Radiation source identification method and device based on bispectrum three-dimensional image texture features

Abstract

The invention relates to a radiation source identification method and device based on bispectrum three-dimensional image texture features. The method comprises the following steps: acquiring sample data of different radiation source signals and calculating bispectrum of signals to obtain bispectrum three-dimensional images; for bispectral three-dimensional images, extracting texture features by agray level co-occurrence matrix; for texture features, using a support vector machine to classify and identify the emitter. The invention avoids the problems of high characteristic dimension and lossof integral bispectrum part classification information and the like existing in adopting bispectrum alone. The steady-state signal RF fingerprint in individual identification of an emitter is extracted, sample information is fully utilized, the efficiency, application range and robustness of classifier training and recognition are effectively improved, and the advantages under different conditionssuch as noise immunity, small sample size, multi-target, and universality of different modulation modes are exhibited. The effectiveness of the classifier in the actual electromagnetic environment isverified by the actual radio signal, and the method and the device have important research values in improving the efficiency and robustness of classifier.

Description

technical field [0001] The invention belongs to the technical field of satellite communication security, in particular to a radiation source identification method and device based on bispectral three-dimensional image texture features. Background technique [0002] In the satellite communication system, the individual identification of the radiation source is realized by processing the transient signal or steady-state signal of the radio frequency signal. Most researchers at home and abroad focus on the study of transient signals such as power on and off, but the duration of transient signals is short, and it is extremely difficult to detect and capture them under non-cooperative conditions. more important practical significance. In the existing steady-state signal research, the bispectrum algorithm has time-shift invariance, scale variability, phase retention and suppression of Gaussian noise, but the direct application of bispectrum requires the calculation of complex two...

AI Technical Summary

Problems solved by technology

Most researchers at home and abroad focus on the study of transient signals such as power on and off, but the duration of transient signals is short, and it is extremely difficult to detect and capture them under non-cooperative conditions. More important practical significance

In the existing steady-state signal research, the bispectrum algorithm has time-shift invariance, scale variability, phase retention and suppression of Gaussian noise, but the direct application of bispectrum requires the calculation of complex two-dimensional matching templates, which leads to its recognition Low efficiency and limited range of application

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