A method for detecting atmospheric turbulence stratification based on pyramid wavefront sensor

Abstract

This invention provides a method for atmospheric turbulence stratification detection based on a pyramidal wavefront sensor, relating to the fields of atmospheric sounding and adaptive optics. The method includes: receiving an incident beam from a single guide star direction, and then passing the incident beam through a pyramidal wavefront sensor to form a detection signal containing atmospheric turbulence disturbance information; periodically moving the position of the detector at the rear end of the pyramidal wavefront sensor, and acquiring a sequence of detection images at multiple conjugate altitudes through time-division conjugation; establishing a mapping relationship between the sequence of detection images at multiple conjugate altitudes and turbulence layer parameters at different altitudes; and performing stratification parameter inversion of atmospheric turbulence based on the sequence of detection images at multiple conjugate altitudes. This invention reduces the dependence on multi-guide star configurations and multi-sensor setups, improves the stratification resolution of atmospheric turbulence stratification measurements, and enables comprehensive detection of atmospheric turbulence profiles and wind field information.

Description

Technical Field

[0001] This invention belongs to the field of atmospheric sounding and adaptive optics technology, and particularly relates to a method for atmospheric turbulence stratification detection based on a pyramidal wavefront sensor. Background Technology

[0002] Atmospheric turbulence is caused by random fluctuations in the refractive index due to minute variations in meteorological parameters such as temperature and pressure in the atmosphere. These fluctuations cause effects such as beam drift, spot widening, wavefront distortion, and intensity flickering during light wave propagation. The core physical quantity characterizing the strength of atmospheric turbulence is the refractive index structure constant, and its distribution along the altitude of the propagation path constitutes the atmospheric turbulence profile. Accurate layered detection of the atmospheric turbulence profile and its time-evolving wind field information is of great significance for fields such as astronomical observation, adaptive optics, laser communication, and beam control.

[0003] Existing methods for measuring atmospheric turbulence mainly include direct measurement methods based on meteorological parameters and optical measurement methods based on the propagation characteristics of light waves. Compared to indirect methods such as sounding balloons, optical measurement methods can directly utilize the response information of light waves during actual propagation to invert atmospheric turbulence, and therefore are widely used in atmospheric profile measurement. Currently, optical-based atmospheric profile measurement methods mainly include measurement methods based on light intensity fluctuations and measurement methods based on wavefront phase fluctuations.

[0004] Among measurement methods based on light intensity fluctuations, techniques such as Scintillation Detection and Ranging (SCIDAR) and Multi-Aperture Scintillation Sensor (MASS) are widely used. The SCIDAR method inverts atmospheric turbulence parameters by analyzing light intensity cross-correlation maps; however, this method requires continuous observation of binary star motion, resulting in low temporal resolution. Although subsequent studies proposed single-star SCIDAR methods to overcome this limitation, these techniques are highly dependent on clear cross-correlation maps, especially under weak exposure and strong turbulence conditions, which limits the accurate measurement of turbulence parameters. The MASS method designs the spectroscopic system with multiple channels of different sizes, using the scintillation index of each channel and the differential scintillation index between channels to invert the turbulence profile. However, due to limitations such as detection altitude and operating time, it is mostly used for observatory site selection and is not suitable for continuous observation of atmospheric profiles.

[0005] With the rapid development of wavefront sensing technology, wavefront variation-based turbulence detection has gradually become a reliable solution. Slope Detection and Ranging (SLODAR) and Solar Differential Image Motion Monitor Plus (S-DIMM+) systems dominate nighttime and daytime turbulence stratification measurements, respectively. These methods typically utilize Shaker-Hartmann wavefront sensors to perform jitter cross-analysis of wavefront tilts of guide stars in different directions, essentially belonging to optical triangulation methods based on multi-line-of-sight geometry. However, the stratification capability of this method is limited by the number and size of sub-apertures and the geometric arrangement of guide stars, resulting in limited resolvable altitude layers and vertical resolution. It often only acquires turbulence information for a limited number of discrete altitude layers, making it difficult to achieve high-density continuous atmospheric profile measurements.

