Aircraft taxiway conformance monitoring with constrained stochastic linear hybrid systems
An algorithm, the modeled fault constrained innovation hybrid estimator (MF-CIHE), is proposed to quickly and correctly detect a nonconforming taxiing aircraft from a set of noisy measurements using estimation and fault detection techniques. Both spatial and temporal conformance monitoring are considered. The spatial conformance monitoring algorithm is based on formally modeling the aircraft and the assigned taxiway as a constrained stochastic linear hybrid system. Constrained stochastic linear hybrid systems (CSLHS) are stochastic linear hybrid systems that have state equality constraints on the dynamics of each mode. A proposed estimator, the constrained innovation hybrid estimator (CIHE), is derived in such a way that the expectation of the innovations of the mode-matched filters satisfy the constraints. Therefore, the constraint information in the innovation results in faster mode detection and smaller estimation errors than unconstrained hybrid estimators. It is proved that the CIHE is unbiased and produces residuals that have zero mean if the system is conforming to the model (which allows the estimator to be used for fault detection and identification (FDI)). Two residuals are generated by the estimator that are shown to distinguish between a discrete or constraint fault (or nonconformance). Temporal conformance monitoring is accomplished by projecting the state estimate of the aircraft into a coordinate frame that moves at the assigned speed of the aircraft. A nonconformance is detected when the coordinates of the aircraft in that frame exceed a design threshold. The proposed algorithms are validated using simulations of an aircraft at the Cleveland-Hopkins (CLE) airport. The simulation results show that the proposed algorithm has smaller estimation error than unconstrained estimators, that it can detect a nonconforming aircraft within two measurement sampling periods with small false alarm rates for a several different nonconforming scenarios, and that the temporal conformance monitoring algorithm is able to successfully detect a temporal nonconformance.
Hwang, Purdue University.
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