنال ورودی و انتخاب صحیح الگوریتم‌های دسته‌بندی، محقق می‌شود و دیگری افزایش سرعت با استفاده از روش رقمی‌ساز بردار یادگیر است در این مقاله با استفاده از ﺷﺒﮑﻪهای ﻋﺼﺒﯽ رقمی‌ساز بردار یادگیر و خود سازمانده ﯾﮏ روش ﮐﺎرا ﺑﺮای ﮐﻼسﺑﻨﺪی داده‌ها اراﺋـﻪ نموده‌ایم. در اﯾﻦ روش اﺑﺘﺪا از اﻟﮕﻮرﯾﺘﻢ شبکه عصبی خود سازمانده ﺑﺮای ﯾﺎﻓﺘﻦ کدﻫﺎی موردنیاز اﺳﺘﻔﺎده کرده و ﺳـﭙﺲ در ﻣﺮﺣﻠـﻪ ﺑﻌـﺪ از اﻟﮕـﻮرﯾﺘﻢ رقمی‌ساز بردار یادگیر ﺑﺮای ﮐﻼسﺑﻨﺪی دادهﻫﺎ استفاده‌شده است. ﻫﻤﭽﻨﯿﻦ در اﯾﻦ ﻣﻘﺎﻟﻪ ﺑﻪ ﺑﺮرﺳﯽ ﺗﺄﺛﯿﺮ ﻣﻌﯿﺎر ﻓﺎﺻﻠﻪ ﺑـﯿﻦ داده‌ها ﺧـﻮاﻫﯿﻢ ﭘﺮداﺧﺖ. ﻧﺘﺎﯾﺞ ﺑﺪﺳﺖ آﻣﺪه از اﺟﺮای اﻟﮕﻮرﯾﺘﻢ ﭘﯿﺸـﻨﻬﺎدی ﺑـﺮ روی دیتاست‌های اﺳـﺘﺎﻧﺪارد جهانی فرماندهی و کنترل و ﻣﻘﺎﯾﺴـﻪ آن ﺑـﺎ ﺑﺮﺧـﯽ از روشﻫﺎی ﻣﺘﺪاول ﮐﻼسﺑﻨﺪی، پرداخته‌ایم که نشان می‌دهد ترکیب این اﻟﮕﻮرﯾﺘﻢها ﮐﺎراﯾﯽ بسیار بالایی داشته و مناسب ﺑﺮای ﻣﺴﺌﻠﻪ ﮐﻼسﺑﻨﺪی است. | ||
| کلیدواژه‌ها | ||
| پردازش رادار؛ تجزیه و تحلیل مؤلفه اصلی؛ شبکه عصبی رقمی‌ساز بردار یادگیر؛ شبکه عصبی خود سازمانده | ||
| عنوان مقاله [English] | ||
| Radar data processing using a combination of principal component analysis methods and self-organizing and digitized neural networks of the learning vector | ||
| نویسندگان [English] | ||
| S. Talati1؛ M. R. Hasani Ahangar2 | ||
| 1Faculty of Electronic Warfare Engineering, Shahid Sattari University of aeronautical Science and Technology | ||
| 2Associate Professor,Imam Hossein University | ||
| چکیده [English] | ||
| In military telecommunication systems, advanced techniques are used to intercept and process real-time signals that are critical to decisions related to electronic warfare and other tactical operations. Today, the need for intelligent systems with modern signal processing techniques is well felt. The main task of such systems is to identify the radars in the operating environment and classify them based on the previous learning of the system and perform the necessary operations at high speed and in real time, especially in cases where the received signal is related to an instantaneous threat such as missiles and electronic warfare systems. They may respond as a warning.The purpose of this study are to use the results of this research in classifying the information extracted by radar listening systems, which is achieved after the steps of selecting the input signal and selecting the correct classification algorithms, and another is to increase the speed using the vector vector digitization method. In this article, we present the data-driven methods of data collection using 4-digit vector learners and self-organizing methods.In this paper, we use learning vector quantization and self-organizing map methods to correlate the data. In this method, the neural network algorithm is first organized for the required coding positions, and in the next step, the quantization vector learning algorithm is created for data retrieval. In this article, we will also consider each database benchmark. The results obtained from the implementation of ordinary humanitarian command-and-control global standard deviation practices have been discussed in the light of the usual restraint methods, which demonstrate the great capability of these concepts. | ||
| کلیدواژه‌ها [English] | ||
| Radar Processing, PCA, LVQ, SOM | ||
| مراجع | ||
