Space instrument neural network for real-time data analysis

Author

Gough, M.P.

Author_Institution

Sch. of Eng., Sussex Univ., Brighton

Volume

31

Issue

6

fYear

1993

fDate

11/1/1993 12:00:00 AM

Firstpage

1264

Lastpage

1268

Abstract

A simple software implementation of an artificial neural network (ANN) was used to analyze up to 200 autocorrelation functions (ACFs) per second within the Shuttle Potential and Return Electron Experiment (SPREE) flown on the Shuttle STS46 mission, July 31, 1992. As all ACF data are stored onboard until postmission, this facility provided ground-based experimenters with their only access to ACF data in real time for optimum instrument control. ACFs contain data either as waveforms or as radar echoes. Operating directly on the ACF, the neural network identifies the type of data, ascertains the wave frequency or radar peak separation, and provides a score or measure of significance of its decision. An effective 16:1 data reduction is achieved and the data interpretation performance is comparable to that achieved by an expert data analyst. Erroneous analysis accounts for less than 1% of data analyzed

Keywords

data analysis; data reduction; geophysical techniques; geophysics computing; ionospheric techniques; neural nets; real-time systems; remote sensing; remote sensing by radar; ACF; SPREE; Shuttle Potential and Return Electron Experiment; artificial neural network; autocorrelation function; data reduction; decision; geophysics; ionosphere; measurement technique; radar echo; radar peak separation; real-time data analysis; satellite instrumentation; score; significance; software; space instrument neural network; Artificial neural networks; Autocorrelation; Data analysis; Electrons; Frequency measurement; Instruments; Neural networks; Performance analysis; Radar measurements; Space shuttles;

fLanguage

English

Journal_Title

Geoscience and Remote Sensing, IEEE Transactions on

Publisher

ieee

ISSN

0196-2892

Type

jour

DOI

10.1109/36.317435

Filename

317435