An experimental validation of the Gauss-Markov model for nonuniformity noise in infrared focal plane array sensors

Octavio Zapata; Felipe Pedreros; Sergio N. Torres

doi:10.1117/12.919367

18 May 2012 An experimental validation of the Gauss-Markov model for nonuniformity noise in infrared focal plane array sensors

Octavio Zapata, Felipe Pedreros, Sergio N. Torres

Author Affiliations +

Proceedings Volume 8355, Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XXIII; 83551G (2012) https://doi.org/10.1117/12.919367
Event: SPIE Defense, Security, and Sensing, 2012, Baltimore, Maryland, United States

Abstract

The aim of this research is to experimentally validate a Gauss-Markov model, previously developed by our group, for the non-uniformity parameters of infrared (IR) focal plane arrays (FPAs). The Gauss-Markov model assumed that both, the gain and the offset parameters at each detector, are random state-variables modeled by a recursive discrete-time process. For simplicity, however, we have regarded here the gain parameter as a constant and assumed that solely the offset parameter follows a Gauss-Markov model. Experiments have been conducted at room temperature and IR data was collected from black-body radiator sources using microbolometer-based IR cameras operating in the 8 to 12 μm. Next, well-known statistical techniques were used to analyze the offset time series and determinate whether the Gauss-Markov model truly fits the temporal dynamics of the offset. The validity of the Gauss-Markov model for the offset parameter was tested at two time scales: seconds and minutes. It is worth mentioning that the statistical analysis conducted in this work is a key in providing mechanisms for capturing the drift in the fixed pattern noise parameters.

Citation Download Citation

Octavio Zapata, Felipe Pedreros, and Sergio N. Torres "An experimental validation of the Gauss-Markov model for nonuniformity noise in infrared focal plane array sensors", Proc. SPIE 8355, Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XXIII, 83551G (18 May 2012); https://doi.org/10.1117/12.919367

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