Noise contaminated transmittance

Andrew Zardecki; Brian D. McVey; Douglas H. Nelson; Mark J. Schmitt

doi:10.1117/12.279068

31 October 1997 Noise-contaminated transmittance

Andrew Zardecki, Brian D. McVey, Douglas H. Nelson, Mark J. Schmitt

Author Affiliations +

Proceedings Volume 3127, Application of Lidar to Current Atmospheric Topics II; (1997) https://doi.org/10.1117/12.279068
Event: Optical Science, Engineering and Instrumentation '97, 1997, San Diego, CA, United States

Abstract

We compare the efficiency of a classifier based on probabilistic neural networks and the general least squares method. Both methods must accommodate noise due to uncertainty in the measured spectrum. The evaluation of both methods is based on a simulated transmittance spectrum, in which the received signal is supplemented by an additive admixture of noise. To obtain a realistic description of the noise mode, we generate several hundred laser pulses for each wavelength under consideration. These pulses have a predetermined correlation matrix for different wavelengths; furthermore, they are composed of three components accounting for the randomness of the observed spectrum. The first component is the correlated 1/f noise; the second component is due to uncorrelated 1/f noise; the third one is the uncorrelated white noise. The probabilistic neural network fails to retrieve the species concentration correctly for large noise levels; on the other hand, its predictions being confined to a fixed number of concentration bins, the network produces relatively small variances. To a large extent, the general least square method avoids the false alarms. It reproduces the average concentrations correctly; however, the concentration variances can be large.

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Andrew Zardecki, Brian D. McVey, Douglas H. Nelson, and Mark J. Schmitt "Noise-contaminated transmittance", Proc. SPIE 3127, Application of Lidar to Current Atmospheric Topics II, (31 October 1997); https://doi.org/10.1117/12.279068

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