[0006] With the development of large field-of-view adaptive optics and optical applications in complex atmospheric environments, the shortcomings of existing atmospheric turbulence measurement techniques have become increasingly apparent. For practical applications, it is not only necessary to obtain information on the distribution of atmospheric turbulence intensity with altitude, but also to further acquire dynamic evolution parameters of each turbulent layer, such as wind speed and direction, to support system modeling, parameter prediction, and real-time control. However, current mainstream methods struggle to balance altitude resolution, continuous detection capability, and system complexity, especially under complex multi-layered turbulent conditions, often making it difficult to simultaneously obtain high-precision turbulence profile information and wind field information.

[0007] In summary, while existing optical measurement methods for atmospheric turbulence can acquire atmospheric turbulence parameters to some extent, they generally suffer from the following shortcomings: First, they are highly dependent on observation conditions, guide star configuration, or the quality of related images; second, the number of resolvable altitude layers is limited, making it difficult to achieve high-density continuous layered measurements; and third, while acquiring turbulence profile information, their ability to invert dynamic wind field parameters such as wind speed and direction in each turbulent layer remains limited. These problems restrict the high-precision and continuous application of existing methods in complex atmospheric environments.

[0008] Pyramidal wavefront sensors, as highly sensitive wavefront detection devices, have attracted widespread attention in the field of wavefront detection. These sensors respond to the incident light field through focal plane modulation and subsequent imaging, and their detection results are closely related to the spatial distribution characteristics of the light field during propagation. By moving the position of the rear detector, the detection surface can be optically conjugate to different height regions along the propagation path. When conjugated to a certain target height, the contributions of different turbulence layers at different heights to the detection results vary, thus enabling the sensor to exhibit differentiated responses to disturbances at different heights. Based on this characteristic, pyramidal wavefront sensors hold promise for overcoming the limitations of existing atmospheric turbulence measurement methods in terms of the number of layers, vertical resolution, and continuous detection capability, providing a new technical approach for achieving stratified atmospheric turbulence measurement at arbitrary heights. Summary of the Invention

[0009] The purpose of this invention is to propose a method for atmospheric turbulence stratification detection based on a pyramidal wavefront sensor, which can solve the problem of high-density and high-precision stratification measurement of atmospheric turbulence.

[0010] The technical problem this invention aims to solve is that existing atmospheric measurement methods generally suffer from limitations in the number of measurement altitude layers and are subject to significant limitations due to the distribution of multiple guide stars or specific observation conditions, making it difficult to achieve high-density, high-precision layered measurements of atmospheric turbulence. Furthermore, existing methods lack the ability to retrieve dynamic information such as wind speed and direction for each turbulence layer when acquiring atmospheric turbulence intensity profiles, making it difficult to meet the multi-parameter measurement requirements of optical systems in complex atmospheric environments.

[0011] This invention provides an atmospheric measurement method for inverting and estimating atmospheric turbulence profiles and wind field parameters by using a pyramidal wavefront sensor and a back-end detector for time-division multi-altitude conjugate sampling. This method can be applied to astronomical observation, laser communication, and other optical systems affected by atmospheric turbulence.

[0012] To achieve the above objectives, this invention provides a method for atmospheric turbulence stratification detection based on a pyramidal wavefront sensor. The method includes: receiving an incident beam from a single guide beam direction, and causing the incident beam to pass through the pyramidal wavefront sensor to form a detection signal containing atmospheric turbulence disturbance information; periodically moving the position of the detector at the rear end of the pyramidal wavefront sensor, causing the detector to acquire a sequence of detection images at multiple conjugate altitudes through time-division conjugation, wherein time-division conjugation refers to forming multiple conjugate altitudes at different times and different altitudes along the beam transmission path; establishing a mapping relationship between the detection image sequence at multiple conjugate altitudes and turbulence parameters at different altitudes based on the characteristic that the pyramidal wavefront sensor has different responses to turbulence disturbances at different altitudes; and performing stratification parameter inversion of atmospheric turbulence based on the detection image sequence at multiple conjugate altitudes.

[0013] The present invention has the following beneficial effects:

[0014] (1) By using only the incident beam in a single guide star direction and a single pyramidal wavefront sensor, the conjugate relationship between the back-end detector and different altitude regions can be changed in time division, thus obtaining a sequence of detection images at multiple preset conjugate altitudes. This enables the layered detection of atmospheric turbulence, reduces the system's dependence on multi-guide star geometry and multi-sensor configuration, and simplifies the system structure.

