Method and device for correcting short-wave infrared atmospheric refraction model

Abstract

This disclosure presents a method and apparatus for correcting shortwave infrared atmospheric refraction models. One specific implementation of the method includes: acquiring an initial set of observations and a meteorological metadata dataset for an observation station; performing geographic matching with a global standard atmospheric model to generate a set of atmospheric physical parameters; generating a theoretical refraction model containing undetermined coefficients based on the atmospheric physical parameter set and the meteorological metadata dataset; generating a refraction residual sequence based on the initial set of observations; performing least-squares fitting between the refraction residual sequence and the theoretical refraction model to solve for the local optimal model coefficients; and using the local optimal model coefficients to perform real-time refraction correction on the atmospheric refraction path in the initial observations to generate corrected observations. This implementation reduces the impact of atmospheric refraction on near-infrared astronomical measurements and navigation.

Description

Technical Field

[0001] The embodiments disclosed herein relate to the field of astronomical observation technology, specifically to a method and apparatus for correcting shortwave infrared atmospheric refraction models. Background Technology

[0002] Shortwave infrared astronomical atmospheric refraction error correction technology is directly applied to ground-based shortwave infrared telescopes, infrared laser rangefinders, and other air observation equipment. Targeting the atmospheric refractive index characteristics of the near-infrared (NIR) band, it addresses refraction deviations in shortwave infrared radiation transmission by constructing a near-infrared astronomical atmospheric refraction model and solving for model parameters using measured data. This technology is suitable for high-precision observation scenarios such as all-weather infrared astronomical measurements, astronomical geodesy, deep-space object infrared positioning, and low-orbit satellite infrared remote sensing. While existing research on atmospheric refraction correction technology has made some progress, current research on near-infrared atmospheric refraction correction models largely focuses on fundamental research into atmospheric physical properties and meteorological parameters, or on applications in other fields such as remote sensing. Furthermore, existing atmospheric refraction models are mostly based on research findings in the visible light or radio wave bands, and systematic theoretical methods and correction models for the near-infrared band are still lacking. Because the near-infrared band has its own unique physical characteristics, namely different atmospheric absorption and scattering properties, it must be studied using independent models and methods.

[0003] However, when using the above methods, the following technical problems often arise: astronomical navigation is characterized by its immunity to electromagnetic interference, non-accumulation of errors over time, high accuracy in vertical deviation measurement, and high accuracy in astronomical azimuth calibration. It still holds an important position in modern earth science, navigation and positioning, autonomous navigation of deep space probes, geophysical research, and high-precision autonomous navigation of lunar / Mars rovers. With the continuous increase in space launch missions and space exploration missions, the importance of using the near-infrared band for astronomical navigation observations is receiving increasing attention. Atmospheric refraction is one of the important factors affecting the accuracy of astronomical navigation, especially its impact on near-Earth astronomical positioning results.

[0004] Most existing atmospheric refraction models are based on data and theories in the visible light band, while research on atmospheric refraction correction for the near-infrared band is relatively scarce. This study presents a starlight atmospheric refraction model based on the NCEP (National Center for Environmental Prediction) database. This model calculates the propagation path of starlight in the atmosphere using high-resolution NCEP atmospheric parameter data and Fourier interpolation algorithms, and establishes a corresponding refraction model. Research shows that this model significantly improves theoretical accuracy, especially in low-elevation observations. However, this model is primarily for the visible light band, and its applicability to the near-infrared band requires further verification. A radio wave refraction error correction device was designed based on the principles of a microwave radiometer and GNSS receiver, and real-time, high-precision refraction error correction was implemented for S-band radar. This device can invert atmospheric water vapor content and temperature distribution, thereby reducing the impact of atmospheric refraction on radar ranging; however, this research mainly focuses on the microwave band. Refraction error correction based on spatial projection and adaptive grid methods represent attempts to apply mathematical methods to the technical field. Geometric projection and numerical simulation can effectively address the atmospheric refraction problem of high-orbit targets, but this method is mainly used for satellite ranging and space target positioning, and cannot fully meet the needs of near-infrared astronomical observations. Summary of the Invention

[0005] The summary portion of this disclosure is intended to provide a brief overview of the concepts, which will be described in detail in the detailed description portion. This summary portion is not intended to identify key or essential features of the claimed technical solutions, nor is it intended to limit the scope of the claimed technical solutions.

