Ambiguity-based classification of phase modulated radar waveforms
Anthony Buchenroth, Brian D. Rigling, Vasu D. Chakravarthy · 2016
Accurate classification or recognition of phase modulated radar waveforms, typically accomplished via the combination of pulse parameter estimates and matched filtering, poses a simple problem in ideal conditions. In less than ideal conditions, carrier frequency, time offset, pulse amplitude, initial phase, and bandwidth are unknown to the electronic warfare (EW) receiver rendering the application of a matched filter futile. Recognition of these waveforms is critical in various spectrum management, surveillance, and EW applications. This effort investigates the use of features extracted from the ambiguity function (AF) of an intercepted pulse. Specifically, this effort will expand upon the methodology of previous work done which uses the autocorrelation as a basis for extracting features. To test the efficacy of this work, extensive Monte Carlo testing employed. Simulation results prove that the methodology implemented herein achieves an overall correct classification rate of about 90% at a signal-to-noise ratio (SNR) of -2 dB on data similar to the training data.