"Characterizing the Effects of RF Eventization on Barker Coded Radar Si" by Dillon M. M. Falkinburg, Michael A. Temple et al. 10.1109/RADAR52380.2025.11031545">
 

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

Document Type

Conference Proceeding

Publication Date

5-3-2025

Abstract

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 (%CΔ) due to eventization span −5.26%< %CΔ< +0.02% 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 %CΔ eventization losses that will ultimately be traded-off as potential 1000X improvements are realized in neuromorphic processing systems

Comments

This conference paper is published by IEEE, and is available via subscription or purchase at the DOI link below.

Paper approved for public release, Case Number 88ABW-2024-0855.

Source Publication

2025 IEEE International Radar Conference (RADAR)

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