[0006] Some embodiments of this disclosure propose a method and apparatus for correcting shortwave infrared atmospheric refraction models to address the technical problems mentioned in the background section above.

[0007] In a first aspect, some embodiments of this disclosure provide a method for correcting a shortwave infrared atmospheric refraction model. The method includes: collecting an initial set of observations and a meteorological metadata dataset for an observation station; performing geographic matching on a global standard atmospheric model to generate an atmospheric physical parameter set; generating a theoretical refraction model containing undetermined coefficients based on the atmospheric physical parameter set and the meteorological metadata dataset; generating a refraction residual sequence based on the initial set of observations; performing least-squares fitting on the refraction residual sequence and the theoretical refraction model to solve for the local optimal model coefficients; and using the local optimal model coefficients to perform real-time refraction correction on the atmospheric refraction path in the initial observations to generate corrected observations.

[0008] Secondly, some embodiments of this disclosure provide a shortwave infrared atmospheric refraction model correction device, the device comprising: an acquisition unit configured to acquire an initial set of observations and a meteorological metadata dataset for an observation station; a matching unit configured to perform geographic matching with a global standard atmospheric model to generate an atmospheric physical parameter set; a first generation unit configured to generate a theoretical refraction model containing undetermined coefficients based on the atmospheric physical parameter set and the meteorological metadata dataset; a second generation unit configured to generate a refraction residual sequence based on the initial set of observations; a fitting unit configured to perform least-squares fitting of the refraction residual sequence and the theoretical refraction model to solve for the local optimal model coefficients; and a correction unit configured to perform real-time refraction correction on the atmospheric refraction path in the initial observations using the local optimal model coefficients to generate corrected observations.

[0009] Thirdly, some embodiments of this disclosure provide an electronic device, including: one or more processors; and a storage device having one or more programs stored thereon, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method described in any implementation of the first aspect above.

[0010] Fourthly, some embodiments of this disclosure provide a computer-readable medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the method described in any of the implementations of the first aspect above.

[0011] The above-described embodiments of this disclosure have the following beneficial effects: The short-wave infrared atmospheric refraction model correction method of some embodiments of this disclosure. Specifically, addressing the problem that traditional astronomical atmospheric refraction correction methods mainly focus on visible light, constructing an atmospheric refraction correction model suitable for the near-infrared band helps to improve the theory and method of astronomical atmospheric refraction correction. Establishing a near-infrared band astronomical atmospheric refraction correction model corrects the error of starlight vectors in astronomical geodesy caused by changes in the propagation path of near-infrared light in the atmosphere. Based on this, the residual error of atmospheric refraction correction is adjusted, reducing the impact of atmospheric refraction on near-infrared band astronomical geodesy and improving the data calculation accuracy of infrared all-weather astronomical geodesy. This provides a new means for infrared all-weather astronomical measurement and navigation. Astronomical navigation can be used for submarine, UAV, and satellite positioning and navigation; it can also be used in aerospace, marine surveying, and emergency rescue operations. When GNSS signals are interfered with or have excessive errors, astronomical navigation can independently provide position information, ensuring system safety and reliability. This patented method can significantly improve the accuracy and reliability of astronomical navigation. After applying this model, the astronomical navigation system achieves millimeter-level positioning accuracy in daylight, darkness, and adverse weather conditions, effectively improving overall positioning accuracy, expanding application areas, and providing guidance for practical applications. Attached Figure Description

[0012] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. Throughout the drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic, and elements are not necessarily drawn to scale.