[0015] (2) By continuously moving the position of the detector at the rear end of the pyramidal wavefront sensor, continuous measurement of atmospheric turbulence can be achieved. At the same time, by utilizing its characteristic of different responses to disturbances in turbulent layers at different conjugate heights, a mapping relationship between multiple preset conjugate height detection image sequences and turbulent layer parameters at different heights can be established, which can improve the layer resolution capability of atmospheric turbulence layer measurement.

[0016] (3) By performing layered parameter inversion on the detection image sequence collected at multiple preset conjugate heights, it is possible not only to obtain the atmospheric turbulence intensity profile, but also to obtain information such as wind speed and wind direction in each turbulence layer, thereby improving the richness of atmospheric measurement results and providing more comprehensive atmospheric parameter support for fields such as astronomical observation, adaptive optics system design, laser communication and beam control. Attached Figure Description

[0017] The accompanying drawings are provided to further illustrate embodiments of the present invention and form part of the specification. They are used together with the following detailed description to explain the embodiments of the present invention, but do not constitute a limitation thereof. In the drawings:

[0018] Figure 1A flowchart of the atmospheric turbulence stratification detection method based on a pyramidal wavefront sensor provided by the present invention;

[0019] Figure 2 A schematic diagram illustrating the principle of the atmospheric turbulence stratification detection method based on a pyramidal wavefront sensor provided by this invention;

[0020] Figure 3 A schematic diagram of an atmospheric turbulence stratification detection system based on a pyramidal wavefront sensor provided by the present invention;

[0021] Figure 4 This is a schematic diagram of the back-end detector of the present invention performing time-division conjugate sampling at different preset conjugate heights;

[0022] Figure 5 This is a schematic diagram illustrating the inversion of atmospheric turbulence parameters from a sequence of pyramidal wavefront sensor images, provided by the present invention. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other. To achieve the above objectives, this invention adopts the following technical solution.

[0024] Figure 1 The flowchart of the atmospheric turbulence stratification detection method based on a pyramidal wavefront sensor provided by this invention is as follows: Figure 1 As shown, the method includes:

[0025] It receives an incident beam from a single guide star and, after passing through a pyramidal wavefront sensor, generates a detection signal containing information about atmospheric turbulence disturbances.

[0026] By periodically moving the position of the detector at the rear end of the pyramidal wavefront sensor, the detector acquires a sequence of detection images at multiple conjugate heights through time-division conjugation. Here, time-division conjugation means forming multiple conjugate heights at different times and different heights on the beam transmission path.

[0027] Based on the characteristic that the pyramidal wavefront sensor has different responses to turbulent layer disturbances at different heights at multiple conjugate heights, a mapping relationship between the detection image sequences at multiple conjugate heights and turbulent layer parameters at different heights is established.

[0028] Based on the mapping relationship between the detection image sequences at multiple conjugate altitudes and the parameters of the turbulent layer at different altitudes, the atmospheric turbulence is inverted by layering parameters according to the detection image sequences at multiple conjugate altitudes.

[0029] Figure 2 This is a schematic diagram illustrating the principle of the atmospheric turbulence stratification detection method based on a pyramidal wavefront sensor provided by the present invention, as shown below. Figure 2 As shown, the incident beam emitted by a single guide star carries wavefront phase perturbation information after passing through atmospheric turbulence. The incident beam is received by the telescope and converged into the subsequent optical path. The relay system is used to perform aperture matching and construct conjugate positions for the beam output by the telescope to meet the incident conditions of the pyramidal wavefront sensor. The pyramidal wavefront sensor is used to split the incident beam at the focal plane and convert the wavefront phase perturbation carried by the incident beam into light intensity distribution information that can be collected by the back-end detector. The back-end detector collects corresponding detection image sequences at multiple preset conjugate heights. The parameter inversion model performs layer parameter inversion based on the detection image sequence and the pre-established mapping relationship, and outputs atmospheric layer parameters, including the height position of the turbulent layer and its corresponding turbulence intensity, wind speed and wind direction.

