Self-adaptive anti-interference beam forming method based on preprocessing deep reinforcement learning

The invention discloses a self-adaptive anti-interference beam forming method based on preprocessing deep reinforcement learning, which belongs to the technical field of navigation, and comprises the following steps: constructing a GPS terminal signal model including a 2 * 2 dual-polarized antenna array, a control variable, a time-varying gain interference variable and Gaussian noise; constructing a deep learning convolutional neural network CNN, wherein the deep learning convolutional neural network CNN comprises a data feature extraction network layer, a convolutional network layer, a pooling layer, an activation function layer and a full link layer; performing Q network reinforcement learning processing and decision implementation on the deep learning convolutional neural network to obtain a deep reinforcement learning Q network; and training the deep reinforcement learning Q network to obtain a trained deep reinforcement learning convolutional neural network. According to the invention, beam control is pe