Time series prediction of nonlinear and nonstationary process modeling for ATR

Andre Sokolnikov

doi:10.1117/12.2017513

23 May 2013 Time series prediction of nonlinear and nonstationary process modeling for ATR

Andre Sokolnikov

Proceedings Volume 8745, Signal Processing, Sensor Fusion, and Target Recognition XXII; 87451D (2013) https://doi.org/10.1117/12.2017513
Event: SPIE Defense, Security, and Sensing, 2013, Baltimore, Maryland, United States

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.

Citation Download Citation

Andre Sokolnikov "Time series prediction of nonlinear and nonstationary process modeling for ATR", Proc. SPIE 8745, Signal Processing, Sensor Fusion, and Target Recognition XXII, 87451D (23 May 2013); https://doi.org/10.1117/12.2017513

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