A low
signal-to-
noise ratio multi-rotor unmanned aerial vehicle
radar signal classification method and
system based on
deep learning belong to the field of complex domain
signal classification. The problem that current methods lack effective classification performance in low signal-to-
noise ratio or complex environment is solved. The method comprises the following steps: arranging
radar echo data into a two-channel one-dimensional
time sequence signal, the first channel being real part data and the second channel being imaginary part data, forming an input
tensor, inputting into a C-ELTSNet network, containing four residual shrinkage modules and four fully connected
layers, each module performing: twice one-dimensional
convolution operation, followed by batch normalization and LeakyReLU activation after
convolution to obtain a feature map;
noise filtering is performed through an MCAT module; the filtered features are added to the input features through residual connection, and are output to four fully connected
layers for feature compression and class mapping, and the type of unmanned aerial vehicle is output according to the C-ELTSNet network. It is used in the field of unmanned aerial vehicle classification.