Low-interception waveform generation method

Abstract

The invention relates to a low-interception waveform generation method, which comprises the following steps of: s1, generating an original signal, and obtaining a modulation identification model; s2, sending an original signal before each disturbance update, and calculating an update gradient according to a recognition effect of the modulation recognition model on the original signal; s3, modifying the original signal according to the gradient to obtain an updated expected signal; s4, judging whether the expected signal is converged or not, if so, continuing to execute the following steps, otherwise, performing loop iteration on the steps s2 to s4 until the expected signal is converged; s5, carrying out amplification and discretization processing on the expected signal; s6, sending an expected signal, and outputting an identified probability by the modulation identification model; s7, comparing the probability with a preset threshold value B, when the probability is less than or equal to the threshold value, generating the waveform of the target low interception signal, and when the probability is greater than the threshold value, repeating the steps s2-s7 until the finally obtained probability is less than or equal to the threshold value. According to the invention, the communication signals can be effectively prevented from being identified, information transmitted in a communication system is prevented from being intercepted and eavesdropped, and the safety and reliability of the communication system are enhanced.

Description

technical field [0001] The invention relates to the technical field of wireless communication anti-jamming, in particular to a method for generating low interception waveforms. Background technique [0002] While the communication system produces important military and civilian application value, it is very easy to be intercepted by eavesdropping because of its complex structure. [0003] Thanks to the development of deep learning technology, the strongest existing intelligent models can already identify the modulation format of spectral signals with extremely high accuracy. This technology has the characteristics of accuracy, high efficiency, strong generalization ability, and low deployment cost for users. However, for communication, the development of this type of technology is a double-edged sword. While bringing excellent performance, it also provides convenience for those who use it maliciously, making existing communication signals face severe interception and Risk ...

AI Technical Summary

Problems solved by technology

The existing technology does not start from the aspect of the communication signal itself to conduct research on reducing the risk of communication being eavesdropped and intercepted

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