Sensor fusion and nonlinear prediction for anomalous event detection

Jose N.V. Hernandez; Kurt R. Moore; Richard C. Elphic

doi:10.1117/12.213065

5 July 1995 Sensor fusion and nonlinear prediction for anomalous event detection

Jose N.V. Hernandez, Kurt R. Moore, Richard C. Elphic

Proceedings Volume 2484, Signal Processing, Sensor Fusion, and Target Recognition IV; (1995) https://doi.org/10.1117/12.213065
Event: SPIE's 1995 Symposium on OE/Aerospace Sensing and Dual Use Photonics, 1995, Orlando, FL, United States

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.

Citation Download Citation

Jose N.V. Hernandez, Kurt R. Moore, and Richard C. Elphic "Sensor fusion and nonlinear prediction for anomalous event detection", Proc. SPIE 2484, Signal Processing, Sensor Fusion, and Target Recognition IV, (5 July 1995); https://doi.org/10.1117/12.213065

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