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Proceedings Paper

Time series prediction of nonlinear and nonstationary process modeling for ATR
Author(s): Andre Sokolnikov
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Paper Abstract

An algorithm is proposed for nonlinear and non-stationary processes concerning ATR. The general approach is to decompose a complex task into multiple domains in space and time based on predictability of the object modification dynamics. The model is composed of multiple modules, each of which consists of a state prediction model and correctional multivariate system. Prediction error function is used to weigh the outputs of multiple hierarchical levels.

Paper Details

Date Published: 23 May 2013
PDF: 10 pages
Proc. SPIE 8745, Signal Processing, Sensor Fusion, and Target Recognition XXII, 87451D (23 May 2013); doi: 10.1117/12.2017513
Show Author Affiliations
Andre Sokolnikov, Visual Solutions and Applications (United States)


Published in SPIE Proceedings Vol. 8745:
Signal Processing, Sensor Fusion, and Target Recognition XXII
Ivan Kadar, Editor(s)

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