Research On Complex Signal Identification Based On High-order Cumulant Under Low Signal/Noise Rate

Rapid development of radar technology has put forward an austere challenge to radar reconnaissance system. On the one hand, the forms of both domestic and foreign radar signal become more and more complex. On the other hand, the number of different kinds of electronic reconnaissance equipment increases rapidly, and the electromagnetism signals become more and more dense, so radar reconnaissance system is operating under a highly dense signal environment. Therefore, the ability of distinguishing and identifying the signals has not only become one of the important criterions to estimate the advanced degree of electronic reconnaissance technology, but also become one of the urgent and difficult tasks of modern radar technology. Research on complex signal identification under low signal/noise rate based on high-order cumulant is put forward based on this idea.By carefully analyzing and studying the theory of fractional Fourier transform and high-order cumulant, high-order cumulant has been combined with fractional Fourier transform, because high-order cumulant can effectively inhibit different kinds of Gaussian noise, and the energy of linear-frequency modulate signal congregates in fractional Fourier domain. Therefore, a novel time-frequency analyze method has been put forward in the thesis, that is, the algorithm of LFM signal kurtosis detection based on high-order cumulant in fractional Fourier domain. The results of the simulations have not only proved the validity of the method, but also compared this method with existed traditional time-frequency analysis methods. It has proved that under the circumstance of low signal/noise rate, for either single LFM signal or multi-LFM signal, compared with traditional methods, this method can more effectively inhibit different kinds of Gaussian noise, avoid the influence of cross term and identify useful signal. So the kurtosis detection algorithm can be used in complex circumstance of multi-LFM signal with low signal/noise rate. It can inhibit different kinds of Gaussian noise more effectively and be more suitable for the identification of complex signal.However, the research and application of both fractional Fourier domain and high-order cumulant should be studied further. In the future work, the kurtosis detection and fractional Fourier transform can be used to estimate important characteristic parameters of LFM signal, and some fine characteristics of high-order cyclic cumulant can also be used in the field of signal detection and identification.