Deep Learning for Radar Pulse Detection

Ha Nguyen, Dat Ngo, Van Do

Abstract

In this paper, we introduce a deep learning based framework for sequential detection of rectangular radar pulses with varying waveforms and pulse widths under a wide range of noise levels. The method is divided into two stages. In the first stage, a convolutional neural network is trained to determine whether a pulse or part of a pulse appears in a segment of the signal envelop. In the second stage, the change points in the segment are found by solving an optimization problem and then combined with previously detected edges to estimate the pulse locations. The proposed scheme is noise-blind as it does not require a noise floor estimation, unlike the threshold-based edge detection (TED) method. Simulations also show that our method significantly outperforms TED in highly noisy cases.

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Paper Citation


in Harvard Style

Nguyen H., Ngo D. and Do V. (2019). Deep Learning for Radar Pulse Detection.In Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-351-3, pages 32-39. DOI: 10.5220/0007253000320039


in Bibtex Style

@conference{icpram19,
author={Ha Nguyen and Dat Ngo and Van Do},
title={Deep Learning for Radar Pulse Detection},
booktitle={Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2019},
pages={32-39},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007253000320039},
isbn={978-989-758-351-3},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Deep Learning for Radar Pulse Detection
SN - 978-989-758-351-3
AU - Nguyen H.
AU - Ngo D.
AU - Do V.
PY - 2019
SP - 32
EP - 39
DO - 10.5220/0007253000320039