An improved tracking method based on data mining in ADS-B for surface surveillance

ADS-B (Automatic Dependent Surveillance - Broadcast) is becoming one of the leading surveillance techniques in both en-route and surface surveillance. The responders in aircrafts broadcast their positions periodically. The measurement error of ADS-B is mainly determined by the responder. Therefore, this paper proposes a method based on data mining to estimate the measurement errors of responder in the en-route stage, and the mining result is used to better the tracking precision in the surface stage. Experimental results illustrate our improved tracking method with data mining logic is superior to the traditional method without measurement error estimation, and our method can achieve high precision.