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Aiming at the problem that the satellite navigation and positioning system can not cover the whole area and that the positioning accuracy of the existing ground-based reinforcement system decreases with the increase of the distance between the target and the ground station, a method for optimizing the positioning system of mobile ground station based on whale optimization algorithm is proposed. Based on the mobile ground station, the method identifies the positioning target with the optimal whale individual and the mobile ground station with other whale individuals, realizes that the position of the ground station varies with the position of the target point, and ensures the high precision positioning of the target in area with weak signals or without satellite navigation and without the support of the enhanced system. The feasibility of this method is proved by MATLAB, which provides a new way of thinking for solving the problem of regional target location.

Aiming at the problems of the difficulty to establish the model and the large data dimension for the traditional integrated navigation method, a method of using the wavelet neural network to directly predict the position and velocity error information is proposed. Get rid of the mathematical model establishment, void introducing new errors in the model establishment, and adopt multiple parallel networks to reduce the dimensionality of the data, which greatly reduces the amount of calculation. The Kalman filter is used as a reference for simulation experiment. The results show that the proposed method can effectively improve the accuracy and real-time performance of the integrated navigation system, and provides a new feasible path for combined navigation filtering.