This authoritative resource presents a comprehensive illustration of modern Artificial Intelligence / Machine Learning (AI/ML) technology for radio frequency (RF) data exploitation.
It identifies technical challenges, benefits, and directions of deep learning (DL) based object classification using radar data, including synthetic aperture radar (SAR) and high range resolution (HRR) radar. The performance of AI/ML algorithms is provided from an overview of machine learning (ML) theory that includes history, background primer, and examples. Radar data issues of collection, application, and examples for SAR/HRR data and communication signals analysis are discussed. In addition, this book presents practical considerations of deploying such techniques, including performance evaluation, energy-efficient computing, and the future unresolved issues.
Table Of Contents
Introduction to Machine Learning and Radio Frequency: Past, Present, and Future;
Mathematical Foundations for Machine Learning; Review of Machine Learning Algorithms;
A Review of Deep Learning Algorithms; Radio Frequency Data for ML Research;
Deep Learning for Single-Target Classification in SAR Imagery;
Deep Learning for Multiple Target Classification in SAR Imagery; RF Signal Classification;
Radio Frequency ATR Performance Evaluation;
Recent Topics in Machine Learning for Radio Frequency ATR.
Erik P. Blasch
is a program officer at the United States Air Force Research Laboratory (AFRL) Air Force Office of Scientific Research (AFOSR). He received Ph.D. in electrical engineering from Wright State University. He is a Fellow of IEEE.
Uttam K. Majumder
is a senior electronics engineer at the United States Air Force Research Laboratory (AFRL). He received his Ph.D. in electrical engineering from Purdue University. He is a senior member of IEEE.
David A. Garren
is a professor at the Naval Postgraduate School. He received his Ph.D. from the College of William and Mary. He is a senior member of IEEE.