1D Convolutional Neural Networks Versus Automatic Classifiers for Known LPI Radar Signals Under White Gaussian Noise

Alper Yildirim,Serkan Kiranyaz

doi:10.1109/access.2020.3027472

Alper Yildirim, Serkan Kiranyaz

Open Access

https://doi.org/10.1109/access.2020.3027472

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Journal: IEEE access : practical innovations, open solutions	Publication Date: Jan 1, 2020
Citations: 32	License type: CC BY 4.0

Abstract

In this study we analyze the signal classification performances of various classifiers for deterministic signals under the additive White Gaussian Noise (WGN) in a wide range of signal to noise ratio (SNR) levels (−40dB to +20dB). The traditional electronic support measure (ESM) systems require high SNR for radar signal classification. LPI (low probability of intercept) radar signals that are received by ESM systems are usually corrupted by noise. So, we demonstrate through extensive simulations that it is possible to achieve high classification performance at low SNR levels providing that the underlying radar signals are known in advance. MF bank classifier, 1D Convolutional Neural Networks (CNNs) and the minimum distance classifier using spectral-domain features (the skewness, the kurtosis, and the energy of the dominant frequency) have been derived for the radar signal classification and their performances have been compared with each other and with the optimal classifier.

Highlights

Receiver sensitivities of the electronic support measure (ESM) systems are often insufficient to detect and classify low power signals
LPI radar signals that are received by ESM systems are usually corrupted by noise; detection, feature extraction, and classification of such signals are not possible from safe distances [6]
The experimental results have shown that when the signals are assumed to be known, it is possible to achieve a high classification performance even at very low signal to noise ratio (SNR) levels by the optimal classifier (OPT)

Summary

Introduction

Receiver sensitivities of the electronic support measure (ESM) systems are often insufficient to detect and classify low power signals. The channelized receiver structures ([1]–[5]) increase the signal processing gain, ESM systems require a clean signal, i.e. a signal with high SNR. LPI radar signals that are received by ESM systems are usually corrupted by noise; detection, feature extraction, and classification of such signals are not possible from safe distances [6]. If no clear signal is received (i.e. the SNR required for a fair parameter estimation is more than 18 dB [7]), it is neither possible to extract the PDWs and nor to classify the threats.

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