Illegal broadcast signal classification method based on recurrent neural network

A technology of cyclic neural network and illegal broadcasting, which is applied in the field of radio signal monitoring and management, can solve problems such as the inability to effectively realize the identification of illegal broadcasting signals of radio signals, and achieve the effects of easy supervision and investigation, high recognition accuracy and simple operation

Active Publication Date: 2019-10-11
DALIAN UNIV OF TECH +1
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Therefore, most of these schemes cannot effectively realize the supervis

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  • Illegal broadcast signal classification method based on recurrent neural network
  • Illegal broadcast signal classification method based on recurrent neural network
  • Illegal broadcast signal classification method based on recurrent neural network

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[0033] The specific implementation manners of the present invention will be further described below in conjunction with the accompanying drawings and technical solutions.

[0034] The classification method of illegal broadcasting signals based on cyclic neural network, the overall block diagram of the system is as follows figure 1 shown. The method can be divided into four links, namely: signal data collection, signal data quality judgment, frequency point library + neural network classification, and prediction value processing. Among them, the signal data acquisition process can obtain the data required for the experiment, and save the frequency point information in the data to classify the frequency point library. The function of signal data quality judgment is to judge the quality of data, and the quality of data directly affects the accuracy of classification. The pre-classification of the frequency point library can distinguish the illegal broadcast signal of the illega...

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Abstract

The invention discloses an illegal broadcast signal classification method based on a recurrent neural network, and belongs to the technical field of radio signal monitoring and management. Firstly, LabVIEW and USRP are used for receiving broadcasting station signals, after quality judgment is conducted on the received signals, data of each frequency point is segmented, and therefore a training sample and a test sample are manufactured. According to the method, illegal signals of illegal frequency points are distinguished through comparison and pre-classification of a legal broadcast signal frequency point library, then deep learning classification is utilized to enable a network to output predicted values of the signals, and then data processing is performed on the predicted values to complete classification and identification of the illegal broadcast signals. Whether illegal signals exist or not can be judged at a high speed and high precision, operation is easy, and radio station monitoring and management of a radio management department are facilitated.

Description

technical field [0001] The invention belongs to the technical field of radio signal monitoring and management, and relates to a method for classifying illegal broadcast signals based on a cyclic neural network, which is a method for classifying and identifying illegal broadcast signals by using a cyclic neural network. Background technique [0002] Illegal broadcast signals are illegal radio stations that are set up without the approval of the radio and television management department and the radio management agency and use broadcast frequencies to broadcast to the society. If it is not controlled, it will disrupt the broadcast order, interfere with civil aviation communications, and even affect social stability. Therefore, an effective way to monitor illegal broadcast signals is needed. After investigation, it is found that there are few studies on direct monitoring of illegal broadcast signals, and there are mainly two methods for radio fingerprint identification associat...

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Application Information

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IPC IPC(8): G06K9/62
CPCG06F18/2431G06F18/214
Inventor 邱天爽刘浩李蓉李景春
Owner DALIAN UNIV OF TECH
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