[0030] The atmospheric turbulence disturbance information includes at least one or more turbulence layer parameters, including turbulence layer height, turbulence intensity, wind speed, and wind direction. By performing layer-by-layer inversion on parameters at different heights, the atmospheric turbulence distribution characteristics and their dynamic evolution characteristics along the beam propagation path can be obtained, providing basic data for subsequent atmospheric parameter analysis, system modeling, and applications.

[0031] Multiple conjugate heights are distributed along the propagation direction of the incident beam. The conjugate heights are determined based on a preset layer resolution, a preset step size, and the number of sampling positions. The distance between adjacent conjugate heights is the preset step size. By setting the conjugate heights in layers, detection image sequences with differentiated layer response characteristics can be acquired at different height locations, thereby improving the ability to distinguish turbulent layers at different heights. The preset step size can be set, for example, to the layer resolution.

[0032] Time-division conjugation can be achieved by moving the back-end detector using a sliding rail mechanism or by using variable-focus optics to change the conjugation relationship between the detector and different altitude layers. By changing the conjugation state between the back-end detector and different altitude regions, detection results corresponding to multiple altitude positions can be obtained without adding multiple independent wavefront sensors, thereby realizing layered measurements based on a single guide star direction and a single pyramidal wavefront sensor.

[0033] The mapping relationship is established as follows: the beam transmission path is divided into multiple height layers, and a preset phase screen with preset turbulence intensity, preset wind speed, and preset wind direction is loaded for each height layer; the preset phase screen is transmitted through free propagation, a telescope, and a relay system and then input into a pyramidal wavefront sensor to obtain a sequence of detection images at multiple preset conjugate heights. The correspondence between the preset turbulence intensity, preset height, preset wind speed, and preset wind direction corresponding to the preset phase screen and the detection image sequence is recorded; based on the multiple height layers and the correspondence between the preset turbulence intensity, preset height, preset wind speed, and preset wind direction corresponding to the preset phase screen and the detector sequence, a mapping relationship from turbulence layer parameters at different heights to detection image sequences at multiple conjugate heights is obtained, where the multiple height layers include the conjugate heights.

[0034] The mapping relationship is established based on the response model of the pyramidal wavefront sensor to aberrations in turbulent layers at different heights. The response model characterizes the influence of turbulent layer parameters at different heights on image sequences detected at multiple preset conjugate heights. The response model can be obtained through theoretical calculations, numerical simulations, experimental calibration, or a combination thereof, so that the established mapping relationship can reflect the differential response characteristics of the pyramidal wavefront sensor to disturbances in different turbulent layers at different conjugate heights.

[0035] The hierarchical parameter inversion includes: based on the mapping relationship between turbulence layer parameters at different heights and detection image sequences at multiple preset conjugate heights, using the detection image sequences as training samples and the corresponding turbulence layer parameters as training labels, training a deep learning network; inputting the actual collected detection image sequences at multiple preset conjugate heights into the trained deep learning network to obtain the actual height of the turbulence layer and the corresponding turbulence intensity, wind speed, and wind direction.

[0036] In other words, layered parameter inversion involves constructing observation vectors for turbulence parameters at different altitudes based on sequences of detected images at multiple preset conjugate altitudes, and then solving the mapping relationship using least squares estimation, deep learning, or other parameter inversion methods to obtain the parameters for each turbulent layer. The parameter inversion results include at least the atmospheric turbulence intensity profile, and may further include dynamic information such as wind speed and direction for each turbulent layer.

[0037] The following description, in conjunction with the accompanying drawings, and using an atmospheric turbulence stratification detection system employing a single guide star, a single pyramidal wavefront sensor, and a rear-end time-division conjugate sampling mechanism as an example, further illustrates the specific implementation of the present invention.

[0038] The atmospheric turbulence stratification detection system in this embodiment mainly includes a pyramidal wavefront sensor, a rear-end detector, a detector position adjustment mechanism, and a parameter inversion calculation module. The pyramidal wavefront sensor is used to respond to the wavefront phase carried by the incident beam to form a detection signal containing atmospheric turbulence disturbance information. The rear-end detector is used to acquire the detection images output by the pyramidal wavefront sensor. The detector position adjustment mechanism is used to change the position of the rear-end detector at regular intervals so that the rear-end detector forms a conjugate relationship with different height regions on the beam transmission path at different times. The parameter inversion calculation module is used to invert the atmospheric turbulence intensity profile and parameters such as wind speed and wind direction based on the detection image sequence acquired at multiple preset conjugate heights and in combination with the pre-established mapping relationship.

