A method for individual identification of frequency hopping radio stations

Abstract

A feature extraction method based on the time-frequency energy spectrum of the frequency hopping signal is proposed. This method firstly reconstructs the time-frequency spectrum sparsely from the frequency-hopping signal, and then blocks the time-frequency energy spectrum under different scale conditions to extract the time-frequency spectrum respectively. Three features of energy spectrum Rayleigh entropy, difference box dimension and multifractal dimension. Classification recognition experiments show that the recognition performance of the present invention is less affected by the number of training samples, and can have a higher recognition accuracy rate under the condition of a small number of training samples.

Description

technical field [0001] The invention relates to wireless communication and signal processing technology, in particular to a method for individual identification of frequency hopping radio stations. Background technique [0002] Frequency hopping communication network station sorting is the first prerequisite for intercepting enemy communication and generating the best jamming signal. The existing frequency hopping signal network station sorting mainly uses the frequency hopping signal duration, orientation information, power and signal time correlation to realize the frequency hopping signal network station sorting and identification. However, with the increase of frequency hopping modes, it is difficult to realize correct sorting of frequency hopping signals only by relying on the above features. Due to the random discreteness of the component performance, production process and debugging of each frequency hopping radio station, the frequency hopping signal radiated by it ...

AI Technical Summary

Problems solved by technology

At present, the commonly used subtle feature extraction methods mainly include: classifying and identifying by extracting the characteristics of frequency conversion transient signals of frequency hopping communication equipment; using different characteristics of the transient response process of transmitting power amplifiers The multi-dimensional features of the energy spectrum realize the individual identification of the radiation source, but due to the redundant information of the feature set extracted by this method, the recognition accuracy is very low; by extracting the first and second moments and energy of the fixed-scale time-frequency energy spectrum Spectral entropy features, but it does not consider that under different block conditions, the time-frequency energy spectrum has different distribution information characteristics, resulting in low classification and recognition accuracy

Moreover, most of the existing research algorithms for fine feature recognition of frequency hopping radio stations are carried out under the conditions of large samples and high signal-to-noise ratio, which cannot effectively overcome the adverse effects of sample size and signal-to-noise ratio on the classification recognition rate.

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