The invention discloses a
phased array online beam calibration method based on
deep learning. The
phased array online beam calibration method comprises three parts of directional diagram
data acquisition and
processing, initial model construction and model training, and model debugging and application. The method comprises the following steps of: randomly setting amplitude and phase parameters of a
phased array by using an amplitude and
phase control device, connecting a test antenna to a frequency
spectrograph, connecting an upper computer with the frequency
spectrograph through a switch, driving the phased array antenna to rotate by using a rotary table, and drawing a directional diagram of a received
signal in the upper computer; then constructing a neural
network model and training by using a directional diagram; and finally, carrying out model
verification by utilizing data of the
verification set, and if a verified
loss function does not meet the requirements of the method, carrying out model debugging. According to the invention, the amplitude and phase parameters of each channel corresponding to the target beam are directly obtained through the
deep learning model, the calculation of the amplitude and
phase error value of each channel is avoided, the calibration time and difficulty are greatly reduced, and the calibration efficiency is improved.