Radar Modulation Identification Using Inequality Measurement in Frequency Domain
Summary :
In this paper, we proposed a method for radar modulation identification based on the measurement of inequality in the frequency domain. Gini's coefficient was used to exploit the inequality in the powers of spectral components. The maximum likelihood classifier was used to classify the detected radar signal into four types of modulations: unmodulated signal (UM), linear frequency modulation (LFM), non-linear frequency modulation (NLFM), and frequency shift keying (FSK). The simulation results demonstrated that the proposed method achieves an overall identification accuracy of 98.61% at a signal-to-noise ratio (SNR) of -6dB without a priori information such as carrier frequency, pulse arrival times or pulse width.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E100-A No.4 pp.975-981
- Publication Date
- 2017/04/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.E100.A.975
- Type of Manuscript
- PAPER
- Category
- Digital Signal Processing
Authors
Kyung-Jin YOU
Soongsil University
Ha-Eun JEON
Soongsil University
Hyun-Chool SHIN
Soongsil University
Keyword
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Kyung-Jin YOU, Ha-Eun JEON, Hyun-Chool SHIN, "Radar Modulation Identification Using Inequality Measurement in Frequency Domain" in IEICE TRANSACTIONS on Fundamentals,
vol. E100-A, no. 4, pp. 975-981, April 2017, doi: 10.1587/transfun.E100.A.975.
Abstract: In this paper, we proposed a method for radar modulation identification based on the measurement of inequality in the frequency domain. Gini's coefficient was used to exploit the inequality in the powers of spectral components. The maximum likelihood classifier was used to classify the detected radar signal into four types of modulations: unmodulated signal (UM), linear frequency modulation (LFM), non-linear frequency modulation (NLFM), and frequency shift keying (FSK). The simulation results demonstrated that the proposed method achieves an overall identification accuracy of 98.61% at a signal-to-noise ratio (SNR) of -6dB without a priori information such as carrier frequency, pulse arrival times or pulse width.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E100.A.975/_p
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@ARTICLE{e100-a_4_975,
author={Kyung-Jin YOU, Ha-Eun JEON, Hyun-Chool SHIN, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Radar Modulation Identification Using Inequality Measurement in Frequency Domain},
year={2017},
volume={E100-A},
number={4},
pages={975-981},
abstract={In this paper, we proposed a method for radar modulation identification based on the measurement of inequality in the frequency domain. Gini's coefficient was used to exploit the inequality in the powers of spectral components. The maximum likelihood classifier was used to classify the detected radar signal into four types of modulations: unmodulated signal (UM), linear frequency modulation (LFM), non-linear frequency modulation (NLFM), and frequency shift keying (FSK). The simulation results demonstrated that the proposed method achieves an overall identification accuracy of 98.61% at a signal-to-noise ratio (SNR) of -6dB without a priori information such as carrier frequency, pulse arrival times or pulse width.},
keywords={},
doi={10.1587/transfun.E100.A.975},
ISSN={1745-1337},
month={April},}
Copy
TY - JOUR
TI - Radar Modulation Identification Using Inequality Measurement in Frequency Domain
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 975
EP - 981
AU - Kyung-Jin YOU
AU - Ha-Eun JEON
AU - Hyun-Chool SHIN
PY - 2017
DO - 10.1587/transfun.E100.A.975
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E100-A
IS - 4
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - April 2017
AB - In this paper, we proposed a method for radar modulation identification based on the measurement of inequality in the frequency domain. Gini's coefficient was used to exploit the inequality in the powers of spectral components. The maximum likelihood classifier was used to classify the detected radar signal into four types of modulations: unmodulated signal (UM), linear frequency modulation (LFM), non-linear frequency modulation (NLFM), and frequency shift keying (FSK). The simulation results demonstrated that the proposed method achieves an overall identification accuracy of 98.61% at a signal-to-noise ratio (SNR) of -6dB without a priori information such as carrier frequency, pulse arrival times or pulse width.
ER -
English
