A communication fingerprint identification method integrating multi-layer sparse learning and multi-view-angle learning

Abstract

The invention discloses a communication fingerprint identification method integrating multi-layer sparse learning and multi-view angle learning, which comprises the following steps: 1) adopting a sparse automatic encoder to suppress noise for an original steady-state signal and an original transient-state signal; Carrying out bispectrum analysis and cyclic spectrum analysis on the de-noised signal, and obtaining characteristics on a transform domain by using a sparse coding method based on an over-complete signal dictionary; 2) for the second-order matrix form features on the transform domain,adopting a sparse coding method to obtain low-dimensional features which describe the fine features of the signal more simply and accurately; 3) for the radio station with multiple frequency points and multiple modulation modes, in order to comprehensively extract the common characteristics of the radio station under different working carrier frequencies and modes, adopting tree structure sparsecoding, and 4) from the characteristics of different visual angles, adopting multi-visual-angle canonical correlation analysis to carry out fusion of multiple sparse coding characteristics, and adopting a full connection neural network to carry out classification.

Description

technical field [0001] The invention relates to a communication fingerprint identification method that integrates multi-layer sparse learning and multi-view learning, and belongs to the sub-field of communication fingerprint identification technology in the field of communication countermeasures and cognitive radio. Background technique [0002] Due to the random discreteness of each individual communication radiation source in terms of component performance, production process, and debugging, the radiated signal will always have characteristics that distinguish it from other individual radiation sources. In recent years, many scientific research institutions at home and abroad have carried out in-depth research on this aspect, and the characteristics of communication radiation sources extracted can be generally classified into transient signal characteristics and steady-state characteristics. [0003] The transient feature refers to the feature extracted from the signal of ...

AI Technical Summary

Problems solved by technology

[0005] None of the above-mentioned traditional methods comprehensively consider the problem of signal noise, the problem of fusion and utilization of multiple features, the problem of multi-carrier frequency stations, etc.

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