Depth Learning Model for RDSS Emission in Maritime Aviation Distress

When the pilot is in maritime distress, the most important thing about rescue is to obtain distress location information. However, due to the complexity of the weather and conditions, The pilot's hand-held life-saving communications equipment has become less effective，and power consumption has also increased significantly. This paper studies the application of short message communication in Beidou satellite navigation system at sea. Based on the deep neural network, this paper proposes a prediction model which converts the parameters of height, latitude and longitude, direction, pitch angle, electric quantity, velocity and acceleration into single precision floating-point input depth neural network. The results of communication success rate prediction and transmission delay prediction can be obtained by processing the data in 10 layers and 32 neurons. The test shows that the success rate of message launch is doubled, on the basis of the 43% extension of the working time.