Research Of ADS-B And Radar Integrated Surveillance Data Fusion Methods

In order to get accurate and reliable surveillance information of aircraft, requirements for precision and reliability of Air Traffic Control systems are increasing as the development of Civil Aviation. Based on ADS-B and radar surveillance systems, integrated multi-sensor surveillance data fusion methods are studied in this thesis. The system track for each target has been built through integrating surveillance data of ADS-B and different radar toward the same target, by which the ATC surveillance's coverage can be expanded, the precision and reliability can be improved and the airspace capacity can be increased. So the flight safety and the utilization of the airspace can be improved.In the thesis, the concepts of data fusion are briefly introduced firstly, and the characteristics of ADS-B system and radar system have been analyzed as well as their data. Secondly, several important problems about data fusion of ADS-B and radar are deeply researched, such as removing the outliers, coordinate conversions, alignment of time, correcting sensor biases and data association. Data association can use ADS-B ICAO address and SSR code firstly, and then use geometric distance to associate the data of radar and ADS-B measurements. Then, for the requirements of real-time performance and reliability in Civil Aviation ATC, a distributed data fusion model is built. And an adaptive Kalman filtering algorithm based on current statistical model are chosen for Civil Aviation aircraft tracking, through analyzing and comparing the relevant algorithms. For the characteristics of ADS-B information, a current statistical model algorithm based on ADS-B intent information was proposed. Finally, an optimal weighted filtering covariance matrix data fusing method is proposed for system tracks fusion. Computer simulation results show that the proposed methods improve the tracking accuracy effectively.