Deep Learning for Pulse Repetition Interval Classification

Ha P. K. Nguyen, Ha Q. Nguyen, Dat Ngo

2019

Abstract

Pulse Repetition Intervals (PRI)—the distances between consecutive times of arrival of radar pulses—is an important characteristic of the radar emitting source. The recognition of various PRI modulation types is therefore a key task of an Electronic Support Measure (ESM) system for accurate identification of threat emitters. This problem is challenging due to the missing and spurious pulses. In this paper, we introduce a deep-learning-based method for the classification of 7 popular PRI modulation types. In this approach, a convolutional neural network (CNN) is proposed as the classifier. Our method works well with raw input PRI sequences and, thus, gets rid of all preprocessing steps such as noise mitigation, feature extraction, and threshold setting, as required in previous approaches. Extensive simulations demonstrate that the proposed scheme outperforms existing methods by a significant margin over a variety of PRI parameters, especially in severely noisy conditions.

Paper Citation

in Harvard Style

Nguyen H., Nguyen H. and Ngo D. (2019). Deep Learning for Pulse Repetition Interval Classification.In Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-351-3, pages 313-319. DOI: 10.5220/0007253203130319

in Bibtex Style

@conference{icpram19,
author={Ha P. K. Nguyen and Ha Q. Nguyen and Dat Ngo},
title={Deep Learning for Pulse Repetition Interval Classification},
booktitle={Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2019},
pages={313-319},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007253203130319},
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 Pulse Repetition Interval Classification
SN - 978-989-758-351-3
AU - Nguyen H.
AU - Nguyen H.
AU - Ngo D.
PY - 2019
SP - 313
EP - 319
DO - 10.5220/0007253203130319