The goal of the proposed program is the development of a machine learning tool that will automatically detect errors and contamination in radar cross section (RCS) measurements at an outdoor test range.This tool will exploit recent advances in machine learning by training a deep learning neural network to extract sophisticated sets of features, which can be adapted to reveal subtle patterns in the data.These patterns are subsequently used to classify between clean sets of RCS measurements versus those containing radar interference, or other sources of error.Inverse Synthetic Aperture Radar,machine learning,Target Recognition
