Automatic modulation classification of radar signals using the generalised time-frequency representation of Zhao, Atlas and Marks

Author

Zeng, Deze ; Zeng, Xuan ; Lu, Guo-Quan ; Tang, Bo-Hui

Author_Institution

Sch. of Electron. Eng., Univ. of Electron. Sci. & Technol. of China, Chengdu, China

Volume

5

Issue

4

fYear

2011

fDate

4/1/2011 12:00:00 AM

Firstpage

507

Lastpage

516

Abstract

The automatic modulation classification (AMC) of a detected radar signal is a challenging task of an electronic intelligence (ELINT) receiver in a non-cooperative environment. With the aim to realise the AMC of five kinds of radar signals under negative signal-to-noise ratio (SNR), the authors have gained four characteristic features, namely, the ratio of sum of absolute slope, the coefficient of polynomial curve fitting, the number of ridge stairs and the normalised coefficient of difference of the extreme, from the generalised time-frequency representation of Zhao, Atlas and Marks (ZAM-GTFR). Simulation results show the probabilities of successful recognition (PSRs) can reach 90% when SNR is above -2%dB. The algorithm is suitable for the ELINT receiver when the detection range is critical.

Keywords

curve fitting; radar signal processing; time-frequency analysis; automatic modulation classification; electronic intelligence receiver; polynomial curve fitting; probabilities of successful recognition; probability of intercept technology; radar signals; time frequency representation;

fLanguage

English

Journal_Title

Radar, Sonar & Navigation, IET

Publisher

iet

ISSN

1751-8784

Type

jour

DOI

10.1049/iet-rsn.2010.0174

Filename

5739673