Method of ADS-B Data Point Densification Based on Predictive Interpolation and Its Application in Multi-Aircraft Situations

The Automatic Dependent Surveillance-Broadcast (ADS-B) system is widely used for air traffic monitoring, providing essential data for air traffic management. However, the spatial and temporal sparsity of ADS-B data points often affects the accuracy and effectiveness of monitoring systems, particularly in scenarios involving multiple aircraft. To address this issue, this paper proposes a method for ADS-B data point densification based on predictive interpolation. By leveraging historical data and machine learning techniques, this method predicts the future trajectory of aircraft and interpolates additional data points, thereby enhancing the spatial and temporal resolution of the dataset. The proposed method has been tested and validated in multi-aircraft situations, showing significant improvement in monitoring accuracy and efficiency.