Optimization of Aircraft Flight Trajectory Combined with Thinking Navigation Algorithm

In order to improve the optimization effect of the flight trajectory of the aircraft, this paper combines the thinking navigation algorithm to optimize the flight trajectory of the aircraft and analyzes the flight trajectory of the aircraft through the intelligent model. By processing the original satellite clock error data by the first-order difference method, the modeling data can be more suitable for nonlinear characteristics. Moreover, this paper chooses a simple network structure and uses the MEA to select the optimal initial parameters of the model for the BP neural network, which can avoid the local optimization of the BP neural network results. In addition, this paper conducts experimental analysis on the MEA-BP model through fitting data of different lengths. The simulation test results show that the thinking navigation algorithm proposed in this paper has a very obvious effect on the optimization of the flight trajectory of the aircraft.