In this proposal, a real time bias estimation system for an airport surveillance data fusion system is presented. This bias estimation system is divided in two main parts. The first part estimates SMR bias terms, taking advantage of the knowledge of the airport map, which is useful because aircraft usually follow the axis of airport taxiways. The other part makes use of SMR corrected measures, which can be assumed to be unbiased. Using them, bias estimators for other important surface surveillance sensors are defined. These estimators are based on processing differences of measurement taken from each sensor and from the SMR. As simulation results show, if the sensor error models are precise enough, both estimations converge to the real bias values, and therefore unbiased measures may be obtained. These unbiased measurements should be provided to the fusion system, in order to enhance tracking performance. These estimation processes do not represent an important computer load increase for the data fusion system. The performance improvement in tracking is also presented.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.