Work

Deep Neural Networks for Radar Waveform Classification

Year: 2021

Type: conference-paper

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 current state-of-the-art.

Source: 2018 52nd Asilomar Conference on Signals, Systems, and Computers

DOI: 10.1109/ieeeconf53345.2021.9723392

Authors: Michael Wharton, Anne M. Pavy, Philip Schniter

Institutions: United States Air Force Research Laboratory, Wright-Patterson Air Force Base, The Ohio State University

Language: English

Topic: Radar Systems and Signal Processing

Subfield: Aerospace Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W4214867320

FWCI: 1.265

Cites: 11

Cited by: 4

Related to: 10