@inproceedings{e2c78d9c30724b5c93e5e19fa8811e93,
title = "Learning of Doppler Tolerant Radar Detectors for Noise Waveforms",
abstract = "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.",
keywords = "Doppler tolerance, Radar detection, neural networks, noise waveforms",
author = "Wensell, {Kyle P.} and James Zhou and Haimovich, {Alexander M.} and Young, {Evan A.} and Vo, {Lam T.}",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 57th Annual Conference on Information Sciences and Systems, CISS 2023 ; Conference date: 22-03-2023 Through 24-03-2023",
year = "2023",
doi = "10.1109/CISS56502.2023.10089751",
language = "English (US)",
series = "2023 57th Annual Conference on Information Sciences and Systems, CISS 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2023 57th Annual Conference on Information Sciences and Systems, CISS 2023",
address = "United States",
}