Gaylord Palms Resort & Convention Center
Orlando, Florida, United States
15 - 19 April 2018
Short Course (SC1245)
Machine Learning Techniques for Radio Frequency Object Classification
Wednesday 18 April 2018
1:30 PM - 5:30 PM

FormatShort Course
Member Price $390.00
Non-Member Price $445.00
Student Member Price $253.00
  • Course Level:
  • Intermediate
  • CEU:
  • 0.4
The focus of this course will be recent research results, technical challenges, and directions of Deep Learning (DL) based object classification using radar data (i.e., Synthetic Aperture Radar / SAR data). First, we will provide a short overview of machine learning (ML) theory. Then we will provide an example and performance of ML algorithm (i.e., DL method) on video imagery. Finally, we will demonstrate algorithmic implementation and performance of DL algorithms on SAR data (a significant portion of the course time). It is evident that significant research efforts have been devoted to applying DL algorithms on video imagery. However, very limited literature can be found on technical challenges and approaches to execute DL algorithms on radio frequency (RF) data. We will present hands-on implementation of DL-based radar object classification using Caffe and/or TensorFlow tools. Unlike passive sensing (i.e., video collections), Radar enables imaging ground objects at far greater standoff distances and all-weather conditions. Existing non-DL based RF object recognition algorithms are less accurate and require impractically large computing resources. With adequate training data, DL enables more accurate, near real-time, and low-power object recognition system development. We will highlight implementations of DL-based (i.e., Convolution Neural Network (CNN)) SAR object recognition algorithms in graphical processing units (GPUs) and energy efficient computing systems. The examples presented will demonstrate acceptable classification accuracy on relevant SAR data. Further, we will discuss special topics of interest on DL-based RF object recognition as requested by the researchers, practitioners, and students.
Learning Outcomes
  • identify object features in radar imagery.
  • construct a machine learning system to detect and classify objects from radar imagery.
  • compare technical challenges involving radar and video image classification.
  • differentiate benefits of DL-based RF object classification as compared to existing algorithms.
  • identify software tools and data applicable to their research interests.
Engineers and researchers interested in applying Deep Learning on radar or electro-optical data for developing object recognition or self-driving car or autonomous/expert systems. Some understanding on machine learning will be useful but not a requirement.
About the
Uttam Kumar Majumder is a senior electronics engineer at Air Force Research Laboratory (AFRL). He has earned Ph.D. degree (in Electrical Engineering) from Purdue University, West Lafayette, Indiana. His research interest includes Machine Learning for object recognition, High Performance Computing, Synthetic Aperture Radar (SAR) algorithm development for surveillance applications, Radar Waveforms Design and Digital Image Processing. He has presented a SAR tutorial at IEEE radar conference.
Erik Blasch is a principal scientist at AFRL researching information fusion evaluation, image fusion, and pattern recognition. He is an SPIE fellow and has supported multiple tutorials.
Back to Top