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Accurate Turbulence Measurement with a Passive Monitoring System

Introduction

Turbulence is a critical factor in various fields, from aviation safety to environmental monitoring. However, measuring the strength of turbulence accurately and efficiently has long been a challenge, requiring complex, often invasive systems that disrupt the environment or operational flow. Our patented passive method to measure the strength of turbulence offers an innovative, non-intrusive solution. By utilizing passive techniques, this system provides precise measurements of turbulence strength in real-time, without the need for disruptive equipment or interference with surrounding conditions. This technology represents a significant advancement for industries where understanding turbulence is key to safety, efficiency, and scientific discovery.

Current Limitations in Turbulence Measurement

In aviation, turbulence poses a significant safety risk to aircraft, making it essential to monitor and predict turbulence with precision. Traditional methods often rely on active detection systems such as radar, which can be expensive, invasive, and limited in range. Similarly, in environmental research, measuring turbulence in atmospheric conditions or oceanic currents is a critical task for understanding weather patterns, climate change, and fluid dynamics. Yet, the tools available today are often intrusive, require complex installations, or disrupt the natural flow of air or water being measured.

For industries that depend on turbulence data—whether to improve safety, optimize performance, or conduct research—there is a need for a more efficient, cost-effective solution that provides accurate data without introducing significant disruptions or costs.

A Passive Solution for Precision Turbulence Measurement

Our patented system offers a passive method to measure turbulence strength, leveraging environmental data and natural signals to gauge turbulence intensity without the need for invasive or active detection methods. This system allows for continuous monitoring of turbulence, providing valuable real-time insights across various environments. Its passive nature means that it can be deployed in sensitive or hard-to-reach locations—such as high altitudes or deep ocean environments—without interfering with the surrounding conditions.

In aviation, this technology can be integrated into aircraft to improve onboard turbulence monitoring, contributing to safer flights by allowing pilots and systems to react more quickly to turbulence changes. In meteorology, it provides a tool for more accurate modeling of atmospheric disturbances, enhancing weather prediction models. Meanwhile, in oceanography, this system can help researchers better understand oceanic turbulence and its effects on marine ecosystems, offering new opportunities for scientific advancement.

Key Benefits

  • Non-Intrusive Monitoring: Provides accurate turbulence measurements without interfering with the environment or operational flow.
  • Real-Time Data: Delivers continuous, real-time data on turbulence strength, allowing for timely reactions and decision-making.
  • Versatile Applications: Suitable for use in aviation, meteorology, oceanography, and industrial fluid dynamics.
  • Cost-Effective: Reduces the need for expensive active detection systems, offering a more affordable solution for measuring turbulence.

A Smart Approach to Turbulence Monitoring Across Industries

Licensing this passive method to measure turbulence strength offers companies and researchers in aviation, environmental science, and oceanography a powerful tool for improving safety, efficiency, and scientific understanding. By providing an accurate, non-invasive way to monitor turbulence, this technology can significantly enhance performance and decision-making in critical operations.

Please see terms and conditions
Disclosed is a method to passively measure and calculate the strength of turbulence via the index of refraction structure constant Cn 2 from video imagery gathered by an imaging device, such as a video camera. Processing may occur with any type computing device utilizing a processor executing machine executable code stored on memory. This method significantly simplifies instrumentation requirements, reduces cost, and provides rapid data output. This method combines an angle of arrival methodology, which provides scale factors, with a new spatial/temporal frequency domain method. As part of the development process, video imagery from high speed cameras was collected and analyzed. The data was decimated to video rates such that statistics could be computed and used to confirm that this passive method accurately characterizes the atmospheric turbulence. Cn 2 accuracy from this method compared well with scintillometer data through two full orders of magnitude and more capability is expected beyond this verification.

What is claimed is:

1. A method for determining turbulence information for an atmospheric distance comprising:

obtaining image data with an image capture device, the image data representing an image of a scene;
performing spatial/temporal spectrum characterization processing on at least a portion of the image data to generate spatial/temporal spectrum characterization turbulence data;
performing high confidence block shift processing on at least a portion of the image data to generate high confidence block shift turbulence data; and
combining the spatial/temporal spectrum characterization turbulence data and the high confidence block shift turbulence data to calculate the turbulence information.
2. The method of claim 1 wherein the image data is an array of pixel data from twenty or more frames of successive image data from the image capture device.
3. The method of claim 1 wherein the image capture device is a video camera.
4. The method of claim 1 wherein the method is passive, using only image data captured at a location of the image capture device without a remote optic signal transmitter.

5. The method of claim 1 wherein spatial/temporal spectrum characterization processing includes:

calculate or receive a scale factor based on system parameters;
reading image data to create an image stack;
calculate variance data over time for pixels in the image stack;
perform a Fourier transform on the variance data to generate power spectral density data;
average the power spectral density data to obtain average power spectral density data;

process the average power spectral density data in relation to a Kolmogorov spectrum to obtain a value proportional to the index of refraction structure constant (cn 2); and

multiply the index of refraction structure constant (cn 2) by the scale factor to generate the spatial/temporal spectrum characterization data.

