Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Optimal method of linear regression in laser remote sensing.

Sergei N Volkov1, Bruno V Kaul, Dmitri I Shelefontuk

  • 1Institute of Atmospheric Optics, Siberian Branch of the Russian Academy of Sciences, Tomsk. snvolk@iao.ru

Applied Optics
|September 6, 2002
PubMed
Summary

We developed an optimal linear regression method to improve atmospheric data accuracy from lidar measurements. This method effectively reconstructs atmospheric parameters, even with noisy signals, enhancing data reliability.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

The AM-4 Family of Layered Titanosilicates: Single-Crystal-to-Single-Crystal Transformation, Synthesis and Ionic Conductivity.

Materials (Basel, Switzerland)·2024
Same author

Investigation of East Asian clouds with polarization light detection and ranging.

Applied optics·2015
Same author

Raman and fluorescent scattering matrix of spherical microparticles.

Applied optics·2011
Same author

Observations of specular reflective particles and layers in crystal clouds.

Optics express·2011
Same author

Investigating particle orientation in cirrus clouds by measuring backscattering phase matrices with lidar.

Applied optics·2005

Area of Science:

  • Atmospheric Science
  • Optical Remote Sensing

Background:

  • Photon-counting lidar is standard for atmospheric studies.
  • Signal processing challenges arise from inhomogeneous noise in lidar measurements.

Purpose of the Study:

  • To introduce an optimal method of linear regression (OMLR) for signal processing in lidar measurements.
  • To estimate the accuracy of OMLR in reconstructing atmospheric parameters.
  • To demonstrate the application of OMLR for temperature profile reconstruction using Raman lidar data.

Main Methods:

  • Optimal Method of Linear Regression (OMLR) for signal processing.
  • Accuracy estimation of the OMLR method.
  • Application to Raman lidar data for temperature profile reconstruction.

Related Experiment Videos

Main Results:

  • The OMLR method provides accurate reconstruction of atmospheric parameters from lidar signals.
  • The method demonstrates effectiveness even in the presence of inhomogeneous noise.
  • Successful application to reconstruct temperature profiles from real-world lidar data.

Conclusions:

  • The proposed OMLR method is a statistically sound and efficient tool for processing lidar measurement data.
  • It simplifies the interpretation of criteria used in signal processing.
  • OMLR enhances the accuracy of atmospheric parameter reconstruction from noisy lidar signals.