[0039] Figure 3 This is a schematic diagram of an atmospheric turbulence stratification detection system based on a pyramidal wavefront sensor provided by the present invention, as shown below. Figure 3 As shown, firstly, an incident beam from a single guiding star direction is received, and after passing through a pyramidal wavefront sensor, it forms a detection signal containing information about atmospheric turbulence disturbances. The pyramidal wavefront sensor includes a pyramidal prism and its associated imaging optical path. The incident beam is focused near the apex of the pyramidal prism, split by the prism, collimated by a collimating lens, and imaged onto a rear-end detector. The collimating lens converts the beam split by the pyramidal prism into multiple parallel beams received by the detector. The rear-end detector is mounted on a guide rail, which is used to change the detector's position along the equivalent conjugate height adjustment direction. During operation, the pyramidal wavefront sensor converts wavefront disturbances in the incident light field into a detection image that the rear-end detector can record through focal plane modulation and subsequent imaging.

[0040] Subsequently, by periodically moving the position of the detector at the rear end of the pyramidal wavefront sensor, the detector is made to form a conjugate relationship with different height regions on the beam propagation path at different times, and corresponding detection image sequences are acquired at each conjugate position. The change of the detector position can be achieved by a sliding rail mechanism or by using a variable focal length optical component, as long as the optical conjugate relationship between the detection surface and different height regions on the beam propagation path can be changed.

[0041] Figure 4 This is a schematic diagram illustrating the inversion of atmospheric turbulence parameters from a sequence of pyramidal wavefront sensor images provided by the present invention, as shown below. Figure 4 As shown, multiple conjugate heights h1, h2, … , h are set along the propagation direction of the incident beam. N And set the corresponding collection time. , , ..., .exist At a certain moment, the rear-end detector moves along the guide rail to a position corresponding to the first conjugate height h1 and acquires a sequence of detection images. At a certain moment, the rear-end detector moves along the guide rail to the position corresponding to the second conjugate height h2 and acquires a sequence of detection images, and so on, in At that moment, the rear-end detector moves along the guide rail to a height h conjugate to the Nth height. N Corresponding locations are then used to acquire detection image sequences, i.e., time-division detection. Adjacent conjugate heights are selected according to a preset step size, which can be determined based on the expected layer-wise resolution, the target transmission path range, and system sampling parameters. When determining the preset step size, the target transmission path range is first set. and desired layer resolution Subsequently, multiple preset conjugate heights are set according to... It is confirmed that, among them, , Indicates the sampling location index. , For the minimum height, To represent the maximum height, in this embodiment, the preset step size is set to the layer resolution. When it is necessary to improve the layer resolution, the conjugate height step size can be reduced and the number of sampling positions N can be increased; when it is necessary to balance sampling efficiency and system complexity, a larger step size can be used for discrete scanning.

[0042] When the back-end detector forms a conjugate relationship with a target height, the contributions of turbulent layers at different heights to the detected image are not the same. Turbulent layers close to the target's conjugate height introduce wavefront disturbances that are more concentrated on the detector surface; while disturbances introduced by layers at non-conjugate heights undergo diffraction expansion during propagation, and together with the sensor's own spatial sampling characteristics, exhibit varying degrees of layer-specific attenuation. Therefore, the response of the pyramidal wavefront sensor to turbulent layers at different conjugate heights is not consistent, but rather exhibits differentiated layer-specific selectivity characteristics.

[0043] After obtaining multiple preset conjugate image sequences, based on the characteristic that the pyramidal wavefront sensor responds differently to disturbances in turbulent layers at different conjugate heights, a mapping relationship is established between turbulent layer parameters at different heights and the image sequences at multiple preset conjugate heights. Specifically, the beam transmission path is divided into multiple turbulent height layers, and a phase screen with preset turbulence intensity, wind speed, and wind direction is loaded for each turbulence height layer. The beam modulated by the phase screen propagates through free space, is transmitted through a telescope and a relay system, and then inputs into the pyramidal wavefront sensor, acquiring corresponding image sequences at multiple preset conjugate heights. The correspondence between the height, turbulence intensity, wind speed, and wind direction of the turbulent layer corresponding to each phase screen and the image sequences is recorded, thereby establishing a mapping relationship from turbulent layer parameters at different heights to image sequences at multiple preset conjugate heights.

