4D Track Prediction Based on BP Neural Network Optimized by Improved Sparrow Algorithm

The prediction accuracy of 4D (four-dimensional) trajectory is crucial for aviation safety and air traffic management. Firstly, the sine chaotic mapping is employed to enhance the sparrow search algorithm (Sine-SSA). This enhanced algorithm optimizes the threshold parameters of the BP (back propagation) neural network (Sine-SSA-BP), thereby improving the quality of the initial solution and enhancing global search capability. Secondly, the optimal weight thresholds obtained from the Sine-SSA algorithm are integrated into the BP neural network to boost its performance. Subsequently, the 4D trajectory data of the aircraft serve as input variables for the Sine-SSA-BP prediction model to conduct trajectory predictions. Finally, the prediction results from three models are compared against the actual aircraft trajectory. It is found that within the specified time series, the errors in longitude, latitude, and altitude for the Sine-SSA-BP prediction model are significantly smaller than those of the simple BP and SSA-BP models. This indicates that the Sine-SSA-BP model can achieve high-precision 4D trajectory prediction. The accuracy of trajectory prediction is notably improved by the sparrow search algorithm optimized with sine chaotic mapping, leading to faster convergence and better prediction outcomes, which better meet the requirements of aviation safety and control.