Patching C2n time series data holes using principal component analysis

Mark P. J. L. Chang; Haedeh Nazari; Carlos O. Font; G. Charmaine Gilbreath; Eun Oh

doi:10.1117/12.724706

4 May 2007 Patching C_n² time series data holes using principal component analysis

Mark P. J. L. Chang, Haedeh Nazari, Carlos O. Font, G. Charmaine Gilbreath, Eun Oh

Proceedings Volume 6551, Atmospheric Propagation IV; 65510L (2007) https://doi.org/10.1117/12.724706
Event: Defense and Security Symposium, 2007, Orlando, Florida, United States

Abstract

Measurements of C_n² time series using unattended commericial scintillometers over long time intervals inevitably lead to data drop-outs or degraded signals. We present a method using Principal Component Analysis (also known as Karhunen-Loève decomposition) that seeks to correct for these event-induced and mechanically-induced signal degradations. We report on the quality of the correction by examining the Intrinsic Mode Functions generated by Empirical Mode Decomposition.

Citation Download Citation

Mark P. J. L. Chang, Haedeh Nazari, Carlos O. Font, G. Charmaine Gilbreath, and Eun Oh "Patching C_n² time series data holes using principal component analysis", Proc. SPIE 6551, Atmospheric Propagation IV, 65510L (4 May 2007); https://doi.org/10.1117/12.724706

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