[0013] Figure 1 This is a flowchart of some embodiments of the shortwave infrared atmospheric refraction model correction method according to the present disclosure;

[0014] Figure 2 These are schematic diagrams of some embodiments of the shortwave infrared atmospheric refraction model correction device according to this disclosure;

[0015] Figure 3 This is a schematic diagram of the structure of an electronic device suitable for implementing some embodiments of the present disclosure;

[0016] Figure 4 This is a schematic diagram of atmospheric refraction according to some embodiments of the shortwave infrared atmospheric refraction model correction method of this disclosure;

[0017] Figure 5 This is a global standard atmospheric model diagram based on some embodiments of the shortwave infrared atmospheric refraction model correction method disclosed herein. Detailed Implementation

[0018] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0019] It should also be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings. Unless otherwise specified, the embodiments and features described in this disclosure can be combined with each other.

[0020] It should be noted that the concepts of "first" and "second" mentioned in this disclosure are used only to distinguish different devices, modules or units, and are not used to limit the order of functions performed by these devices, modules or units or their interdependencies.

[0021] It should be noted that the terms "a" and "a plurality of" used in this disclosure are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0022] The names of messages or information exchanged between multiple devices in the embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.

[0023] This disclosure will now be described in detail with reference to the accompanying drawings and embodiments.

[0024] Figure 1 A flowchart 100 is shown, illustrating some embodiments of a shortwave infrared atmospheric refraction model correction method according to the present disclosure. This shortwave infrared atmospheric refraction model correction method includes the following steps:

[0025] Step 101: Collect the initial set of observations and meteorological metadata for the observation station.

[0026] In some embodiments, the implementer of the shortwave infrared atmospheric refraction model correction method (e.g., a computing device) may collect an initial set of observations and a meteorological metadata set for the observation station.

[0027] Here, the aforementioned initial observation set can be a collection of raw measurement data that has not yet undergone atmospheric refraction correction. The aforementioned meteorological metadata set can be a collection of key physical quantities characterizing the near-surface atmospheric state, collected in real-time by meteorological sensors installed at the station. The aforementioned raw measurement data can be the pixel coordinates of the centroid of a star's image.

[0028] Optionally, the aforementioned implementing entity can collect the initial set of observations and meteorological metadata for the observation station through the following steps:

[0029] The first step is to extract the centroids of stars from the acquired raw observation images to generate initial observation values ​​for each star, thus obtaining an initial observation set. The raw observation images include at least one star, and the raw star images are acquired by a ground-based shortwave infrared telescope.

[0030] As an example, the aforementioned execution entity can use image processing algorithms to identify and calculate the precise center position coordinates of each star image point on an image sensor (e.g., an infrared detector) to obtain an initial set of observations.

[0031] The second step is to collect meteorological metadata from the meteorological sensors at the station. The meteorological metadata includes total air pressure, thermal temperature, and water vapor pressure.

[0032] Here, the total air pressure mentioned above can be expressed as: , representing the atmospheric pressure at the station. Thermodynamic temperature can be expressed as... , represents the atmospheric temperature at the station (unit: Kelvin K). Water vapor pressure represents the partial pressure of water vapor in the air, used to calculate water vapor density. .

[0033] Step 102: Geographically match the global standard atmospheric model to generate a set of atmospheric physical parameters.

[0034] In some embodiments, the aforementioned implementing entity may perform geographic matching of a global standard atmospheric model to generate a set of atmospheric physical parameters.

[0035] Here, the aforementioned global standard atmospheric model includes Earth's atmospheric model parameters for tropical, temperate, and polar regions. For example, the geocentric distance from the top of the troposphere. Temperature in the stratosphere The temperature lapse rate β in the troposphere, the gravitational acceleration g at the center of mass of the local gas column at the station, the geocentric distance r1 of the station, the total atmospheric pressure P1 and thermodynamic temperature T1 near the station, and the global standard atmospheric model such as Figure 5 As shown.

[0036] As an example, the aforementioned implementing entity can select corresponding constant parameters from a global standard atmospheric model based on the geographical region (e.g., tropical, temperate, polar) where the station is located to generate a set of atmospheric physical parameters. This set of atmospheric physical parameters can characterize the average vertical structure of the atmosphere in that region.

