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Proceedings Paper

Sensor fusion and nonlinear prediction for anomalous event detection
Author(s): Jose N.V. Hernandez; Kurt R. Moore; Richard C. Elphic
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Paper Abstract

We consider the problem of using the information from two time series, each characterizing a different physical quantity, to predict the future state of the system and, based on that information, to detect and classify anomalous events. We stress the application of principal components analysis (PCA) to analyze and combine data from the different sensors. We construct both linear and nonlinear predictors. In particular, for linear prediction we use the least-mean-square (LMS) algorithm and for nonlinear prediction we use both back-propagation (BP) networks and fuzzy predictors (FP). As an application, we consider the prediction of gamma counts from past values of electron and gamma counts recorded by the instruments of a high altitude satellite.

Paper Details

Date Published: 5 July 1995
PDF: 11 pages
Proc. SPIE 2484, Signal Processing, Sensor Fusion, and Target Recognition IV, (5 July 1995); doi: 10.1117/12.213065
Show Author Affiliations
Jose N.V. Hernandez, Los Alamos National Lab. (United States)
Kurt R. Moore, Los Alamos National Lab. (United States)
Richard C. Elphic, Los Alamos National Lab. (United States)


Published in SPIE Proceedings Vol. 2484:
Signal Processing, Sensor Fusion, and Target Recognition IV
Ivan Kadar; Vibeke Libby, Editor(s)

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