1D Convolutional Neural Networks Versus Automatic Classifiers for Known LPI Radar Signals under White Gaussian Noise
المؤلف | Yildirim A. |
المؤلف | Kiranyaz, Mustafa Serkan |
تاريخ الإتاحة | 2022-04-26T12:31:21Z |
تاريخ النشر | 2020 |
اسم المنشور | IEEE Access |
المصدر | Scopus |
المعرّف | http://dx.doi.org/10.1109/ACCESS.2020.3027472 |
الملخص | 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. |
اللغة | en |
الناشر | Institute of Electrical and Electronics Engineers Inc. |
الموضوع | Convolution Convolutional neural networks Higher order statistics Military electronic countermeasures Radar signal processing Signal to noise ratio White noise Additive White Gaussian noise Classification performance Electronic support measure systems Extensive simulations Low probability of intercept Minimum distance classifiers Radar signal classifications Signal classification Gaussian noise (electronic) |
النوع | Article |
الصفحات | 180534-180543 |
رقم المجلد | 8 |
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