6. The method of claim 1 wherein high confidence block shift processing includes:

process image data to create image data blocks;
calculate an information content metric for one or more image data blocks;
track information content metric for one or more image data blocks over time;
record or designate block tracks which meet a predetermined threshold as surviving blocks;
calculate pixel variance value for surviving blocks related to cn 2;
calculate platform motion using differential frame motion, for one or more pixel variance values, calculate mean variance from one or more sets of pixel variance values to determine the index of refraction structure constant (cn 2); and
calculate the high confidence block shift turbulence data based on platform movement and (cn 2);.
7. The method of claim 1 wherein combining the spatial/temporal spectrum characterization turbulence data and the high confidence block shift turbulence data includes rescaling the spatial/temporal spectrum characterization turbulence data based on an average of a ratio of the high confidence block shift turbulence data and spatial/temporal spectrum characterization data for a period of time.
8. The method of claim 1 wherein further comprising processing the image data utilizing the turbulence data to remove aberrations in the image caused by the turbulence over the atmospheric distance.

9. A computer-readable medium having non-transitory computer-executable instructions for determining turbulence information for an atmospheric distance, wherein the computer executable instructions are executed on a processor and comprise the steps of:

obtaining image data with an image capture device, the image data representing an image of a scene over the atmospheric distance;
performing spatial/temporal spectrum characterization processing on at least a portion of the image data to generate spatial/temporal spectrum characterization data;
performing high confidence block shift processing on at least a portion of the image data to generate high confidence block shift turbulence data; and
combining the spatial/temporal spectrum characterization turbulence data and the high confidence block shift turbulence data to calculate the turbulence information for the atmospheric distance.
10. The computer-readable medium of claim 9, wherein the image data is an array of pixel data from twenty or more frames of successive image data from the image capture device.
11. The computer-readable medium of claim 9, wherein the image capture device is a video camera.
12. The computer-readable medium of claim 9, wherein the method is passive, using only image data captured at a location of the image capture device without a remote optic signal transmitter.

13. The computer-readable medium of claim 9, wherein high confidence block shift processing includes:

process image data to create image data blocks;
calculate an information content metric for one or more image data blocks;
track information content metric for one or more image data blocks over time;
record or designate block tracks which meet a predetermined threshold as surviving blocks;
calculate pixel variance value for surviving blocks related to cn 2;
calculate platform motion using differential frame motion;
for one or more pixel variance values, calculate mean variance from one or more sets of pixel variance values to determine the index of refraction structure constant (cn 2); and
calculate the high confidence block shift turbulence data based on platform movement and (cn 2).
14. The computer-readable medium of claim 9, wherein combining the spatial/temporal spectrum characterization turbulence data and the high confidence block shift turbulence data includes rescaling the spatial/temporal spectrum characterization turbulence data based on an average of a ratio of the high confidence block shift turbulence data and spatial/temporal spectrum characterization data for a period of time.
15. The computer-readable medium of claim 9, further comprising processing the image data utilizing the turbulence data to remove aberrations in the image caused by the turbulence over the atmospheric distance.

16. A system for determining turbulence information for an atmospheric distance, the system comprising:

an image capture device configured to capture frames of image data;
a processor configured to execute computer-executable instructions based on the image data to calculate turbulence data; and
a memory storing non-transitory computer-executable instructions for determining turbulence information for an atmospheric distance, wherein the computer-executable instructions are executed on the processor and comprise the steps of:
obtaining image data with an image capture device, the image data representing an image of a scene over the atmospheric distance;
performing spatial/temporal spectrum characterization processing on at least a portion of the image data to generate spatial/temporal spectrum characterization data;
performing high confidence block shift processing on at least a portion of the image data to generate high confidence block shift turbulence data; and
combining the spatial/temporal spectrum characterization turbulence data and the high confidence block shift turbulence data to calculate the turbulence information for the atmospheric distance.
17. The system of claim 16 wherein the image data is an array of pixel data from twenty or more frames of successive image data from the image capture device.
18. The system of claim 16 wherein the image capture device is a video camera.
19. The system of claim 16 wherein the method is passive, using only image data captured at a location of the image capture device without a remote optic signal transmitter.

20. The system of claim 16 wherein high confidence block shift processing includes:

process image data to create image data blocks;
calculate an information content metric for one or more image data blocks;
track information content metric for one or more image data blocks over time;
record or designate block tracks which meet a predetermined threshold as surviving blocks;
calculate pixel variance value for surviving blocks related to cn 2;
calculate platform motion using differential frame motion;
for one or more pixel variance values, calculate mean variance from one or more sets of pixel variance values to determine the index of refraction structure constant (cn 2); and
calculate the high confidence block shift turbulence data based on platform movement and (cn 2).
21. The system of claim 16 wherein combining the spatial/temporal spectrum characterization turbulence data and the high confidence block shift turbulence data includes rescaling the spatial/temporal spectrum characterization turbulence data based on an average of a ratio of the high confidence block shift turbulence data and spatial/temporal spectrum characterization data for a period of time.
22. The system of claim 16 further comprising processing the image data utilizing the turbulence data to remove aberrations in the image caused by the turbulence over the atmospheric distance.

23. The system of claim 16 wherein spatial/temporal spectrum characterization processing includes:

calculate or receive a scale factor based on system parameters;
reading image data to create an image stack;
calculate variance data over time for pixels in the image stack;
perform a Fourier transform on the variance data to generate power spectral density data;
average the power spectral density data to obtain average power spectral density data;
process the average power spectral density data in relation to a Kolmogorov spectrum to obtain a value proportional to the index of refraction structure constant (cn 2); and
multiply the index of refraction structure constant (cn 2) by the scale factor to generate the spatial/temporal spectrum characterization data.
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Title

Passive method to measure strength of turbulence

Inventor(s)

Mary Morabito O'Neill, David Terry

Assignee(s)

Mission Support And Test Services LLC

Patent #

20190373172

Patent Date

December 5, 2019

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