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SCIENTIFIC AND TECHNICAL AEROSPACE REPORTS

A Biweekly Publication of the National Aeronautics and Space Administration
VOLUME 44, ISSUE 12 - JUNE 20, 2006

NASA STAR REPORTS: 06/20/06
Astronautics

12 Astronautics (General)

13 Astrodynamics

15 Launch Vehicles and Launch Operations

17 Space, Communications, Spacecraft Communications, Command and Tracking

18 Spacecraft Design, Testing and Performance

19 Spacecraft Instrumentation and Astrionics

20 Spacecraft Propulsion and Power

17 SPACE COMMUNICATIONS, SPACECRAFT COMMUNICATIONS, COMMAND AND TRACKING
Includes space systems telemetry; space communications networks; astronavigation and guidance; and spacecraft radio blackout.

For related information see also 04 Aircraft Communications and Navigation; and 32 Communications and Radar.


20060013616 National Taiwan Ocean Univ., Keelung, Taiwan, Province of China

Precise GPS Orbit Determination and Prediction Using H-Infinity Neural Network

Hong, Jang-Lee; Journal of the Chinese Institute of Engineers Vol. 29, No. 2; March 2006, pp. 211-219; In English; See also



20060013612; Copyright; Avail.: Other Sources

In this paper, I present a method to determine and predict precisely the GPS satellite orbit by using a neural network. The neural network used in this paper is based on the BP (backpropagation) learning algorithm. The BP algorithm is particularly attractive because it is H-infinity optimal. It is a robust algorithm in the sense that small disturbances and modeling errors lead to small estimation errors (For a nonrobust algorithm, such as the classical maximum likelihood and least square methods, it is possible that small disturbances and modeling errors may result in large estimation errors). This is certainly the case for the estimation of the GPS satellite orbit because the satellite orbital model usually contains small disturbances and perturbations that are difficult to model. Currently, the simulation result shows that we can use the well-trained network to predict about six days data and the orbital will can be within a meter. The result is compared with the classical polynomial interpolation method. It is believed that, if we extend the training time, the prediction period can be much longer. Author

Global Positioning System; Neural Nets; H-Infinity Control; Orbit Determination; Maximum Likelihood Estimates; Error Analysis

Source: NASA


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