A Radar Intelligence Simulation Method Based on Data Mining Techniques

Abstract

A new method to generate radar air intelligent information by using data mining techniques based on historical radar data is proposed. This method has two stages: One is “filtering separation - piecewise fitting - feature clustering". In this stage, the radar historical data is divided into the actual true track and noise. Through computing the second-order discrete curvature, the actual true track is decomposed into several segments, such as straight line and arc, which are fitted with multinomial subsequently. On this basis, after analyzing the characteristic vector of radar historical data, the clustering database is established; the other is “feature association-track recombination”. The track in pre-deigned air scenario is segmented by the second-order discrete curvature. After the correlative feature information of the segmented scenario is searched, matched and associated with the information in clustering database, a new track will be restructured by using this output results. This method is very available for its effective application in simulation test-bed of C3I system.