Radio signal identification method based on deep learning model and realization system thereof
A radio signal, deep learning technology, applied in signal pattern recognition, character and pattern recognition, biological neural network model and other directions, to achieve the effect of easy promotion and application
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Embodiment 1
[0020] figure 1 A schematic flow chart showing a radio signal identification method based on a deep learning model provided by the present invention, figure 2 An example diagram of a two-dimensional time-frequency sample diagram and a target subband time-frequency diagram provided by the present invention is shown. The radio signal recognition method based on a deep learning model provided in this embodiment includes the following steps.
[0021] S101. Convert the time-domain sample data of the target radio signal into time-frequency sample data through STFT transformation, and generate a two-dimensional time-frequency sample graph according to the time-frequency sample data.
[0022] In the step S101, the target radio signal may, but is not limited to, be displayed by the user in a broadband data waterfall diagram (a way of displaying broadband data in a way of abscissa frequency, ordinate time, and signal strength of time-frequency points identified by color shades) The t...
Embodiment 2
[0035] image 3 A schematic diagram of the system structure for realizing the radio signal identification method based on the deep learning model provided by the present invention is shown. This embodiment provides a system for implementing the radio signal identification method described in Embodiment 1, including a sample signal preprocessing module, a sample training module, an air interface signal receiving module, an air interface signal preprocessing module, a classification identification module and a reverse calculation module The sample signal preprocessing module is connected to the sample training module in communication, and is used to convert the time-domain sample data of the target radio signal into time-frequency sample data through STFT transformation, and generate a two-dimensional time-frequency sample graph according to the time-frequency sample data The sample training module is communicatively connected to the classification identification module, which i...
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