Learning of Doppler Tolerant Radar Detectors for Noise Waveforms
Document Type
Conference Proceeding
Publication Date
1-1-2023
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.
Identifier
85154071629 (Scopus)
ISBN
[9781665451819]
Publication Title
2023 57th Annual Conference on Information Sciences and Systems Ciss 2023
External Full Text Location
https://doi.org/10.1109/CISS56502.2023.10089751
Recommended Citation
Wensell, Kyle P.; Zhou, James; Haimovich, Alexander M.; Young, Evan A.; and Vo, Lam T., "Learning of Doppler Tolerant Radar Detectors for Noise Waveforms" (2023). Faculty Publications. 2101.
https://digitalcommons.njit.edu/fac_pubs/2101
