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Determination of overlap in lidar systems
Joshua Vande Hey1, Jeremy Coupland, Ming Hui Foo
1Wolfson School of Mechanical and Manufacturing Engineering, Loughborough University, Ashby Road, Loughborough, Leicestershire, LE11 3TU, UK. mmjdv@lboro.ac.uk
Applied Optics
|October 22, 2011
Summary
This study introduces a novel method to measure lidar overlap using a virtual cloud and imaging system. The technique accurately determines the lidar
Area of Science:
- Atmospheric Science
- Optical Engineering
- Remote Sensing
Background:
- Lidar (light detection and ranging) measurements are crucial for atmospheric research and remote sensing.
- The overlap profile, also termed crossover function or geometric form factor, is a key parameter influencing lidar data accuracy.
- Uncertainty in overlap profile determination can lead to significant errors in lidar-derived geophysical parameters.
Purpose of the Study:
- To develop and validate a new method for precisely measuring the lidar overlap profile.
- To reduce uncertainty associated with lidar measurements by accurately characterizing the geometric form factor.
- To provide a reliable technique for overlap profile assessment in lidar systems.
Main Methods:
- A novel approach utilizing an imaging system to present a virtual cloud to the lidar.
- Characterization of the lidar's geometric form factor through controlled virtual target presentation.
- Comparison of results with established measurement techniques, including horizontal hard target lidar measurements.
Main Results:
- The developed method provides accurate measurements of the lidar overlap profile.
- Results demonstrate good agreement with traditional horizontal hard target lidar measurements.
- Validation against geometric overlap calculations for an ideal aberration-free lidar system.
Conclusions:
- The virtual cloud method offers a robust and accurate means for lidar overlap profile measurement.
- This technique effectively mitigates uncertainty in lidar measurements stemming from overlap profile variations.
- The findings support the adoption of this imaging-based approach for routine lidar calibration and validation.
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