Track-to-airline association based on multi-feature reasoning

Considering track classification problem, the application of complex reasoning in the multi-feature track decision is studied. Firstly, according to the requirements of air traffic control system for airway and flight, an association model of track-to-airline is developed. Secondly, similarities between target features(position, direction) and information of known tracks are computed, basic belief assignments are constructed and then the target single feature classification results are obtained by fusion. The introduction of meta-class brings out the generalized credit classification for targets class. A multi-feature discount method is developed, giving the discount on features' basic belief assignments before fusion to get the target multi-feature classification results. The simulations and the test on real data of air traffic control system show that the method not only make the track classification, but also decrease the fault rate of classification.