Estimation fusion with radar and ADS-B for air traffic surveillance

The surveillance sensor that has been mainly used for target tracking in air traffic control (ATC) environment is radar. The automatic dependent surveillance - broadcasting (ADS-B), which is based on the technologies of global navigation satellite systems (GNSS), is recently participating in ATC systems for more accurate and reliable surveillance. Although ADS-B provides more accurate and a wider variety of measurements than does radar, it needs careful considerations for the application of the ATC systems. This is due to the fact that the reliability of ADS-B measurements is dependent upon each aircraft whereas that of radar is not. This study proposes a practical system for the estimation fusion of multiple heterogeneous sensors, which includes radar and ADS-B, whose measurements and sensor characteristics are different from one another. A centralized fusion architecture for the data that are received asynchronously from multiple sensors is adopted for the optimum estimation fusion. The three-dimensional earth-centered earth-fixed (ECEF) coordinate system is used for the common coordinates for preprocessing and filtering in order to avoid the registration errors, which can occur in two-dimensional coordinates with a stereographic projection at the system center. In case of the ADS-B, the validity of the sensor data for each aircraft is primarily checked using the accuracy and integrity information of the aircraft. Another important validation for the ADS-B is performed using a comparison of the ADS-B data with the radar data because the ADS-B validation information from each aircraft may not be fully reliable. Furthermore, additional measurements from the downlinked aircraft parameters (DAP), which are available in mode-S radar and ADS-B, are incorporated into state estimation. A variable-sized measurement vector and matrix are used for the tracking filter in order to dynamically reflect the availability of the additional measurements. The simulation results indicate that the proposed fusion system can improve the tracking performance with the advantages of different types of surveillance sensors.