Deep Neural Network Design for Radar Applications

Saved in:
Bibliographic Details
Imprint:Stevenage : SciTech Publishing Inc., 2020.
Description:1 online resource (420 p.)
Series:Radar, Sonar & Navigation
Radar, Sonar & Navigation.
Doppler radar.
Machine learning.
Signal processing -- Digital techniques -- Data processing.
Synthetic aperture radar.
Target acquisition.
convolutional neural nets.
data structures.
Doppler radar.
learning (artificial intelligence).
passive radar.
radar applications.
radar computing.
radar imaging.
radar target recognition.
remote sensing by radar.
sensor fusion.
synthetic aperture radar.
Format: E-Resource Book
URL for this record:
Hidden Bibliographic Details
Other authors / contributors:Gurbuz, Sevgi Zubeyde, ed.
Summary:The book has 11 chapters including a Prologue: perspectives on deep learning of RF data and an Epilogue: looking toward the future; and is divided into 3 parts. The first part deals with Fundamentals and covers the following topics: Radar systems, signals, and phenomenology; Basic principles of machine learning; and Theoretical foundations of deep learning. The second part covers Special topics and following topics are dealt with: Radar data representation for classification of activities of daily living; Challenges in training DNNs for classification of radar micro-Doppler signatures; and Machine learning techniques for SAR data augmentation. The third part deals with Applications and covers the following topics: Classifying micro-Doppler signatures using deep convolutional neural networks; Deep neural network design for SAR/ISAR-based automatic target recognition; Deep learning for passive synthetic aperture radar imaging; Fusion of deep representations in multistatic radar networks; and Application of deep learning to radar remote sensing.
Standard no.:10.1049/SBRA529E