Airspace/Airport Conformance Monitoring

Conformance Monitoring in Airspace

Conformance monitoring in air traffic control is an approach that is used to detect any deviations of the aircraft from its cleared (or planned) flight path that might compromise safety and efficiency. This task is made difficult by the fact that some deviations are normally expected of a conforming aircraft due to uncertainties.

In this paper, a conformance monitoring algorithm that uses a stochastic linear hybrid system (SLHS) model is proposed. The SLHS model divides the aircraft dynamics into a number of flight modes and is able to describe the random deviations in (conforming) aircraft trajectories.

The detection of any nonconforming trajectories is determined by a fault detection technique.

Model-based fault detection concept

  • First, a hybrid estimation algorithm is used to estimate the hybrid state of the SLHS and generate a residual vector based on the measurements. The statistical characteristics of the residual expected of a conforming aircraft are derived.
  • Then, a statistical decision test is used to detect any deviation of the observed residual from the expected residual.
  • Left: Typical plot of a residual generated without modeling trajectory deviations, due to uncertainties during turns
  • Right: Typical plot of a residual generated by the SDTHE algorithm, which accounts for trajectory deviations. The residual for a conforming aircraft does not significantly deviate from a zero mean during a turn

Two illustrative examples are presented to demonstrate the performance of the proposed algorithm (Please find the detailed results in [1]).

Simulated trajectories in the horizontal plane

Simulated trajectories in the vertical plane

Conformance Monitoring in Taxiway

A taxiway conformance-monitoring algorithm is proposed using constrained stochastic linear hybrid systems (SLHS), a mathematical modeling method that models a system as a set of modes with different constrained continuous dynamic models, with random switching between the modes to model conforming and nonconforming (to the assigned taxi route) motion of an aircraft on a taxiway.

A state estimator for each constrained SLHS generates a residual that indicates the conformance of the aircraft to the respective model.

  • A spatial non-conformance is detected by posing statistical tests to determine which of the models the aircraft is mostly likely following. The state estimator using the hybrid model produces an accurate estimate that is projected into a coordinate frame that moves with the modeled aircraft position.
  • Information about the temporal conformance of the aircraft is found from the coordinates of the aircraft in this frame.

Overview of conformance-monitoring algorithm

The algorithm is tested with simulations from four different nonconforming scenarios from the Cleveland–Hopkins airport.

Cleveland-Hopkins Airport diagram showing the departing taxi route and the modeled waypoint and guard condition types

Four nonconforming scenarios from the CLE airport

  • The results from the proposed algorithm are compared with results from four algorithms:
    • an algorithm that models only the conforming aircraft as a constrained stochastic linear hybrid system,
    • two corresponding unconstrained detection algorithms, and
    • a position-based conformance-monitoring algorithm representing the current state of the art.
  • The comparison demonstrates that the proposed algorithm significantly improves the mean detection time, reducing the detection time by up to 7 s compared with the baseline with small (less than 10%) false alarm rates (Please find the detailed results in [2]).

Related Publications

  1. C.-E. Seah, A. Aligawesa, and I. Hwang, "An Algorithm for Conformance Monitoring in Air Traffic Control," AIAA Journal of Guidance, Control and Dynamics, Vol. 33(2), March-April 2010
  2. G. Mann and I. Hwang, "Four-Dimensional Aircraft Taxiway Conformance Monitoring with Constrained Stochastic Linear Hybrid Systems," AIAA Journal of Guidance, Control and Dynamics, Vol.35(5), p1593-1604, September-October 2012