Deep representation method for radar emitter signal using wavelet packets decomposition

Abstract

Signal representation is to transform the intercepted original signal into feature parameters, which is the basis for the electronic reconnaissance system to analyse and identify a radar. The traditional method is to manually design a limited number of features with prior knowledge, which can only achieve the desired results in specific applications. Therefore, it is necessary to study signal representation methods that are more adaptable, reasonable, and effective. This study proposes a feature-level design scheme that extracts multi-level signal feature parameters to obtain deep essential feature representations, which is consistent with deep-learning frameworks. Through the decomposition through wavelet packet transform (WPT), the best sub-band tree structure of the signal can be obtained, and the depth representation of the signal can be well achieved, which can meet requirements of time–frequency analysis automatically. Here, Symlets wavelet packet decomposition (WPD) is used as an example to implement multi-layer signal feature representation. The combination of characteristic parameters at different levels can reflect the different information contained in different radar signals. The simulation results show that the method can effectively identify the modulation mode of radar signals and enhance the information mining ability from radar signals.