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

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.

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