Interacting multiple model and probabilistic data association filter on radar tracking for ATM system

According to the International Civil Aviation Organization's plan, the Communications, Navigation, Surveillance, and Air Traffic Management (CNS/ATM) system based on the Global Navigation Satellite System (GNSS) technology should be implemented to replace the traditional Air Traffic Control (ATC) system based on ground-based radar. The major concern of the CNS/ATM system may experience service interruption when the GNSS signal blocked by either intentional or unintentional radio-frequency interference. To maintain the normal operation of the CNS/ATM system, a promising approach is to use the existing ground-based radar system to provide navigation and surveillance services for the ATM system. To utilize the existing radar system as a backup solution, the tracking capability of the radar system has to be enhanced to be compatible with standard GNSS positioning services. This study implements a tracking algorithm to improve the aircraft tracking performance of the radar system. The proposed tracking algorithm is called the Interacting Multiple Model and Probabilistic Data Association filter (IMMPDAF). The IMMPDAF can accurately track an aircraft in a cluttered environment under various maneuver modes. The procedure of implementing the IMMPDAF into a radar system is discussed. The tracking performance of the IMMPDAF is compared with that of a filter that uses the nearest neighbor method as well as the standard Kalman filter. Additionally, the computation loads of these filters are evaluated. Finally, real radar data collected by the Civil Aeronautics Administration of Taiwan are used to demonstrate the tracking performance improvement with the IMMPDAF.