Complex radar radiation source identification method based on one-dimensional self-stepping convolutional neural network

The invention provides a complex radar radiation source identification method based on a one-dimensional self-stepping convolutional neural network, and solves the problems that in the prior art, dimension transformation processing needs to be carried out on radar signals and the identification rate is low. The implementation scheme comprises the steps of collecting radar radiation source signalsto make a data set; dividing the data set into a training set and a verification set; constructing a one-dimensional self-stepping convolutional neural network; setting a self-stepping sample trainingstrategy and training the network by using the training set; and inputting the data of the test set into the trained one-dimensional self-stepping convolutional neural network, and outputting the recognition rate of the overall test signal. The one-dimensional self-stepping convolutional neural network constructed by the method is simple in structure and small in parameter quantity, can directlyextract the time domain sig