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  4. Deep neural network design for SAR/ISAR-based automatic target recognition
 
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2021
Book Article
Title

Deep neural network design for SAR/ISAR-based automatic target recognition

Abstract
The automatic recognition of targets with radar is an ongoing research field for many years already. Since 2014, a new methodology based on deep neural networks is becoming more established within this field. This chapter gives a short overview with some examples of this short history of target recognition using deep learning (DL) and some comparative results with a basic implementation of a convolutional neural network (CNN). This network is applied to the commonly used Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset and to an inverse synthetic aperture radar (ISAR) dataset measured with the Tracking and Imaging Radar (TIRA) of Fraunhofer FHR. Chapter Contents: ⢠8.1 Introduction ⢠8.2 Deep learning methods used for target recognition ⢠8.3 Datasets ⢠8.3.1 MSTAR SAR dataset ⢠8.3.2 TIRA ISAR dataset ⢠8.3.3 Artificial training data ⢠8.4 Classification system ⢠8.4.1 CNN structure ⢠8.4.2 Training algorithm ⢠8.4.2.1 Stocha stic diagonal Levenberg-Marquardt algorithm ⢠8.4.2.2 Adam algorithm ⢠8.4.3 Regularization methods ⢠8.5 Experimental results ⢠8.5.1 CNN structure ⢠8.5.2 Training method and cost function ⢠8.5.3 Regularization methods ⢠8.5.4 Artificial training data ⢠8.5.5 Use of SVMs as classifier ⢠8.6 Summary and conclusion ⢠References.
Author(s)
Wagner, S.
Brüggenwirth, S.
Mainwork
Deep Neural Network Design for Radar Applications  
DOI
10.1049/SBRA529E_ch8
Language
English
Fraunhofer-Institut für Hochfrequenzphysik und Radartechnik FHR  
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