Processing 3D Flight Trajectory Data with Adaptive Kalman Filtering

Flight trajectory data from Quick Access Recorders (QAR) is critical for ensuring flight safety and conducting performance analysis. However, the inherent uncertainties and noise present in this data necessitate the use of filtering techniques. The conventional Kalman filter, widely applied in civil aviation, exhibits limitations when addressing varying noise levels across different aircraft types and flight phases. This study addresses these challenges through a multistep approach. First, QAR data from Daocheng Yading Airport underwent preprocessing, including data cleaning, resampling, key field selection, and target trajectory extraction. Next, the Kalman filter’s adaptive capabilities were enhanced and applied specifically to three-dimensional trajectory data. Finally, a comparative analysis was conducted with the segmented noise matrix adjustment method. The results demonstrate that the adaptive Kalman filter effectively preserves essential data characteristics while streamlining the filtering process.