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Fiber Optic Distributed Sensors for High-resolution Temperature Field Mapping
Published on: November 7, 2016
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Research on denoising method based on temperature and humidity profile lidar.
Bowen Zhang1,2, Guangqiang Fan3, Tianshu Zhang4
1Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, China.
Scientific Reports
|September 10, 2024
Summary
An improved data filtering method enhances lidar performance by reducing noise. This boosts signal quality, improving atmospheric measurements and detection range for temperature and humidity profiling.
Area of Science:
- Atmospheric Science
- Optical Remote Sensing
- Signal Processing
Background:
- Temperature and humidity profile lidar signals (vibration and pure rotational Raman) are significantly weaker than Mie scattering signals.
- Weak signals are prone to electronic and white noise, degrading the system's signal-to-noise ratio (SNR).
- Effective noise reduction is crucial for accurate atmospheric optical parameter inversion.
Purpose of the Study:
- To develop and validate an improved filtering method for denoising weak lidar signals.
- To enhance the signal-to-noise ratio and detection range of temperature and humidity profile lidar.
- To assess the algorithm's robustness and adaptability in real-world atmospheric conditions.
Main Methods:
- An improved Variational Mode Decomposition-Wavelet Transform (VMD-WT) filtering method was employed.
- Performance was evaluated against other filtering algorithms using simulated noisy signals.
- Quantitative metrics including SNR, root mean square error, and correlation were used for comparison.
- Experimental analysis was performed on continuously collected temperature and humidity lidar data.
Main Results:
- The improved VMD-WT algorithm demonstrated superior performance in enhancing signal-to-noise ratio compared to other methods.
- The algorithm effectively suppressed high-altitude noise and improved lidar detection range.
- Robust performance was observed even when processing strong interference signals, such as those from clouds.
- Significant improvements were noted in atmospheric optical parameter inversion results.
Conclusions:
- The enhanced VMD-WT filtering method offers a robust and adaptable solution for denoising weak lidar signals.
- This technique substantially improves the quality of atmospheric measurements, enabling more accurate profiling of temperature and humidity.
- The method shows significant potential for advancing atmospheric remote sensing applications.
