22 - 26 April 2019
Westin Waterfront Hotel
Boston, Massachusetts USA

Tutorial: Machine Learning Techniques for Radar ATR

22 April Monday Morning Session 11:00 AM – 3:00 PM


Dr. Uttam Majumder
Air Force Research Laboratory

Dr. Erik Blasch
Air Force Research Laboratory

Prof. David Garren
U.S. Naval Postgraduate School

The focus of this short 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 present an overview RF ATR research in the past (i.e., template-based approach conducted under DARPA MSTAR (Moving and Stationary Target Acquisition and Recognition) program and limitations of this approach.  Then will provide an overview of various machine learning (ML) theories.  Finally, we will demonstrate implementations and performance analysis of DL-based ATR on SAR data. We will present hands-on implementation of DL-based radar object classification using PyTorch and 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 Networks (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.