Stochastic Hybrid Models with Applications to Air Traffic Management

The ability to model, anal yze, simulate and verify realistic air traffic management conflict detection scenarios in a scalable, composable, multi -aircraft fashion is an extremely difficult endeavor. Verifiably accurate techniques for aircraft mode detection are critical in order t o enable the accurate projection of aircraft conflicts, and for the enactment of altitude separation resolution strategies. The introduction of a hybrid formalism encompassing hidden Markov modeling enables us to tune the discrete and continuous parameter s of a stochastic hybrid hidden Markov model (HHMM) in order to perform mode detection upon actual flight track data. Two different formats of air traffic data are used to tune the models, allowing for the refinement of the differential equations governin g the HHMM, thereby leading to an increased resolution capability, and resulting in a decreased latency for mode detection. The HHMM is evaluated using aircraft transitioning between level and ascending/descending flight in order to validate the effective ness of the model in detecting the switch between multiple flight modes for a given aircraft.

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