SPACE INSTRUMENT NEURAL-NETWORK FOR REAL-TIME DATA-ANALYSIS

Space instruments generate vast quantities of data that often require immmediate analysis. A simple software implementation of an artificial neural network (ANN) was used to analyze up to 200 autocorrelation functions (ACF) per second within the Shuttle Potential and Return Electron Experiment (SPREE) flown on the Shuttle STS46 mission, July 31, IM. As all ACF data are stored onboard until post mission, this facility provided ground based experimenters with their only access to ACF data in real time for optimum instrument control. ACF's concerned 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 while data interpretation performance is comparable to that achieved by an expert data analyst. Erroneous analysis accounts for less than one percent of date analyzed.