[0044] Figure 5 This is a schematic diagram illustrating the inversion of atmospheric turbulence parameters from a pyramidal wavefront sensor image sequence provided by the present invention, as shown below. Figure 5 As shown, after establishing the mapping relationship, this embodiment uses a deep learning network to perform hierarchical parameter inversion on the detection image sequences acquired at multiple preset conjugate heights. The deep learning network includes an image feature extraction module, a temporal feature fusion module, and a parameter output module connected in sequence. The image feature extraction module includes three convolutional layers, each with a kernel size of 3×3 and output channels of 32, 64, and 128 respectively. Each convolutional layer is followed by a batch normalization layer, a ReLU activation layer, and a 2×2 max pooling layer to extract image spatial features from the input image sequence. The temporal feature fusion module uses a long short-term memory network with 128 hidden units to fuse the image spatial features corresponding to consecutive moments in the detection image sequence to obtain temporal fusion features. The parameter output module includes two fully connected layers with 256 and 128 nodes respectively, and an output layer with 3L nodes, where L is the number of candidate turbulence height layers. The three output nodes corresponding to each candidate turbulence height layer are used to output turbulence intensity, wind speed, and wind direction, respectively.

[0045] During training, detection image sequences acquired at multiple preset conjugate heights at consecutive time points are uniformly cropped or resampled to 128×128 pixels and stitched together along the channel dimension, while light intensity normalization is performed. The turbulent layer height, turbulence intensity, wind speed, and wind direction obtained from simulation or experimental calibration are used as labels. Mean squared error loss is used as the loss function for the deep learning network, and the Adam optimizer is used to train the deep learning network with an initial learning rate of 0.001, a batch size of 32, and 100 to 300 training epochs. After training, the detection image sequences actually acquired at multiple preset conjugate heights are input into the deep learning network, which outputs the height position of one or more turbulent layers and the corresponding turbulence intensity, wind speed, and wind direction.

[0046] Through the above implementation, the present invention utilizes an incident beam in a single guide direction and a single pyramidal wavefront sensor to obtain multiple preset conjugate image sequences at different altitudes by periodically changing the conjugate relationship between the back-end detector and different altitude regions. Based on the different responses of the pyramidal wavefront sensor to disturbances in the turbulent layer at different altitudes, the invention achieves layered inversion acquisition of atmospheric turbulence intensity profiles and wind speed and direction information.

[0047] The optional embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the embodiments of the present invention are not limited to the specific details in the above embodiments. Within the scope of the technical concept of the embodiments of the present invention, various simple modifications can be made to the technical solutions of the embodiments of the present invention, and these simple modifications all fall within the protection scope of the embodiments of the present invention.

[0048] It should also be noted that the various specific technical features described in the above embodiments can be combined in any suitable manner without contradiction. To avoid unnecessary repetition, the embodiments of the present invention will not describe the various possible combinations separately.

[0049] Furthermore, various different implementations of the present invention can be combined arbitrarily, as long as they do not violate the spirit of the present invention, they should also be regarded as the content disclosed in the present invention.

Claims

1. A method for detecting atmospheric turbulence stratification based on a pyramidal wavefront sensor, characterized in that, The method includes: It receives an incident beam from a single guide star and, after the incident beam passes through a pyramidal wavefront sensor, forms a detection signal containing atmospheric turbulence disturbance information. By periodically moving the position of the detector at the rear end of the pyramidal wavefront sensor, the detector acquires a sequence of detection images at multiple conjugate heights through time-division conjugation. Here, time-division conjugation means forming multiple conjugate heights at different times and different heights on the beam transmission path. Based on the characteristic that the pyramidal wavefront sensor has different responses to turbulent layer disturbances at different heights at multiple conjugate heights, a mapping relationship is established between the detection image sequences at multiple conjugate heights and turbulent layer parameters at different heights. Based on the mapping relationship between the detection image sequences at multiple conjugate altitudes and the parameters of the turbulent layer at different altitudes, the atmospheric turbulence is inverted by layering parameters according to the detection image sequences at multiple conjugate altitudes.

