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2014
Conference Paper
Titel
A frequentistic and a bayesian approach for optimal optical filter design
Abstract
This report discusses three merit functions to optimize optical interference filter coatings. The applications of these filters are intentionally optical 3D sensors, e.g. a chromatic confocal triangulation sensor. Optimizing these optical filters is done by minimizing the measurement uncertainty of the sensor. The measurement task is handled as a parameter estimation problem and the sensor is considered as a physical experiment. As part of the experimental design, the optical filters are optimized to achieve measurements with lower uncertainty. The first merit function is based on a frequentistic statistic utilizing the Cramér-Rao lower bound. An example is used to point out disadvantages and two alternative merit functions are proposed. Instead of a lower bound, the other merit functions incorporate a specific estimator function.