Deep Neural Networks for Radar Waveform Classification

Michael Wharton; Anne M. Pavy; Philip Schniter

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arXiv · 2102.07827

Deep Neural Networks for Radar Waveform Classification

Michael Wharton·Anne M. Pavy·Philip Schniter

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Abstract

We consider the problem of classifying radar pulses given raw I/Q waveforms in the presence of noise and absence of synchronization. We also consider the problem of classifying multiple superimposed radar pulses. For both, we design deep neural networks (DNNs) that are robust to synchronization, pulse width, and SNR. Our designs yield more than 100x reduction in error-rate over the previous state-of-the-art.

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Michael Wharton, Anne M. Pavy, Philip Schniter. 2021-02-15. Deep Neural Networks for Radar Waveform Classification. https://arxiv.org/abs/2102.07827

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