Communication radar radiation source identification method in presence of unsteady SNR (Signal Noise Ratio)

Abstract

The invention provides a communication radar radiation source identification method in the presence of an unsteady SNR (Signal Noise Ratio). The communication radar radiation source identification method comprises the steps of performing discrete normalization respectively for different types of radiation source signals; capturing the same discrete point number for signals of each radiation source so as to respectively compose a respective radiation source signal sequence; regrouping the composed radiation source signal sequences to obtain a series of feature vectors; selecting a multi-fractal dimension, then calculating the feature vector of the multi-fractal dimension feature of the signals in the presence of multiple different signal noise ratios and then storing the feature vector of the multi-fractal dimension feature as a feature database; training the feature vectors in the database by utilizing a neural network so as to obtain public features of the same type of the radiation source signals; and training and testing a neural network system by regarding a to-be-identified signal as an input so as to realize identification of the radiation source signals in the presence of the unsteady SNR.

Description

technical field [0001] The present invention relates to the technical field of radiation source signal identification in electronic countermeasure intelligence reconnaissance, in particular to the specific identification method of radar radiation source signal and communication radiation source signal, especially radar and communication radiation based on multi-fractal dimension under unstable signal-to-noise ratio Source signal feature extraction and recognition methods. Background technique [0002] In the modern electronic reconnaissance radiation source signal identification system [1-2], it is not only required to be able to identify different types of radiation source signals with different functional parameters, but also to identify different individual radiation source signals with similar parameters. In a complex electromagnetic environment, the signal-to-noise ratio is often not stable. Therefore, how to realize the identification of radiation sources under an unst...

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Problems solved by technology

[0004] In the field of communication radiation source identification, there are several identification methods based on decision theory, neural network, wavelet transform, and parameter statistics. Among them, the method based on parameter statistics is relatively simple, with a small amount of calculation and easy to implement. Parameters are the difficulty of this algorithm; wavelet transform has been widely used in the field of radiation source identification, however, its anti-noise performance is poor, and it is difficult to achieve identification under low signal-to-noise ratio; neural network identification methods have been widely used in various fields It is widely used. By training and testing the obtained signal features, a better recognition effect can usually be achieved, but at the cost of training time.

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