In air traffic flow management system, more attention of flight trajectory prediction is paid to the passing time and altitudes on some report points. For this purpose, a KNN based method using both flight plan and radar trajectory data is proposed in this paper. This method takes radar trajectory data to search for the neighbors of the query trajectory, and then takes the corresponding flight plan data to predict the report-point-conditioned time and altitudes. The experiments on actual flight data verify that the proposed method is able to predict flight point-conditioned time and altitudes accurately.
Four dimensional (4D) flight trajectories play an important role in air traffic future plans. In this paper, the time and altitude variables in 4D trajectories are analyzed for their characteristics, and the procedure of preprocessing flight trajectory data is provided, and support vector regression and decision tree regression are introduced to build the prediction models for trajectory time and altitude, respectively. It is demonstrated by the experiments on actual flight trajectory data that the proposed method can improve the 4D trajectory prediction accuracy effectively.
The measurement of trajectory distance is the base of trajectory clustering. To deal with the flight trajectory clustering in air traffic, a novel method is proposed in this paper to measure the flight trajectory distance. This method views the trajectory as a set of segments, whose end points are trajectory points, and it measures the distance from a trajectory point to another trajectory, and thus presents the distance definition of trajectories. Based on the calculated distance matrix, spectral clustering algorithm is adopted to cluster flight trajectories. The experiment on actual flight trajectory data verifies the effectiveness of the proposed method.
One-class SVM (OCSVM) is widely adopted as an effective method for fault detection, and its Gaussian kernel parameter directly influences its fault detection performance. However, the absence of fault samples in the training set makes it difficult to optimize this parameter. To solve this problem, a novel method of Gaussian kernel parameter optimization is proposed in this paper. This method first automatically selects edge and inner samples from the training set, and then optimizes the parameter through adjusting the distribution of the mappings of edge and inner samples in the feature space, so as to facilitate the building of OCSVM models. Moreover, this method needs not to train OCSVM models during the parameter optimization, which can save computational sources. The effectiveness of this proposed method is testified by experiments on 2D data sets and UCI data sets.