Radar Waveform Recognition using Fourier-Based Synchrosqueezing Transform and CNN

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Standard

Radar Waveform Recognition using Fourier-Based Synchrosqueezing Transform and CNN. / Kong, Gyuyeol; Koivunen, Visa.

2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP) . IEEE, 2019.

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Harvard

Kong, G & Koivunen, V 2019, Radar Waveform Recognition using Fourier-Based Synchrosqueezing Transform and CNN. in 2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP) . IEEE, IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, Le Gosier, Guadeloupe, 15/12/2019.

APA

Kong, G., & Koivunen, V. (Accepted/In press). Radar Waveform Recognition using Fourier-Based Synchrosqueezing Transform and CNN. In 2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP) IEEE.

Vancouver

Kong G, Koivunen V. Radar Waveform Recognition using Fourier-Based Synchrosqueezing Transform and CNN. In 2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP) . IEEE. 2019

Author

Kong, Gyuyeol ; Koivunen, Visa. / Radar Waveform Recognition using Fourier-Based Synchrosqueezing Transform and CNN. 2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP) . IEEE, 2019.

Bibtex - Download

@inproceedings{68b007769cc145c7967f4317406081c0,
title = "Radar Waveform Recognition using Fourier-Based Synchrosqueezing Transform and CNN",
abstract = "In this paper the problem of recognizing radar waveforms is addressed. Waveform classification is needed in spectrum sharing and radar-communications coexistence, cognitive radars and signal intelligence. Different radar waveforms exhibit different properties in time-frequency domain. We propose a deep learning method for waveform classification. The received signal is processed with Fourier synchrosqueezing transform that has excellent properties in revealing timevarying behavior, rate of, strength and number of oscillatory components in received signals. The resulting time-frequency description is represented as a bivariate image that is fed into a convolutional neural network. The proposed method has superior performance over the widely used the Choi-Williams distribution (CWD) method in distinguishing the polyphase waveforms even at low signal-to-noise ratio regime.",
author = "Gyuyeol Kong and Visa Koivunen",
note = "Avaa k{\"a}sikirjoitus, kun julkaistu.",
year = "2019",
month = "9",
day = "17",
language = "English",
booktitle = "2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)",
publisher = "IEEE",
address = "United States",

}

RIS - Download

TY - GEN

T1 - Radar Waveform Recognition using Fourier-Based Synchrosqueezing Transform and CNN

AU - Kong, Gyuyeol

AU - Koivunen, Visa

N1 - Avaa käsikirjoitus, kun julkaistu.

PY - 2019/9/17

Y1 - 2019/9/17

N2 - In this paper the problem of recognizing radar waveforms is addressed. Waveform classification is needed in spectrum sharing and radar-communications coexistence, cognitive radars and signal intelligence. Different radar waveforms exhibit different properties in time-frequency domain. We propose a deep learning method for waveform classification. The received signal is processed with Fourier synchrosqueezing transform that has excellent properties in revealing timevarying behavior, rate of, strength and number of oscillatory components in received signals. The resulting time-frequency description is represented as a bivariate image that is fed into a convolutional neural network. The proposed method has superior performance over the widely used the Choi-Williams distribution (CWD) method in distinguishing the polyphase waveforms even at low signal-to-noise ratio regime.

AB - In this paper the problem of recognizing radar waveforms is addressed. Waveform classification is needed in spectrum sharing and radar-communications coexistence, cognitive radars and signal intelligence. Different radar waveforms exhibit different properties in time-frequency domain. We propose a deep learning method for waveform classification. The received signal is processed with Fourier synchrosqueezing transform that has excellent properties in revealing timevarying behavior, rate of, strength and number of oscillatory components in received signals. The resulting time-frequency description is represented as a bivariate image that is fed into a convolutional neural network. The proposed method has superior performance over the widely used the Choi-Williams distribution (CWD) method in distinguishing the polyphase waveforms even at low signal-to-noise ratio regime.

M3 - Conference contribution

BT - 2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)

PB - IEEE

ER -

ID: 40357932