关键词: 核密度估计, 浮标, 带宽优化, 递归神经网络, 助航效能, 空间信息

Abstract: It is one of the responsibilities of the navigation support department to ensure the correct layout position of the light buoy and provide as accurate position information as possible for ship navigation and positioning. If the position deviation of the light buoy is too large to be detected in time, sending wrong navigation assistance information to the ship will directly affect the navigation safety of the ship and increase the pressure on the management department. Therefore, mastering the offset characteristics of light buoy is of great significance for the maintenance of light buoy and improving the navigation aid efficiency of light buoy. Kernel density estimation can intuitively express the spatial and temporal distribution characteristics of buoy position, and indicates the intensive areas of buoy position in the channel. In this paper, in order to speed up deciding the optimal variable width of kernel density estimator, an improved adaptive variable width kernel density estimator is proposed, which reduces the risk of too smooth probability density estimation phenomenon and improves the estimation accuracy of probability density. A fractional recurrent neural network is designed to search the optimal bandwidth of kernel density estimator. It not only achieves faster training speed, but also improves the estimation accuracy of probability density.