پردازش داده‌های راداری با استفاده از ترکیب روش‌های تجزیه‌وتحلیل مؤلفه اصلی و شبکه‌های عصبی خودسازمانده و رقمی‌ساز بردار یادگیر

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشگاه علوم و فنون هوایی شهید ستاری

2 دانشیار، ریاست دانشگاه جامع امام حسین

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


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. Talati 1
  • M. R. Hasani Ahangar 2
1 Faculty of Electronic Warfare Engineering, Shahid Sattari University of aeronautical Science and Technology
2 Associate 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
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دوره 9، شماره 2 - شماره پیاپی 34
شماره پیاپی 34، فصلنامه تابستان
تیر 1400
صفحه 1-7
  • تاریخ دریافت: 02 مهر 1398
  • تاریخ بازنگری: 15 آبان 1399
  • تاریخ پذیرش: 06 بهمن 1399
  • تاریخ انتشار: 01 تیر 1400