[0037] Step 103: Based on the above atmospheric physical parameter set and the above meteorological metadata set, generate a theoretical refraction model containing undetermined coefficients.

[0038] In some embodiments, the aforementioned execution entity may generate a theoretical refraction model containing undetermined coefficients based on the aforementioned atmospheric physical parameter set and the aforementioned meteorological metadata set.

[0039] Optionally, the aforementioned implementing entity can generate a theoretical refraction model containing undetermined coefficients based on the aforementioned atmospheric physical parameter set and meteorological metadata dataset through the following steps:

[0040] The first step is to fix the carbon dioxide content in the group refractive index formula based on the above atmospheric physical parameter set and meteorological metadata set to generate the dry air group refractive index difference and the water vapor group refractive index difference.

[0041] Here, the refractive index difference of the dry air group is The refractive index difference between the water vapor group and the water vapor group is The expression form is: , . and λ and λ' are the dispersion coefficients of the i-th absorption line in dry atmosphere and water vapor, respectively, and λ is the wavelength in vacuum, in μm. It is the negative square of the center wavelength of the i-th atmospheric absorption line.

[0042] Under standard meteorological conditions in dry atmosphere, the group refractive index difference in dry atmosphere is: .in, , .in, It is the wavelength in a vacuum, measured in micrometers. It refers to the concentration of carbon dioxide in the Earth's atmosphere, expressed in ppm (parts per million). =375ppm. The above. This can represent a scaling factor that controls the magnitude of the absolute value of the refractive index. (The above...) It can represent a scaling factor related to dispersion characteristics.

[0043] The second step is to calculate the ratio between the above meteorological metadata dataset and the standard density value to generate the actual dry air density and the actual water vapor density.

[0044] Here, the actual dry air density mentioned above can be The actual water vapor density mentioned above can be... The above standard density values ​​can be preset density values, and no specific numerical limit is specified here.

[0045] The third step is to perform a weighted summation of the refractive index difference of the dry air group, the refractive index difference of the water vapor group, the actual dry air density, and the actual water vapor density to generate the total group refractive index difference.

[0046] As an example, the aforementioned implementing entity can address the refractive index difference of the aforementioned dry air group. The refractive index difference of the above-mentioned water vapor groups The above actual dry air density Compared with the above actual water vapor density Perform a weighted sum to generate the total group refractive index difference: .in, This represents the group refractive index difference in the near-infrared band, which is the actual group refractive index difference of the atmosphere (including dry air and water vapor) in the near-infrared band. This is the reference density of dry atmosphere under standard meteorological conditions. This is the reference density of water vapor under standard conditions.

[0047] The fourth step is to perform vertical layered integration on the above total group refractive index difference to generate an array of atmospheric refraction path cumulative effects.

[0048] As an example, the aforementioned executing entity can first calculate the thermodynamic temperature. The thermodynamic temperature at the top of the troposphere in the atmospheric distribution model is: . This represents the rate of temperature increase. Then calculate... The phase refractive index of broadcasts near the ground is .in, The wavelength of the observed light wave is expressed in μm. n represents the atmospheric refractive index. This represents the ground refractive index modulus. Then, calculate ( -1). The troposphere is divided into two layers, with the atmospheric pressure at the bottom of the first layer being... ,temperature The phase refractive index of the atmosphere near the ground is The distance from the Earth's center is The temperature increase rate in the first layer is The atmospheric pressure at the bottom of the second layer is... Temperature is Atmospheric refractive index is The distance from the Earth's center is The temperature increase rate in the second layer is β2, and the top of the second layer is adjacent to the stratosphere. Therefore: .right Calculating with -1, we get: Among them, the atmospheric constant is Then, calculate. Define the integral array of the stratosphere. Its value is expressed as: . This represents the molar mass of dry air. R represents the specific atmospheric constant, with units of... Where J = 0, 1, 2, ..., L = 0, 1, 2, ..., considering only the static components in (n-1), it can be decomposed into: .make The cumulative effect array of atmospheric refraction paths is obtained: In the formula, It is an integral array in the stratosphere. It is an integral array in the troposphere. In the stratosphere... middle, The integral array can be simplified as follows: In the formula, It is the distance from the Earth's center to the top of the troposphere. This indicates the distance from the center of the station to the Earth's center. This represents the geocentric distance from the top of the troposphere. `integer index` represents the dimension of the integral array. If we treat the convection layer as a single layer, then... The unified expression for (J=0, 1, 2, ..., L=0, 1, 2, ...) is: . Indicates the temperature at the bottom of the stratosphere The power of L. Indicates ground temperature The power of L.

