Advanced Signal Analysis Techniques for Use on Non-Periodic Radio Frequency Signals

This proposal describes the development of an analog-to-information (A2I) system capable of detecting and classifying non-periodic radio frequency (RF) signals such as noise radars, ultra wide band (UWB) radars, and low probability of detection/intercept (LPD/LPI) waveforms. The proposed system leverages advanced algorithms for non-periodic signal classification that were originally developed for marine mammal classification, and improves performance through the use of deep neural networks (DNNs). Phase-I is structured into three sub-phases: the Baseline Phase, the Mark-1 Phase, and the Mark-2 Phase . Preliminary results obtained by applying a developmental algorithm to a real-world data set containing non-periodic signals suggests that the proposed approach is capable of achieving state-of-the-art performance as measured by validation accuracy.