Automatic modulation classification of radar signals using the generalised time-frequency representation of Zhao, Atlas and Marks
Automatic modulation classification of radar signals using the generalised time-frequency representation of Zhao, Atlas and Marks
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DOI：
10.1049/iet-rsn.2010.0174
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发表时间：
2011-03
影响因子：
1.7
通讯作者：
Deguo Zeng;X. Zeng;G. Lu;Bo Tang
中科院分区：
文献类型：
--
作者：
Deguo Zeng;X. Zeng;G. Lu;Bo Tang
在非合作环境下，对雷达信号进行自动调制分类是电子情报接收机的一项挑战性任务。为了实现负信噪比下5种雷达信号的自适应调制，从Zhao、Atlas和Marks的广义时频表示（ZAM-GTFR）中提取了4个特征量，即绝对斜率和比、多项式拟合系数、岭阶数和极值归一化差系数。仿真结果表明，当信噪比大于-2%dB时，系统的成功识别率可达90%。该算法适用于探测距离要求较高的电子情报接收机。
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.