Passive Wind Velocity Measurement via Fourier Transform
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Solution Overview
Problem
Existing passive wind measurement methods, such as those described in U.S. Pat. No. 5,469,250, tend to be inaccurate due to picking up artifacts unrelated to wind velocity, particularly in turbulent air conditions where eddies with different refractive indices affect light rays, causing distortion and glare.
Innovation Solution
A novel passive method using a Fourier Transform (FT) and correlation between wave vector number and temporal frequency to measure wind velocity, specifically filtering out internal structure changes of turbulence eddies and focusing on frequencies related to wind velocity, employing a 'Freezed Turbulence' model that approximates the movement of eddies to calculate perpendicular wind velocity components through image distortion analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If passive wind measurement methods detect all light intensity variations, then more data is available for analysis, but artifacts unrelated to wind velocity are included reducing accuracy
Solution Approach 1:
The patent extracts only the relevant information (wind-related frequencies) from the total light intensity variations by using Fourier Transform to separate different frequency components. The correlation between wave vector number and temporal frequency allows isolation of wind-induced variations from other artifacts, effectively extracting the useful signal while discarding irrelevant information.
Solution Approach 2:
The patent changes the parameter of analysis from raw light intensity values to frequency domain representation through Fourier Transform. By analyzing temporal frequencies and their correlation with wave vector numbers, the method transforms the measurement approach to selectively identify wind-related signals based on their characteristic frequency signatures rather than treating all variations equally.
2Measurement precision
If the measurement method analyzes all frequency components, then complete turbulence information is captured, but internal structure changes of eddies interfere with wind velocity detection
Solution Approach 1:
The patent extracts only the specific frequency components that are correlated with wind velocity through the relationship between wave vector number and temporal frequency. By using Fourier Transform to decompose the signal and then selecting only those frequency components that exhibit the characteristic correlation with spatial variations, the method separates wind-related signals from those caused by internal turbulence structure changes.
Solution Approach 2:
The patent applies different analysis quality to different frequency components. Rather than treating all frequency components uniformly, the method identifies and applies specific processing (correlation analysis with wave vector number) only to those frequency components that contain wind velocity information, while treating or filtering out components related to internal turbulence dynamics.
3Measurement precision
If the method uses complex signal processing to eliminate artifacts, then measurement accuracy improves, but computational complexity increases
Solution Approach 1:
The patent replaces complex physical filtering mechanisms with mathematical signal processing. Instead of using physical devices to separate wind-related signals from artifacts, the method uses Fourier Transform and correlation analysis to achieve the separation computationally, substituting mechanical/optical complexity with algorithmic processing that can be implemented in software.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This method provides more accurate wind velocity measurements by isolating wind-related frequencies and eliminating artifacts, utilizing discrete Fourier Transform (DFT) to correlate perturbation size and time changes in images affected by atmospheric turbulence, resulting in improved precision and reliability.
Implementation Method 1
The turbulence eddies have a different density than its surround air and therefore somewhat different index of refraction. The effect of these eddies, with different indices of refraction, on light rays passing through them, is that the eddies act as weak lenses.
Data Source
AI summary
A method for measuring wind velocity including using a Fourier Transform (FT) and a correlation between a wave vector number and temporal frequency of a wind to calculate wind velocity. Contour line deviations in a series of images of a far object are evaluated by performing a spatial discrete fourier transform (DFT) on deviations within each image, and subsequently a time discrete fourier transform (DFT) on the Fourier coefficients obtained by the spatial DFT, to get the frequency dependence of each Fourier coefficient.