Learning of Doppler Tolerant Radar Detectors for Noise Waveforms

This work analyzes neural network learning as it pertains to noise waveform radar detectors. The concept of noise waveform radar is explored, and the core issue of Doppler tolerance is addressed. In order for the network to successfully learn the noise waveform, a pre-processing step of phase alignment is performed on the data to allow the neural network to establish a pattern. The training data is then augmented with Dopplershifted waveforms, such that this Doppler shift appears in the phase-aligned data. We demonstrate that this pre-processing and training scheme successfully allows for the detector to learn Doppler intolerant waveforms such as the noise waveforms.