Abstract: Aiming at the problem of anomalous points such as missing points and outliers in the data of automatic dependent surveillance broadcast (ADS-B) ground receiving station, the missing points of ADS-B trajectory are screened on the basis of establishing a multi-constraint discriminant model, and the trajectory is interpolated. The improved neighbor algorithm with standardized Euclidean metric is used to detect anomalous point data of the whole trajectory. Finally, the multiple interpolation method is applied to repair the anomalous trajectory points. Taking actual operational data as an example, the accuracy of the improved K-nearest neighbor (KNN) for detecting ADS-B anomalous trajectory points is verified and compared. This method can effectively solve the problem of detection omission of trajectory anomalous point and provide support for mining original trajectory data of aircraft ADS-B.

Key words: font-size:15.04px, ">ADS-B data screening, anomalous point, standardized Euclidean metric, improved neighbor algorithm, multiple interpolation method