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رسائل ماجيستير الانجليزية 2018 f29c54c7-1646-449b-adce-27e72f3b7277

Feature Extraction for Moving Targets Classification Using Radar Doppler Echoes

Naoufel Werghi , Esra Al Hadhrami

جامعة خليفة · الامارات

الموضوعات

علوم تطبيقية وتكنولوجية

الملخص

Automatic target recognition (ATR) is one of the most demanding tasks in modern radar systems, which is one of the most challenging to implement. The efficiency of knowledge-based recognition or classification depends highly on the training dataset, the superiority of the extracted features as well as the classifier's performance. Most of the efforts in the radar's target classification focused on the Micro-Doppler signatures as they show distinctive characteristics of different targets. Micro-Doppler signatures are created by the frequency shifts introduced by the target's movement and captured by Doppler radar. Micro-Doppler signatures can be observed in Spectrogram, a time-frequency analysis that illustrates the time varying Micro-Doppler shifts. Convolutional Neural Networks (CNNs) are the state-of-the-art when it comes to image vision tasks as they proved to achieve an outstanding performance. In this thesis, we are utilizing CNNs in a transfer learning approach to classify ground moving targets of Pulsed-Doppler radar. Pre-trained CNNs were used as feature extractors to extract feature automatically from spectrograms, rather than depending on hand-crafted features. Three different pre-trained CNNs modules were exploited, namely, AlexNet, VGG16 and VGG19, as feature extractors whereas the output features were used to train a classifier. Two classifiers were experimented: a multiclass support vector machine (SVM) and a softmax. To validate our approach, we used RadEch database of 8 ground moving target classes. Our approach outperformed the state-of-the-art methods, using the same database, with an accuracy of 99.9%. Indexing Terms: Micro-Doppler, Radar classification, Automatic target recognition, Convolutional Neural Network, Transfer learning, AlexNet, VGG.

التعريف والنوع

رقم الوثيقة
f29c54c7-1646-449b-adce-27e72f3b7277
رقم العقد
0
نوع الوسائط
Crawler
نوع المحتوى
الرسائل العلمية
صيغة المصدر
رسائل ماجيستير
نوع الملف
pdf text
أسماء الملفات
2283290_1.pdf

بيانات النشر

ألقاب المؤلفين
[{"name_ar":" Naoufel Werghi ","title_ar":"اشراف","title_en":"Supervision"},{"name_ar":"Esra Al Hadhrami","title_ar":"اعداد","title_en":"Preparation"}]
اللغة
English

المصدر والدورية

اسم المصدر
Feature Extraction for Moving Targets Classification Using Radar Doppler Echoes

المحتوى والصفحات

عدد الصفحات
0
كلمات الباحثين
Micro-Doppler, Radar classification, Automatic target recognition, Convolutional Neural Network, Transfer learning, AlexNet,VGG.

إشراف وإعداد

الإشراف
Naoufel Werghi
الإعداد
Esra Al Hadhrami

الاقتباسات الببليوغرافية

APA

Naoufel Werghi و Esra Al Hadhrami. (2018). Feature Extraction for Moving Targets Classification Using Radar Doppler Echoes. أطروحة(رسائل ماجيستير). جامعة خليفة. الامارات .

MLA

Naoufel Werghi و Esra Al Hadhrami. Feature Extraction for Moving Targets Classification Using Radar Doppler Echoes. 2018. جامعة خليفة، رسائل ماجيستير.