Methods for Aircraft State Estimation from Airport Surface Surveillance
Stephen Pledgie, Stephen Atkins, Christopher Brinton · 2009
Existing surveillance of aircraft on the airport surface includes significant errors that suggest infeasible vehicle motion. Current analysis of airport surface operations and automation systems based on this surveillance data frequently use the un-improved data with little regard for the effect of the noise on the analysis results or automation performance. This paper reports on an application of single and multiple-model filtering techniques to achieve improved state estimates for aircraft taxiing on an airport system. These state estimation methods demonstrated tremendous potential to provide more physically realistic aircraft state estimates and to significantly improve the performance of a threshold-based motion classification process. Our approach is suitable for either real-time or post-analysis applications.