Three-dimensional atmospheric tomography method and system for low-altitude airborne beidou observation

Abstract

The application provides a three-dimensional atmospheric tomography method and system for low-altitude airborne Beidou observation, comprising: obtaining dynamic airborne Beidou observation data, processing the dynamic airborne Beidou observation data by using a preset GNSS processing software to obtain an airborne information set; projecting Beidou zenith troposphere delay information onto a slant path by using signal elevation angle and azimuth angle information to obtain Beidou slant path troposphere delay information; determining the boundary space position, horizontal resolution and vertical resolution of each layer of tomography according to the aircraft position information and the Beidou slant path troposphere delay information through a dynamic adaptive tomographic boundary optimization algorithm; constructing a Beidou atmospheric tomography observation equation according to the intercept information of the Beidou signal in the tomographic voxel block; solving the tomographic equation set by using meteorological background field data through an adaptive algebraic reconstruction algorithm; and performing smoothing processing on the tomographic horizontal grid result by using a Gaussian filtering method to obtain the final three-dimensional atmospheric tomographic result.

Description

Technical Field

[0001] This invention relates to the field of GNSS meteorology technology, and in particular to a three-dimensional atmospheric tomography method and system for low-altitude airborne BeiDou observation. Background Technology

[0002] Atmospheric water vapor is a key element of weather and climate systems, and accurate atmospheric water vapor information plays a crucial role in improving the accuracy of weather forecasts, monitoring extreme weather, and studying climate change. Therefore, obtaining high-precision, high spatiotemporal resolution water vapor information is of great significance for the monitoring and forecasting of severe weather. Global Navigation Satellite System (GNSS) water vapor detection technology, including BeiDou, offers advantages such as global all-weather observation, high precision, high spatiotemporal resolution, low cost, and ease of use, providing a new means for detecting tropospheric atmospheric water vapor compared to traditional methods such as radiosonde observation, satellite remote sensing, and microwave radiometers.

[0003] Ground-based GNSS receivers within a specific area can not only obtain comprehensive two-dimensional spatial distribution information of water vapor in the troposphere along the vertical direction, but also reconstruct three-dimensional profile information of water vapor in the vertical direction in a local area using tomography techniques. Three-dimensional water vapor information is more capable of capturing finer climate change information than two-dimensional water vapor information. With the increasing maturity of three-dimensional water vapor tomography technology, dense GNSS observation networks can be used to acquire real-time, high spatiotemporal resolution three-dimensional tropospheric water vapor information. Combined with numerical weather prediction models, the spatiotemporal variation characteristics of water vapor can be monitored more accurately. However, because ground-based GNSS relies on ground-based observation stations, it suffers from low horizontal and vertical resolution. Airborne equipment, such as aircraft, can fly receivers from the ground to high altitudes, offering flexible spatiotemporal operations. A simple flight over the area of ​​interest is sufficient to acquire the required water vapor information, providing higher spatial resolution water vapor information compared to ground-based systems. With the booming development of the low-altitude economy, airborne GNSS water vapor detection, represented by unmanned aerial vehicles (UAVs), shows promising prospects. However, current traditional three-dimensional atmospheric tomography methods mainly rely on fixed site locations and fixed tomographic boundaries, which are difficult to adapt to highly dynamic aircraft carriers. Summary of the Invention

[0004] This invention provides a three-dimensional atmospheric tomography method and system for low-altitude airborne BeiDou observation, which addresses the shortcomings of existing technologies, enables the provision of refined airborne three-dimensional atmospheric tomography products, and broadens the application scenarios of low-altitude aircraft. In addition, the three-dimensional monitoring of water vapor distribution and vertical variation with high spatiotemporal resolution is of great research significance and application value for capturing local water vapor anomalies, improving numerical weather models, and expanding water vapor meteorological services.