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[18] V. Vapnik, “The Nature of Statistical Learning Theory,” Springer Science & Business Media, p. 314, 2000.## [19] R. G. Wiley, “Electronic Intelligence: The Analysis of Radar Signals,” Artech House, p. 337, 1993.## [20] G. Bhattacharya, Gh. Koushik, and A. S. Chowdhury, “An affinity-based new local distance function and similarity measure for kNN algorithm,” Pattern Recognition Letters, vol. 33.3, pp. 356-363, 2012.## [21] S. Hashemi, Sh. Barati, S. Talati, and H. Noori, “A genetic algorithm approach to optimal placement of switching and protective equipment on a distribution network,” Journal of Engineering and Applied Sciences, vol. 11, pp. 1395-1400, 2016.## [22] S. Hashemi, M. Abyari, Sh. Barati, S. Tahmasebi, and S. Talati, “A proposed method to controller parameter soft tuning as accommodation FTC after unknown input observer FDI,” Journal of Engineering and Applied Sciences, vol. 11, pp. 2818-2829, 2016.## [23] O. Sharifi-Tehrani and S. Talati, “PPU Adaptive LMS Algorithm, a Hardware-Efficient Approach, a Review on,” Majlesi Journal of Mechatronic Systems, vol. 6, no. 1, 2017.## [24] S. Talati, A. Rahmati, and H. Heidari, “Investigating the Effect of Voltage Controlled Oscillator Delay on the Stability of Phase Lock Loops,” MJTD, vol. 8, no. 2, pp. 57-61, 2019.##
[25] S. Talati, B. Ebadi, and H. Akbarzade, “Determining of the fault location in distribution systems in presence of distributed generation resources using the original post phasors,” QUID 2017, Special Issue No.1- ISSN: 1692-343X, Medellín-Colombia, pp. 1806-1812, April 2017.## [26] S. Talati and M. R. Hasani Ahangar, “Analysis, Simulation and Optimization of LVQ Neural Network Algorithm and Comparison with SOM,” MJTD, vol. 10, no. 1, 2020.## [27] S. Talati and P. Etezadifar, “Providing an Optimal Way to Increase the Security of Data Transfer Using Watermarking in Digital Audio Signals,” MJTD, vol. 10, no. 1, 2020.## [28] S. Talati and M. R. Hasani Ahangar, “Combining Principal Component Analysis Methods and Self-Organized and Vector Learning Neural Networks for Radar Data,” Majlesi Journal of Telecommunication Devices, vol. 9(2), pp. 65-69, 2020.## [29] M. R. Hasani Ahangar, S. Talati, A. Rahmati, and H. Heidari, “The Use of Electronic Warfare and Information Signaling in Network-based Warfare,” Majlesi Journal of Telecommunication Devices, vol. 9(2), pp. 93-97, 2020.## [30] S. Talati and P. Etezadifar, “Providing an Optimal Way to Increase the Security of Data Transfer Using Watermarking in Digital Audio Signals,” MJTD, vol. 10, no. 1, 2020.## [31] M. Aslinezhad, O. Mahmoudi, and S. Talati, “Blind Detection of Channel Parameters Using Combination of the Gaussian Elimination and Interleaving,” Majlesi Journal of Mechatronic Systems, vol. 9(4), pp. 59-67, 2020.## [32] S. Talati and A. Amjadi, “Design and Simulation of a Novel Photonic Crystal Fiber with a Low Dispersion Coefficient in the Terahertz Band,” Majlesi Journal of Mechatronic Systems, vol. 9(2), pp. 23-28, 2020.## [33] S. Talati and S. M. Alavi, “Radar Systems Deception using Cross-eye Technique,” Majlesi Journal of Mechatronic Systems, vol. 9(3), pp. 19-21, 2020.## | ||
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آمار تعداد مشاهده مقاله: 819 تعداد دریافت فایل اصل مقاله: 529 |
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