[0049] Define another array H(J,l) within it, of the form: Agreement ,but: .

[0050] The fifth step is to construct a series expansion of the refraction angle based on the above-mentioned array of cumulative effects of atmospheric refraction paths, so as to generate a theoretical refraction model containing undetermined coefficients.

[0051] As an example, the aforementioned execution entity can simultaneously treat the troposphere as one layer. Then, construct the series expansion of the refraction angle E(k). .in, same k represents a non-negative integer index used to identify the astronomical atmospheric refraction angle. The order of each term in the series expansion.

[0052] Step 104: Generate a refraction residual sequence based on the initial set of observations mentioned above.

[0053] In some embodiments, the aforementioned execution entity may generate a refraction residual sequence based on the aforementioned initial observation set.

[0054] Optionally, the aforementioned execution entity can generate a refraction residual sequence based on the aforementioned initial observation set through the following steps:

[0055] The first step is to perform zenith distance transformation on the initial observation set to generate an apparent zenith distance dataset.

[0056] As an example, the aforementioned entity can convert initial observations (such as pixel coordinates) based on the instrument coordinate system into an apparent zenith distance with the station's zenith as a reference, using known telescope optical parameters, pointing model, and station location. This is the angle (observed value) between the observed target and the zenith direction. Due to atmospheric refraction, this is the angle between the target's actual direction as seen by the observer and the zenith. It is not equal to the target's true direction.

[0057] The second step is to compare the above apparent zenith distance dataset with the known true zenith distances of stars to generate a refraction residual sequence.

[0058] As an example, the aforementioned implementing entity can determine the true zenith distance when electromagnetic waves reach the ground station. zenith distance The following relationship exists: .in, It is due to atmospheric refraction. This represents a dimensionless intermediate quantity. Under the standard atmospheric model, its fundamental differential equation is: z is the zenith distance. Let: According to Bouguer's formula, we get: Assuming in In this case, expanding it yields: Defined within: Define array E(K): The series expansion of astronomical atmospheric refraction is obtained as follows: .

[0059] Step 105: Perform least-squares fitting on the above refraction residual sequence and the above theoretical refraction model to solve for the local optimal model coefficients.

[0060] In some embodiments, the execution entity may perform least-squares fitting on the refraction residual sequence and the theoretical refraction model to solve for the local optimal model coefficients.

[0061] Here, as mentioned above.

[0062] As an example, the above execution entity can, when k is 2, [perform the following]. The second-order expansion is expressed as: .like Figure 4 As shown, Figure 4 In the diagram, A represents the actual position of the celestial body; The dashed line represents the position of a celestial body as observed by the observer; the true path of light rays is represented by the dashed line; the apparent path of light rays is represented by the dashed line. The zenith distance from the actual location A; Apparent location Zenith distance; The angle of refraction is the change in zenith distance due to refraction. If we observe n stars using an infrared all-weather astronomical measurement system, then we have: At this point, the local optimal model coefficients A and B can be obtained using the least squares method.

[0063] Step 106: Use the above-mentioned local optimal model coefficients to perform real-time refraction correction on the atmospheric refraction path in the initial observations to generate corrected observations.

[0064] In some embodiments, the aforementioned execution entity may use the aforementioned local optimal model coefficients to perform real-time refraction correction on the atmospheric refraction path in the initial observations to generate corrected observations.

[0065] As an example, the aforementioned execution entity can use the fitted local optimal model coefficients for any star (including calibration stars and new target stars). , According to the model Calculate its precise refraction correction value Then subtract it from the observed value to obtain the corrected observed value. .

