The present invention discloses a deep convolutional network-based airborne
ground penetrating radar target identification method, relates to the
machine learning and
ground penetrating radar application technologies, in particular to the application of a
deep learning method in the airborne
ground penetrating radar target identification. The method comprises the following steps of acquiring and pre-
processing the
radar data, designing the multiple
layers of structures of a neural network, selecting a hyper-parameter, preventing the
overfitting, activating a function, training a convolutionalmodel and displaying a prediction result. The airborne ground penetrating
radar target identification method of the present invention identifies an airborne ground penetrating
radar target, can automatically extract the parameters of the updated network during the training process, and reduces the manual intervention during the
processing process. Meanwhile, the convolutional model of the presentinvention can extract the two dimensional filter characteristics of the different levels of the target, and the characteristics can represent the characteristics, such as the target, the background, the interference, etc. The deep convolutional network-based airborne ground penetrating radar target identification method enables the accuracy of the airborne ground penetrating radar
target signal identification to be improved.