Machine Learning Atmospheric Turbulence Estimation from Star Speckles

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Solution Overview

Problem

Existing methods for estimating atmospheric turbulence parameters, such as Cn2(h), r0, and wind profile V(h), require complex and expensive dedicated instrumentation and involve intricate mathematical models, making them impractical for amateur telescopes.

Innovation Solution

A machine learning model is trained using optical speckle images from defocused star images to estimate atmospheric turbulence parameters, eliminating the need for specialized equipment and complex models.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If dedicated measuring instruments and complex mathematical models are used to estimate atmospheric turbulence parameters, then measurement precision is improved, but device complexity and cost increase

Engineering Contradiction:

Improveatmospheric turbulence parameter estimation accuracyVSAvoidinstrumentation complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent uses a machine learning model trained on simulated speckle images to create a virtual copy of the complex atmospheric turbulence estimation process. Instead of using dedicated complex instruments, the system copies the essential features of turbulence-induced speckle patterns and uses a trained neural network to estimate parameters, thereby simplifying the physical instrumentation while maintaining measurement capability

Inventive Principle:

Principle #26Copying

Solution Approach 2:

The patent replaces complex mechanical/optical measurement systems with an information processing system. Instead of using dedicated turbulence measuring instruments with complex optical paths and mechanical components, the system uses a camera to capture speckle images and a machine learning model to process them, substituting physical complexity with computational simplicity

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Measurement precision

If dedicated measuring instruments and complex mathematical models are used to estimate atmospheric turbulence parameters, then measurement precision is improved, but cost increases

Engineering Contradiction:

Improveatmospheric turbulence parameter estimation accuracyVSAvoidimplementation cost

Core Design Contradiction:

Measurement precisionVSEase of manufacture

Solution Approach 1:

The patent employs a standard camera and computational algorithms instead of expensive dedicated turbulence measuring instruments. The system uses off-the-shelf components (camera, processor) that are much cheaper than specialized equipment, making the solution economically viable for amateur telescopes and budget-conscious applications while still providing accurate turbulence parameter estimation

Inventive Principle:

Principle #27Cheap short-living objects (Disposable)

3Measurement precision

If complex mathematical models are used to extract Cn2(h) profile from speckle images, then measurement precision is improved, but device complexity increases

Engineering Contradiction:

ImproveCn2(h) profile accuracyVSAvoidmathematical model complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent replaces complex mathematical models with a machine learning model. Instead of implementing intricate algorithms for extracting Cn2(h) profiles from speckle images, the system uses a neural network that has been trained on simulated data. This substitution transforms a mathematically complex problem into a computationally simpler pattern recognition task, maintaining accuracy while reducing implementation complexity

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

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

The method allows for accurate, robust, and cost-effective estimation of atmospheric turbulence parameters using amateur telescopes, enhancing the precision and accessibility of atmospheric turbulence analysis.

Implementation Method 1

This scintillation phenomenon is a temporal fluctuation of the amplitude of the wave corresponding to a dilution or a concentration of the energy of the wave front. Speckles consist of small dark and shiny spots that appear fluctuating in images of the pupil of a telescope and that illustrate this scintillation phenomenon.

Methodology Applied

Scientific EffectScintillation: Scintillation

Implementation Method 2

These are high-frequency structures that correspond to interferences of the rays deviated by the turbulence.

Methodology Applied

Scientific EffectInterference: Interference

Data Source

PatentUS12455398B2Method and installation for estimating a characteristic atmospheric turbulence parameter

Publication Date: 2025.10.28 UNISTELLAR

AI summary

A method for estimating a characteristic atmospheric turbulence parameter, the method including the steps of: training a machine learning model using, as learning data, values of the characteristic parameter with which are associated optical speckle images corresponding to defocused images of one or more stars, and using the trained learning model to estimate the characteristic parameter from input data containing one or more optical speckle images acquired by at least one measuring telescope, where the speckle images correspond to defocused images of one or more stars observed in real conditions by the telescope.