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Deep Learning for Radio Frequency Automatic Target Recognition

Deep Learning for Radio Frequency Automatic Target Recognition

Copyright: 2020
ISBN: 9781630816377
Coming Soon: Available 07/31/2020
List Price: $179.00

New Release Discount Price
Hardback $134.00 Qty:

This exciting resource identifies technical challenges, benefits, and directions of Deep Learning (DL) based object classification using radar data (i.e., Synthetic Aperture Radar / SAR and High range resolution Radar / HRR data). An overview of machine learning (ML) theory to include a history, background primer, and example and performance of ML algorithm (i.e., DL method) on video imagery is provided. Radar data with issues of collection, application, and examples for SAR/HRR data and communication signals analysis is also discussed. Practical considerations of deploying such techniques, including performance evaluation, hardware issues, and the future unresolved issues are presented.

Introduction; Mathematical foundations for ML; A Review of various ML algorithms; Radio Frequency Data as Big Data; ML Algorithms on RF Data; ML algorithms Implementation for Radar Image Classification; ML algorithms Implementation for communication signal classification; Computational advancements for ML algorithms: Neuromorphic processor, Memristor, GPUs, FPGA; Emerging Research Topics.

  • 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
  • David A. Garren

    is an associate 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.

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