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

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.

Citation Download Citation

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

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available