Intelligent Radiation Source Identification Method Based on GRU Deep Convolutional Network

Abstract

The invention is an intelligent radiation source identification method based on GRU deep convolutional network, which mainly solves the problem that the serialization characteristics of radar radiation source signals cannot be extracted in the prior art. The scheme is: classify radar radiation source signals; simulate radar The radiation source signal, and slice the radar radiation source signal; convert the sliced ​​samples into two-dimensional real number samples, normalize the two-dimensional real number samples and divide the training sample set and test sample set; construct GRU based on the gated loop unit input the training sample set into the deep neural network, and obtain the trained deep neural network model by optimizing the loss function; input the test sample set into the trained deep neural network model to obtain the radar radiation source Signal recognition results. The invention can extract the associated features before and after the signal, avoid manual feature extraction and prior knowledge, have low complexity, and have accurate classification results, and can be used to identify radar radiation sources in complex electromagnetic environments.

Description

technical field [0001] The invention belongs to the technical field of radar, and further relates to a radiation source signal identification method, which can be used for automatic feature extraction and related parameter identification of radar radiation source signals in complex and changeable electromagnetic environments. Background technique [0002] Radar emitter signal classification and identification is an important part of modern electronic intelligence reconnaissance system and electronic support system, and it is of great significance to national defense construction. After years of painstaking research, the identification of radar emitter signals has made great progress. The traditional signal identification method based on five radar parameter characteristics is no longer suitable for modern electronic warfare. Therefore, some scholars extract intrapulse feature information from radar radiation sources for identification, and can obtain a satisfactory accuracy...

AI Technical Summary

Problems solved by technology

This method can achieve a high recognition rate in the identification of radiation source signals, solve the shortcomings of traditional identification methods that are highly dependent on prior knowledge, and complete the extraction of relevant features before and after the signal, so as to identify radiation sources in more complex situations. signal for identification

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