Radar radiation source signal identification technology in non-Gaussian clutters

Abstract

The invention belongs to the technical field of radar signal processing in a non-Gaussian clutter environment, and discloses a radar signal identification method based on generalized variational modedecomposition combined with a support vector machine, which comprises the following steps: carrying out generalized variational mode decomposition on a received radar radiation source signal to obtainan intrinsic mode component IMF; computing smooth pseudo Wigner-Ville time-frequency distribution matrix for each intrinsic mode component, and extracting Renyi entropy features of time-frequency distribution to construct a feature vector T; and finally, carrying out classification and identification by utilizing a support vector machine. When the generalized signal-to-noise ratio is larger than5dB, the identification rate of various signals reaches 65% or above, especially when the generalized signal-to-noise ratio is larger than 10dB, the identification rate of various signals reaches 90%or above, and therefore the identification effect of the method is good.

Description

technical field [0001] The invention belongs to the technical field of radar signal processing, and in particular relates to a radar radiation source signal classification and identification method based on generalized variational mode decomposition in a non-Gaussian clutter environment. Background technique [0002] Radar emitter signal recognition is a key technology in radar electronic reconnaissance, which directly affects the performance of electronic reconnaissance equipment and is related to subsequent combat decisions. According to the basis, it has a very important position and function in the process of radar electronic countermeasures, so it is of practical significance to study the identification of radar emitter signal. At present, many achievements have been made in the research on radar emitter signal recognition in Gaussian clutter environment. Ataollah Abrahamzadeh et al. used the 8th-order moment and 8th-order cumulant for identification, which can achieve...

AI Technical Summary

Problems solved by technology

[0003] To sum up, the problem of the existing technology is that it is only applicable to the environment of Gaussian clutter, and the existing technology cannot achieve good results for the interference of non-Gaussian clutter, and it is only suitable for the identification of a few source signals , the recognition effect for multiple source signals is poor

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