Data fusion of secondary and primary surveillance radars for increased robustness in air-traffic monitoring

The paper presents methods for increasing robustness of civil air-traffic monitoring systems, with secondary surveillance radars (SSR) and primary radars, against intentional jamming. Jamming could cause large problems to civil air traffic from an economically point of view as well as increase the risk for serious accidents. The methods are based on a sensor network and data fusion. In a sensor network false objects do not appear simultaneously in different sensors. Consequently, data fusion methods can be used to reveal false objects. The data fusion methods discussed are; statistical distance, covariance intersection and hidden Markov models (HMM). The statistical distance is used to associate data from different sensors. The covariance intersection is used to fuse data from different sensors. HMM is used to describe the behaviour of an aircraft. The paper discusses the use of these methods on this application and presents some simulation results.