Networked differential GPS system

An embodiment of the present invention relates to a worldwide network of differential GPS reference stations (NDGPS) that continually track the entire GPS satellite constellation and provide interpolations of reference station corrections tailored for particular user locations between the reference stations Each reference station takes real-time ionospheric measurements with codeless cross-correlating dual-frequency carrier GPS receivers and computes real-time orbit ephemerides independently. An absolute pseudorange correction (PRC) is defined for each satellite as a function of a particular user's location. A map of the function is constructed, with iso-PRC contours. The network measures the PRCs at a few points, so-called reference stations and constructs an iso-PRC map for each satellite. Corrections are interpolated for each user's site on a subscription basis. The data bandwidths are kept to a minimum by transmitting information that cannot be obtained directly by the user and by updating information by classes and according to how quickly each class of data goes stale given the realities of the GPS system. Sub-decimeter-level kinematic accuracy over a given area is accomplished by establishing a mini-fiducial network

Effect of Uncertainty on En Route Descent Advisor (EDA) Predictions

The En Route Descent Advisor (EDA) is one of the Center TRACON Automation System (CTAS) decision support tools under development at the NASA Ames Research Center. EDA generates maneuver advisories for arrival aircraft to meet scheduled arrival times at the arrival meter fix, sometimes 20 – 25 minutes ahead of the aircraft’s scheduled meter fix arrival time. This work determined the sensistivity of the EDA advisories to system uncertainties, including initial condition, environmental, and aircraft performance data errors. Using a Monte Carlo simulation that incorporates a Matlab Trajectory Synthesizer (TS) simulation, the sensitivities of the EDA predicted trajectory to these data error sources were obtained. The key metric is the meter fix crossing time error since this metric directly measures the performance of EDA. This performance analysis involved a minimum of 200 Monte Carlo trials per error parameter. In addition to the single aircraft performance analysis, the impact of aircraft prediction errors on conflict detection between closely-spaced aircraft was also explored. These Monte Carlo performance analyses determined how robust the EDA advisories are to input parameter uncertainties. Nomenclature g = gravitational acceleration constant h = altitude m = aircraft mass q = dynamic pressure s = distance t = time x, y = east and north position γTAS, γi = aerodynamic and inertial flight path angle Ψ = heading VTAS = true airspee