[0066] Further reference Figure 2 As an implementation of the methods shown in the above figures, this disclosure provides some embodiments of a shortwave infrared atmospheric refraction model correction device, which are similar to... Figure 1 Corresponding to the method embodiments shown, this shortwave infrared atmospheric refraction model correction device can be specifically applied to various electronic devices.

[0067] like Figure 2 As shown, a shortwave infrared atmospheric refraction model correction device 200 in some embodiments includes: an acquisition unit 201, a matching unit 202, a first generation unit 203, a second generation unit 204, a fitting unit 205, and a correction unit 206. The acquisition unit 201 is configured to acquire an initial set of observations and a meteorological metadata dataset for the observation station; the matching unit 202 is configured to perform geographic matching with a global standard atmospheric model to generate an atmospheric physical parameter set; the first generation unit 203 is configured to generate a theoretical refraction model containing undetermined coefficients based on the atmospheric physical parameter set and the meteorological metadata dataset; the second generation unit 204 is configured to generate a refraction residual sequence based on the initial set of observations; the fitting unit 205 is configured to perform least-squares fitting of the refraction residual sequence and the theoretical refraction model to solve for the local optimal model coefficients; and the correction unit 206 is configured to use the local optimal model coefficients to perform real-time refraction correction on the atmospheric refraction path in the initial observations to generate corrected observations.

[0068] It is understandable that the elements described in the shortwave infrared atmospheric refraction model correction device 200 are related to the reference. Figure 1 The steps in the described method correspond accordingly. Therefore, the operations, features, and beneficial effects described above for the method also apply to the shortwave infrared atmospheric refraction model correction device 200 and the units contained therein, and will not be repeated here.

[0069] The following is for reference. Figure 3 It shows a schematic diagram of the structure of an electronic device 300 (e.g., a computing device) suitable for implementing some embodiments of the present disclosure. Figure 3 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments of this disclosure.

[0070] like Figure 3As shown, the electronic device 300 may include a processing unit 301 (e.g., a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 302 or a program loaded from a storage device 308 into a random access memory (RAM) 303. The RAM 303 also stores various programs and data required for the operation of the electronic device 300. The processing unit 301, ROM 302, and RAM 303 are interconnected via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0071] Typically, the following devices can be connected to I / O interface 305: input devices 306 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 307 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 308 including, for example, magnetic tapes, hard disks, etc.; and communication devices 309. Communication device 309 allows electronic device 300 to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 3 An electronic device 300 with various devices is shown; however, it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed alternatively. Figure 3 Each box shown can represent a device or multiple devices as needed.

[0072] In particular, according to some embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, some embodiments of this disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication device 309, or installed from storage device 308, or installed from ROM 302. When the computer program is executed by processing device 301, it performs the functions defined in the methods of some embodiments of this disclosure.

[0073] It should be noted that, in some embodiments of this disclosure, the computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium may be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In some embodiments of this disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In some embodiments of this disclosure, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0074] In some implementations, clients and servers can communicate using any currently known or future-developed network protocol such as HTTP (Hypertext Transfer Protocol) and can interconnect with digital data communication (e.g., communication networks) of any form or medium. Examples of communication networks include local area networks (“LANs”), wide area networks (“WANs”), the Internet (e.g., the Internet of Things), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future-developed networks.

[0075] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device. The aforementioned computer-readable medium carries one or more programs that, when executed by the electronic device, cause the electronic device to: acquire an initial set of observations and a meteorological metadata dataset for the observation station; perform geographic matching with a global standard atmospheric model to generate a set of atmospheric physical parameters; generate a theoretical refraction model containing undetermined coefficients based on the aforementioned atmospheric physical parameter set and the aforementioned meteorological metadata dataset; generate a refraction residual sequence based on the aforementioned initial set of observations; perform least-squares fitting of the aforementioned refraction residual sequence with the aforementioned theoretical refraction model to solve for the local optimal model coefficients; and use the aforementioned local optimal model coefficients to perform real-time refraction correction on the atmospheric refraction path in the initial observations to generate corrected observations.

