Classifying LPI radar waveforms with time-frequency transformations using multi-stage CNN system

Name: Classifying LPI radar waveforms with time-frequency transformations using multi-stage CNN system
Author: Güven, İslam, Yağmur, İsmail Can, Karadaş, Hasan Bahadır, Parlak, Mehmet

İsim	Classifying LPI radar waveforms with time-frequency transformations using multi-stage CNN system
Yazar	Güven, İslam, Yağmur, İsmail Can, Karadaş, Hasan Bahadır, Parlak, Mehmet
Basım Tarihi:	2022
Basım Yeri	- IEEE
Konu	Convolutional neural networks, Low probability of intercept, Radar waveform classification, ResNet-50, Short-time autocorrelation, ShuffleNet, Squeezenet, Time-frequency transform, Wigner Ville
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	978-839560205-4
Kayıt Numarası	2ad99532-b050-4109-b1be-0efa3fc7f227
Lokasyon	Electrical & Electronics Engineering
Tarih	2022
Örnek Metin	As the number of radar waveforms in the cognitive electronic warfare applications increases, individual detection and classification performances of each waveform vary furthermore due to their different characteristics. To provide a supervised signal classification in an efficient framework, we propose a multi-stage waveform classification system, where multiple modular blocks are combined to classify 18 different radar waveforms. In the first stage, we transform the signals into time-frequency images (TFIs) using Fourier-based Synchrosqueezing Transform (FSST) and SqueezeNet to classify the signals into two subsets: P1-4 and others. Then, the subsets are used as inputs to two different systems. These systems use different TFI techniques such as FSST and Smoothed Pseudo Wigner Ville Distribution (SPWVD) for processing and convolutional neural network (CNN) architectures such as Squeezenet, ResNet-50, and ShuffleNet for classification. In experiments, we provide supervised classification results at different signal-to-noise ratio (SNR) levels and achieve 98.08% classification accuracy at 10-dB SNR on a diverse set of frequency and phase modulated signals.

Kaynağa git Özyeğin Üniversitesi

Aramaya Dön

Özyeğin Üniversitesi

Kaynağa git

Classifying LPI radar waveforms with time-frequency transformations using multi-stage CNN system

Yazar Güven, İslam, Yağmur, İsmail Can, Karadaş, Hasan Bahadır, Parlak, Mehmet

Basım Tarihi 2022

Basım Yeri - IEEE

Konu Convolutional neural networks, Low probability of intercept, Radar waveform classification, ResNet-50, Short-time autocorrelation, ShuffleNet, Squeezenet, Time-frequency transform, Wigner Ville

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 978-839560205-4

Kayıt Numarası 2ad99532-b050-4109-b1be-0efa3fc7f227

Lokasyon Electrical & Electronics Engineering

Tarih 2022

Örnek Metin As the number of radar waveforms in the cognitive electronic warfare applications increases, individual detection and classification performances of each waveform vary furthermore due to their different characteristics. To provide a supervised signal classification in an efficient framework, we propose a multi-stage waveform classification system, where multiple modular blocks are combined to classify 18 different radar waveforms. In the first stage, we transform the signals into time-frequency images (TFIs) using Fourier-based Synchrosqueezing Transform (FSST) and SqueezeNet to classify the signals into two subsets: P1-4 and others. Then, the subsets are used as inputs to two different systems. These systems use different TFI techniques such as FSST and Smoothed Pseudo Wigner Ville Distribution (SPWVD) for processing and convolutional neural network (CNN) architectures such as Squeezenet, ResNet-50, and ShuffleNet for classification. In experiments, we provide supervised classification results at different signal-to-noise ratio (SNR) levels and achieve 98.08% classification accuracy at 10-dB SNR on a diverse set of frequency and phase modulated signals.