Characterizing the Effects of RF Eventization on Barker Coded Radar Signal Discrimination

This work supports development of an envisioned “RF Event Radio” capability by extending previous communication signal demonstrations into the radar arena. Promising methods in recent communications-based RF eventization works are adopted here and adapted for radar signal demonstration. As a matter of convenience, 13-bit Barker coded radar signals from collection archives are used here for demonstration. Multiple Discriminant Analysis (MDA) and Random Forest (RndF) classifiers are used to discriminate four different radar signal channels. Emphasis is on RndF discrimination performance using non-eventized and eventized fingerprint features generated from pulse two-dimensional Gabor transform (2D-GTX) responses. Resultant classification performance losses <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\%\mathrm{C}_{\Delta})$</tex> due to eventization span <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$-5.26 \%&lt;\% \mathrm{C}_{\Delta}&lt;+0.02 \%$</tex> using low, medium and high frequency resolution GTX responses. As with previous communication signal eventization, it is expected that radar signal RF eventization will benefit from using more robust convolutional (CNN) and spiking (SNN) neural network classifiers. The use of these classifiers is expected to reduce radar <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\% \mathrm{C}_{\Delta}$</tex> eventization losses that will ultimately be traded-off as potential 1000X improvements are realized in neuromorphic processing systems.