[0076] Computer program code for performing operations of some embodiments of this disclosure can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0077] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0078] The functions described above in this document can be performed at least in part by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), system-on-a-chip (SoCs), complex programmable logic devices (CPLDs), and so on.

[0079] The above description is merely a selection of preferred embodiments of this disclosure and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in the embodiments of this disclosure is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described inventive concept. For example, technical solutions formed by substituting the above-described features with (but not limited to) technical features with similar functions disclosed in the embodiments of this disclosure.

Claims

1. A method for correcting a shortwave infrared atmospheric refraction model, characterized in that, include: Collect initial observation sets and meteorological metadata for the observation stations; Geographically match global standard atmospheric models to generate a set of atmospheric physical parameters; Based on the atmospheric physical parameter set and the meteorological metadata set, a theoretical refraction model containing undetermined coefficients is generated; Based on the initial set of observations, a refraction residual sequence is generated; The refraction residual sequence is fitted with the theoretical refraction model using least squares to solve for the local optimal model coefficients; The local optimal model coefficients are used to perform real-time refraction correction on the atmospheric refraction path in the initial observations to generate corrected observations.

2. The method according to claim 1, characterized in that, The data collection targets the initial observation set and meteorological metadata set of the observation station, including: The centroids of stars are extracted from the acquired raw observation images to generate initial observation values ​​for each star, resulting in an initial observation set. The raw observation images include at least one star, and the raw star images are acquired by a ground-based shortwave infrared telescope. Meteorological metadata is collected from meteorological sensors at the monitoring station, wherein the meteorological metadata includes: total atmospheric pressure, thermal temperature, and water vapor pressure.

3. The method according to claim 1, characterized in that, The step of generating a refraction residual sequence based on the initial set of observations includes: The initial set of observations is transformed by zenith distance to generate an apparent zenith distance dataset; The apparent zenith distance dataset is compared with the known true zenith distances of stars to generate a refraction residual sequence.

4. The method according to claim 1, characterized in that, The step of generating a theoretical refraction model containing undetermined coefficients based on the atmospheric physical parameter set and the meteorological metadata set includes: Based on the atmospheric physical parameter set and the meteorological metadata set, the carbon dioxide content is fixed in the group refractive index formula to generate the dry air group refractive index difference and the water vapor group refractive index difference. The ratio of the meteorological metadata dataset to the standard density value is calculated to generate the actual dry air density and the actual water vapor density. The refractive index difference of the dry air group, the refractive index difference of the water vapor group, the actual dry air density, and the actual water vapor density are weighted and summed to generate the total group refractive index difference; The total group refractive index difference is subjected to vertical layered integration to generate an array of atmospheric refraction path cumulative effects. Based on the array of cumulative effects of atmospheric refraction paths, a series expansion of the refraction angle is constructed to generate a theoretical refraction model containing undetermined coefficients.

5. A shortwave infrared atmospheric refraction model correction device, characterized in that, include: The data acquisition unit is configured to acquire the initial set of observations and meteorological metadata for the observation station; The matching unit is configured to perform geographic matching with global standard atmospheric models to generate a set of atmospheric physical parameters; The first generation unit is configured to generate a theoretical refraction model containing undetermined coefficients based on the atmospheric physical parameter set and the meteorological metadata set. The second generation unit is configured to generate a refraction residual sequence based on the initial set of observations; The fitting unit is configured to perform least-squares fitting on the refraction residual sequence and the theoretical refraction model to solve for the local optimal model coefficients; The correction unit is configured to use the local optimal model coefficients to perform real-time refraction correction on the atmospheric refraction path in the initial observations to generate corrected observations.

6. An electronic device, characterized in that, include: One or more processors; A storage device on which one or more programs are stored; When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any one of claims 1 to 4.

7. A computer-readable medium, characterized in that, It stores a computer program thereon, wherein the computer program, when executed by a processor, implements the method as described in any one of claims 1 to 4.

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