نوع مقاله : مقالة‌ تحقیقی‌ (پژوهشی‌)

نویسندگان

1 دانشکده مهندسی برق و کامپیوتر ,دانشگاه شیراز,شیراز,ایران

2 استاد دانشکده مهندسی برق و کامپیوتر،دانشگاه شیراز،شیراز،ایران

چکیده

امروزه علاقه روز‌‌افزونی مبنی بر استفاده از رادار دهانه مصنوعی(SAR) در کاربرد آشکارسازی اهداف متحرک زمینی (GMTI ) و تصویر‌برداری از اهداف متحرک زمینی ( (GMTIm برای هر دو کاربرد نظامی و غیر نظامی وجود دارد.از آنجا که SAR برای تصویربرداری از صحنه ثابت طراحی شده است، تصویر SAR از هدف در حال حرکت مات و جابه‌جا می‌شود. از این‌رو برای به دست آوردن تصویر با وضوح بالا در این مقاله از یک الگوریتم جدید استفاده شده است که چارچوب آن مبتنی بر یادگیری بیزی تنک (SBL) است. برای ارزیابی کیفیت تصاویر، از نسبت هدف به کلاتر (TCR) و آنتروپی شانون استفاده شده است که معمولاً برای ارزیابی تصویر رادار دهانه مصنوعی استفاده می‌شود. الگوریتم پیشنهادی TCR تصویر را در مقایسه با روش‌های متداول در حدود 10dB افزایش و آنتروپی تصویر را به مقدار 60%کاهش می‌دهد.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

SAR Ground Moving Target Imaging based on Sparse Representation

نویسندگان [English]

  • S. Andishe Moezzi 1
  • Mohamad Ali Masnadi-Shirazi 2

1 Department of Electrical and Computer Engineering , shiraz University, shiraz, Iran

2 Professor, Department of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran

چکیده [English]

synthetic aperture radar (SAR) for ground moving target indication (GMTI) and imaging (GMTIm) have been gaining increasing interests for both civilian and military applications. Because SAR is generally designed for imaging a stationary scene, the SAR image of a moving target will be both displaced and smeared.
More specifically, by exploiting the inherent sparsity of the moving targets in the clutter-suppressed SAR image domain, in this article. the intended SAR-GMTIm problem is solve by a sparse Bayesian perspective.
The theory of CS has been successfully applied to SAR/ISAR imagery to achieve high cross-range resolution with a limited number of pulses
In order to evaluate the quality of images, we apply the target-to-clutter ratio (TCR), which is commonly used in synthetic
aperture radar (SAR) image assessment.
The proposed algorithm shows a 10-dB higher TCR compared to the conventional algorithm.

کلیدواژه‌ها [English]

  • : Synthetic Aperture Radar (SAR)
  • Ground Moving Target Indication (GMTI)
  • Ground Moving Target Imaging (GMTIm)
  • Sparse Bayesian Learning(SBL)
  • Lv’s Distribution (LVD)
  • VB-EM
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