2. The atmospheric turbulence stratification detection method based on a pyramidal wavefront sensor according to claim 1, characterized in that, The atmospheric turbulence disturbance information includes at least one or more turbulence layer parameters, including turbulence layer height, turbulence intensity, wind speed, and wind direction.

3. The atmospheric turbulence stratification detection method based on a pyramidal wavefront sensor according to claim 2, characterized in that, The multiple conjugate heights are distributed along the propagation direction of the incident beam, and the conjugate heights are determined according to the preset step size and the number of sampling positions.

4. The atmospheric turbulence stratification detection method based on a pyramidal wavefront sensor according to claim 3, characterized in that, The preset step size is set to the layer resolution.

5. The atmospheric turbulence stratification detection method based on a pyramidal wavefront sensor according to claim 4, characterized in that, The time-division conjugation is achieved by driving the rear detector to move through a sliding rail mechanism or by using a variable-focus optical component to change the conjugation relationship between the detector and different height layers.

6. The atmospheric turbulence stratification detection method based on a pyramidal wavefront sensor according to claim 5, characterized in that: The mapping relationship is established in the following way: The beam transmission path is divided into multiple height layers, and a preset phase screen with preset turbulence intensity, preset wind speed and preset wind direction is loaded for each height layer. After the preset phase screen is transmitted through free propagation, telescope and relay system, it is input into the pyramidal wavefront sensor to obtain a sequence of detection images at multiple preset conjugate heights. The correspondence between the preset turbulence intensity, preset height, preset wind speed and preset wind direction corresponding to the preset phase screen and the detection image sequence is recorded. Based on the correspondence between multiple height layers and the preset turbulence intensity, preset height, preset wind speed, and preset wind direction corresponding to the preset phase screen and the detector sequence, the mapping relationship from the turbulence layer parameters at different heights to the detector image sequence at multiple conjugate heights is obtained, where the multiple height layers include the conjugate heights.

7. The atmospheric turbulence stratification detection method based on a pyramidal wavefront sensor according to claim 6, characterized in that, The hierarchical parameter inversion includes: Based on the mapping relationship between the turbulence layer parameters at different heights and the detection image sequences at multiple preset conjugate heights, the detection image sequences are used as training samples, and the corresponding turbulence layer parameters are used as training labels to train the deep learning network. The sequence of detection images collected at multiple preset conjugate heights is input into the trained deep learning network to obtain the actual height of the turbulent layer and the corresponding turbulence intensity, wind speed and wind direction.

8. The atmospheric turbulence stratification detection method based on a pyramidal wavefront sensor according to claim 7, characterized in that, The deep learning network includes an image feature extraction module, a temporal feature fusion module, and a parameter output module connected in sequence. The image feature extraction module comprises three convolutional layers, each with a 3×3 kernel size and output channels of 32, 64, and 128 respectively. Each convolutional layer is followed by a batch normalization layer, a ReLU activation layer, and a 2×2 max pooling layer to extract image spatial features from the input image sequence. The temporal feature fusion module includes a Long Short-Term Memory network with 128 hidden units, used to fuse the image spatial features corresponding to consecutive acquisition times in the probe image sequence to obtain temporal fusion features.

9. A method for detecting atmospheric turbulence stratification based on a pyramidal wavefront sensor according to claim 8, characterized in that, The parameter output module includes two fully connected layers with 256 and 128 nodes respectively, and an output layer with 3L nodes, where L is the number of candidate turbulence height layers. The three output nodes corresponding to each candidate turbulence height layer are used to output turbulence intensity, wind speed and wind direction respectively.

10. A method for detecting atmospheric turbulence stratification based on a pyramidal wavefront sensor according to claim 9, characterized in that, Mean squared error loss is used as the loss function of the deep learning network. The Adam optimizer is used to train the deep learning network with an initial learning rate of 0.001, a batch size of 32, and 100 to 300 training epochs.

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