[0005] In a first aspect, the present invention provides a three-dimensional atmospheric tomography method for low-altitude airborne BeiDou observations, comprising: Acquire dynamic airborne BeiDou observation data, process the dynamic airborne BeiDou observation data using preset GNSS processing software, and obtain aircraft position information, BeiDou zenith tropospheric delay information, signal elevation angle and azimuth angle information, and phase residual information. By projecting the BeiDou zenith tropospheric delay information onto the oblique path using signal elevation angle and azimuth angle information, the oblique path tropospheric delay information of BeiDou is obtained. Based on the aircraft location information and the BeiDou oblique path tropospheric delay information, the boundary spatial position, horizontal resolution and vertical resolution of each tomographic layer are determined by a dynamic adaptive tomographic boundary optimization algorithm. The BeiDou atmospheric tomography observation equation is constructed based on the intercept information of the BeiDou signal in the tomographic voxel block. The tomographic equations were solved using meteorological background field data and an adaptive algebraic reconstruction algorithm. The Gaussian filtering method was used to smooth the horizontal grid results of the tomography to obtain the final three-dimensional atmospheric tomography results.

[0006] According to a three-dimensional atmospheric tomography method for low-altitude airborne BeiDou observations provided by the present invention, the tropospheric wet delay ZWD is obtained by subtracting the dry tropospheric delay ZHD from the acquired BeiDou zenith tropospheric delay information ZTD. The ZHD is calculated using the following formula:

[0007] In the formula, P s This is the air pressure value at the aircraft location, in hPa. φ The latitude of the station is expressed in radians. h This refers to the aircraft altitude, measured in km. Considering the influence of the horizontal gradient, the tropospheric wet delay (ZWD) and the tropospheric water vapor SWV along the oblique path are obtained by the following formulas using the tropospheric wet delay (ZWD) and the projection function:

[0008]

[0009] In the formula, e and a These are the elevation angle and azimuth angle of the BeiDou signal, respectively. For wet projection functions; and These are the horizontal moisture gradients in the east-west and north-south directions, respectively. The atmospheric weighted average temperature is calculated from the air temperature at the aircraft using a model. The density of liquid water; The constant of water vapor; , These are atmospheric physical parameters, with empirical values ​​as follows: and .

[0010] According to the present invention, a three-dimensional atmospheric tomography method for low-altitude airborne BeiDou observations is provided, wherein the dynamic adaptive tomography boundary optimization algorithm includes: The location of the aircraft per second within a preset short time period is determined as a static GNSS station, and the aircraft flight trajectory within the preset short time period is treated as a series of stations for unified processing. The horizontal resolution of the tomography model is determined by the aircraft's horizontal flight speed and the observation sampling interval, while the vertical resolution of the tomography model is determined by fixing constant values ​​or based on the actual vertical distribution of water vapor in meteorological profile data. Set the first layer of the airborne tomography model in the current flight cycle to the layer closest to the minimum flight altitude; Determine the height of each layer in the tomographic model, and calculate the intersection point of the GNSS rays with each layer based on the aircraft position, satellite elevation angle, and azimuth angle; For any given layer, determine the maximum and minimum latitude and longitude of all ray intersections. Starting from the minimum latitude and longitude, delineate each tomographic node outwards with a horizontal resolution step size until all piercing intersections are included. The boundaries of each layer of the entire airborne 3D tomographic model are determined sequentially from the first layer to the top layer.

[0011] According to the present invention, a three-dimensional atmospheric tomography method for low-altitude airborne BeiDou observation is provided, which constructs a BeiDou atmospheric tomography observation equation based on the intercept information of the BeiDou signal in the tomographic voxel block, including: The integral values ​​of SWD or SWV are approximated by the Newton-Cotes integral formula, and a functional relationship is established between the wet refractive index or water vapor density parameters of each node and the wet refractive index or water vapor density of the Newton-Cotes interpolation points on the BeiDou signal path. The tomographic observation equations corresponding to BeiDou signals after node-based parameterization are determined, and multiple BeiDou signals within the same tomographic epoch form corresponding BeiDou atmospheric tomographic observation equations based on node parameterization:

[0012] In the formula, A is a coefficient matrix composed of the intercepts of the observed values ​​across the grid; X is a parameter matrix representing the wet refractive index or water vapor density at each grid point. m It is the number of rows in the observation equation. n It is the number of observation equations, that is, the number of unknown parameters.

