Bayesian approach to the design of chemical species tomography experiments

Samuel J. Grauer, Paul J. Hadwin, Kyle J. Daun

Research output: Contribution to journalArticlepeer-review

36 Scopus citations


Reconstruction accuracy in chemical species tomography depends strongly on the arrangement of optical paths transecting the imaging domain. Optimizing the path arrangement requires a scheme that can predict the quality of a proposed arrangement prior to measurement. This paper presents a new Bayesian method for scoring path arrangements based on the estimated a posteriori covariance matrix. This technique focuses on defining an objective function that incorporates the same a priori information about the flow needed to carry out limited data tomography. Constrained and unconstrained path optimization studies verify the predictive capabilities of the objective function, and that superior reconstruction quality is obtained with optimized path arrangements.

Original languageEnglish (US)
Pages (from-to)5772-5782
Number of pages11
JournalApplied optics
Issue number21
StatePublished - Jul 20 2016

All Science Journal Classification (ASJC) codes

  • Atomic and Molecular Physics, and Optics
  • Engineering (miscellaneous)
  • Electrical and Electronic Engineering


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