مقاله انگلیسی رایگان در مورد تشخیص هدف رادار روزنه مصنوعی – IEEE 2019
مشخصات مقاله | |
ترجمه عنوان مقاله | تشخیص هدف رادار روزنه مصنوعی (SAR) بر اساس یادگیری انتقالی دامنه متقابل و وظیفه متقابل |
عنوان انگلیسی مقاله | SAR Target Recognition Based on Cross-Domain and Cross-Task Transfer Learning |
انتشار | مقاله سال ۲۰۱۹ |
تعداد صفحات مقاله انگلیسی | ۹ صفحه |
هزینه | دانلود مقاله انگلیسی رایگان میباشد. |
پایگاه داده | نشریه IEEE |
نوع نگارش مقاله |
مقاله پژوهشی (Research Article) |
مقاله بیس | این مقاله بیس نمیباشد |
نمایه (index) | Scopus – Master Journals List – JCR |
نوع مقاله | ISI |
فرمت مقاله انگلیسی | |
ایمپکت فاکتور(IF) |
۴٫۶۴۱ در سال ۲۰۱۸ |
شاخص H_index | ۵۶ در سال ۲۰۱۹ |
شاخص SJR | ۰٫۶۰۹ در سال ۲۰۱۸ |
شناسه ISSN | ۲۱۶۹-۳۵۳۶ |
شاخص Quartile (چارک) | Q2 در سال ۲۰۱۸ |
مدل مفهومی | ندارد |
پرسشنامه | ندارد |
متغیر | ندارد |
رفرنس | دارد |
رشته های مرتبط | مهندسی برق،مهندسی کامپیوتر، مهندسی فناوری اطلاعات |
گرایش های مرتبط | برق مخابرات، شبکه های کامپیوتری |
نوع ارائه مقاله |
ژورنال |
مجله / کنفرانس | دسترسی – IEEE Access |
دانشگاه | School of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211100, China |
کلمات کلیدی | رادار روزنه مصنوعی، تشخیص هدف، شبکه عصبی پیچشی، یادگیری متا، سازگاری دامنه مخالف |
کلمات کلیدی انگلیسی | Synthetic aperture radar (SAR), target recognition, convolutional neural network (CNN), meta-learning, adversarial domain adaptation |
شناسه دیجیتال – doi |
https://doi.org/10.1109/ACCESS.2019.2948618 |
کد محصول | E13898 |
وضعیت ترجمه مقاله | ترجمه آماده این مقاله موجود نمیباشد. میتوانید از طریق دکمه پایین سفارش دهید. |
دانلود رایگان مقاله | دانلود رایگان مقاله انگلیسی |
سفارش ترجمه این مقاله | سفارش ترجمه این مقاله |
فهرست مطالب مقاله: |
Abstract I. Introduction II. Methodology III. Experimental Results IV. Conclusion Authors Figures References |
بخشی از متن مقاله: |
Abstract
Inspired by their tremendous success in optical image detection and classification, convolutional neural networks (CNNs) have recently been used in synthetic aperture radar automatic target recognition (SAR-ATR). Although CNN-based methods can achieve excellent recognition performance, it is difficult to collect a large number of real SAR images available for training. In this paper, we introduce simulated SAR data to alleviate the problem of insufficient training data. To address domain shift and task transfer problems caused by differences between simulated and real data, we propose a model that integrates meta-learning and adversarial domain adaptation. We use sufficient simulated data and a few real data to pre-train the model. After fine-tuning, the pre-trained model can quickly adapt to new tasks in real data. Extensive experimental results obtained in the real SAR dataset demonstrate that our model effectively solves the cross-domain and cross-task transfer problem. Compared with conventional SAR-ATR methods, the proposed model can achieve better recognition performance with a small amount of training data. Introduction Synthetic aperture radar (SAR) is an active sensor mounted on moving platforms such as aircraft, satellites, and spaceships. SAR provides two-dimensional high-resolution images by receiving the electromagnetic echoes of targets. Benefiting from its unique imaging mechanism, SAR can operate day and night, independent of weather conditions, and has specific surface penetration capability. The SAR system has unique advantages in many applications, ranging from disaster monitoring and resource exploration to military inspection, and it plays an unreplaceable role in both military and civilian fields. Automatic target recognition (ATR) is an essential topic in the field of SAR application research. According to different implementation methods, classic ATR methods can be classified into feature-based and model-based approaches. Feature-based methods extract discriminative features, such as binary regions [1], target contours [2], monogenic signals [3], [4], projection features [5], [6], and tensor decomposition features [7] from images. Classifiers such as K-nearest neighbor (KNN) [8], support vector machine (SVM) [9], the Bayesian classifier [10], and the sparse representation classifier [11] have been developed to classify the extracted features. Both feature extraction and classification require careful selection by experienced researchers. Model-based methods [12]–[۱۴] focus on the electromagnetic scattering features of a target, which are related to the physical characteristics of the target. |