[0013] According to the present invention, a three-dimensional atmospheric tomography method for low-altitude airborne BeiDou observations is provided, wherein the adaptive algebraic reconstruction algorithm includes:

[0014] In the formula, Indicates the first k In the nth iteration j The wet refractive index or water vapor density value of each node basis; λ Indicates the relaxation factor; Represents the first in the coefficient matrix i The first observation value j One parameter; m This indicates the number of rows in all observation equations, including the input SWD or SWV observation equations; n This indicates the number of rows in the observation equation, i.e., the number of unknown parameters.

[0015] According to the present invention, a three-dimensional atmospheric tomography method based on low-altitude airborne BeiDou observations is provided, which uses a Gaussian filtering method to smooth the horizontal grid results of the tomography to obtain the final three-dimensional atmospheric tomography result, including:

[0016] In the formula, Indicates the ( ) i 0, j 0, k 0) Filtered wet refractive index or water vapor density at each node; s Voxels ( i , j , k 0) and ( i 0, j 0, k The distance between 0); σ This parameter represents the standard deviation of the adjusted filter intensity.

[0017] Secondly, the present invention also provides a three-dimensional atmospheric tomography system for low-altitude airborne BeiDou observation, comprising: The acquisition module is used to acquire dynamic airborne BeiDou observation data. It uses preset GNSS processing software to process the dynamic airborne BeiDou observation data to obtain aircraft position information, BeiDou zenith tropospheric delay information, signal elevation angle and azimuth angle information, and phase residual information. The projection module is used to project the BeiDou zenith tropospheric delay information onto the oblique path using the signal elevation angle and azimuth angle information to obtain the BeiDou oblique path tropospheric delay information. The determination module is used to determine the boundary spatial position, horizontal resolution, and vertical resolution of each tomographic layer based on the aircraft position information and the BeiDou oblique path tropospheric delay information, using a dynamic adaptive tomographic boundary optimization algorithm. The module is used to construct the BeiDou atmospheric tomography observation equation based on the intercept information of the BeiDou signal in the tomographic voxel block; The solution module is used to solve the tomographic equations using meteorological background field data through an adaptive algebraic reconstruction algorithm. The processing module is used to smooth the horizontal grid results of the tomography using a Gaussian filtering method to obtain the final three-dimensional atmospheric tomography results.

[0018] Thirdly, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the three-dimensional atmospheric tomography method for low-altitude airborne BeiDou observation as described above.

[0019] Fourthly, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the three-dimensional atmospheric tomography method for low-altitude airborne BeiDou observation as described above.

