The present application belongs to the technical field of
radio spectrum monitoring, and provides a wide-frequency unmanned aerial vehicle spectrum detection and
rapid identification method based on
deep learning, which comprises the following steps: collecting wide-frequency radio signals to form a
composite signal sequence and converting the
composite signal sequence into a time-frequency feature map, performing
cepstrum transformation on the original
signal to extract a
cepstrum time
delay peak, constructing an environment compensation parameter vector, identifying frequency selective
fading notches in the time-frequency feature map, dividing to form a spectrum fragment set, constructing a fragment
association model in combination with a
frequency domain spacing and an inverse frequency position matching rule, and completing physical constraints, inputting the spectrum fragment spatial features and the environment compensation parameter vector into the model, calculating a coherence weight through a deep fragment
association model based on a Transform architecture, completing logical reorganization of the spectrum fragments in a vector space and generating a logical reorganization vector, inputting the vector into a double-
branch parallel classification network, and realizing accurate identification of unmanned aerial vehicle target models and communication protocols.