Abstract
Rank annihilation-factor analysis is potentially the best method of analyzing fluorescence lidar returns because of the following capability. Rank annihilation can recognize a fluorescence signal of a component that is hidden by a large fluorescence background without a spectrum of that background. Theoretical models were developed to analyze the effectiveness of rank annihilation-factor analysis in the interpretation of lidar returns. Interferents such as background fluorescence, photon-counting noise, sky radiance, and atmospheric extinction degraded the lidar-return spectra in numerical simulations. The rank annihilation-factor analysis detection algorithm was most severely biased by the combination of photon-counting noise and sky radiance. Rank annihilation calculations were also compared with calculations done by two other detection algorithms: finding peak wavelengths and the least-squares technique. Rank annihilation is better than both techniques.
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