[0020] The present invention provides a three-dimensional atmospheric tomography method and system for low-altitude airborne BeiDou observation. By performing three-dimensional atmospheric tomography on airborne BeiDou data, this method can effectively utilize the high sampling rate of airborne BeiDou information, proving the feasibility of using aircraft for real-time three-dimensional atmospheric detection. The airborne dynamic adaptive tomography boundary optimization algorithm established in this invention can effectively utilize all BeiDou observation signals and adapt to changes in the highly dynamic aircraft position and signal propagation path compared to traditional three-dimensional tomography methods. This invention can utilize current low-altitude aircraft to conduct directional tracking observations of targets such as typhoons and severe convection, fill the gaps in ground-based stations such as those in oceans and deserts, achieve high spatiotemporal resolution monitoring, and improve the ability to issue early warnings for extreme weather, thus having strong application value. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0022] Figure 1 This is one of the flowcharts of the three-dimensional atmospheric tomography method for low-altitude airborne BeiDou observation provided by the present invention; Figure 2 This is a schematic diagram of the airborne dynamic adaptive tomography boundary optimization algorithm provided by the present invention; Figure 3 This is the second flowchart of the three-dimensional atmospheric tomography method for low-altitude airborne BeiDou observation provided by the present invention; Figure 4 This invention provides actual aircraft flight information and a distribution map of the ground stations used. Figure 5 This is a diagram of an actual airborne tomography result provided by the present invention; Figure 6 This is a schematic diagram of the structure of the three-dimensional atmospheric tomography system for low-altitude airborne BeiDou observation provided by the present invention; Figure 7 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0024] Figure 1 This is one of the flowcharts illustrating the three-dimensional atmospheric tomography method for low-altitude airborne BeiDou observations provided by this invention, such as... Figure 1 As shown, it includes: Step 100: Acquire dynamic airborne BeiDou observation data, process the dynamic airborne BeiDou observation data using preset GNSS processing software, and obtain aircraft position information, BeiDou zenith tropospheric delay information, signal elevation angle and azimuth angle information, and phase residual information. Step 200: Project the BeiDou zenith tropospheric delay information onto the oblique path using the signal elevation angle and azimuth angle information to obtain the BeiDou oblique path tropospheric delay information; Step 300: Based on the aircraft position information and the BeiDou oblique path tropospheric delay information, determine the boundary spatial position, horizontal resolution and vertical resolution of each tomographic layer using a dynamic adaptive tomographic boundary optimization algorithm; Step 400: Construct the BeiDou atmospheric tomography observation equation based on the intercept information of the BeiDou signal in the tomographic voxel block; Step 500: Solve the tomographic equations using the adaptive algebraic reconstruction algorithm based on the meteorological background field data; Step 600: Use Gaussian filtering to smooth the horizontal grid results of the tomography to obtain the final three-dimensional atmospheric tomography results.

[0025] Specifically, the embodiments of the present invention include performing dynamic precise single-point positioning processing on dynamic airborne BeiDou observation data to obtain the aircraft position and BeiDou tropospheric oblique path delay information, determining the dynamic tomographic boundary spatial position of the airborne BeiDou signal through a dynamic adaptive tomographic boundary optimization algorithm, constructing the BeiDou atmospheric tomographic observation equation based on the spatial position relationship between the BeiDou signal and the dynamic tomographic boundary, solving the tomographic equation set using an adaptive algebraic reconstruction algorithm, and finally smoothing the tomographic horizontal grid results using a Gaussian filtering method to obtain the final airborne three-dimensional atmospheric tomographic results.

[0026] Further, in step 100, the raw airborne BeiDou observation data is processed using PANDA software to obtain the three-dimensional coordinate position information of the aircraft flight path, the BeiDou zenith tropospheric delay information (ZTD), the signal elevation angle and azimuth angle information, and the phase residual information.

[0027] Further, in step 200, the tropospheric dry delay ZHD is subtracted from the ZTD obtained in step one to obtain the tropospheric wet delay ZWD, wherein the ZHD can be calculated using the following formula (1): (1) In the formula, P s This refers to the air pressure (hPa) at the aircraft location. φ The latitude (in radians) of the station. h Let be the aircraft altitude (km). Considering the horizontal gradient effect, the tropospheric wet delay SWD and the tropospheric water vapor SWV along the oblique path can be obtained from the following formulas using ZWD and the projection function: (2) (3) In the formula, e and a These are the elevation angle and azimuth angle of the BeiDou signal, respectively. For wet projection functions; and These are the horizontal moisture gradients in the east-west and north-south directions, respectively. The atmospheric weighted average temperature can be calculated from the air temperature at the aircraft using a model. The density of liquid water; The constant of water vapor; , These are atmospheric physical parameters; their empirical values ​​are respectively and .

[0028] Furthermore, the dynamic adaptive tomographic boundary optimization algorithm in step 300 is described below: First, it is assumed that the aircraft's location per second in the short term is considered a static GNSS station, and the aircraft's flight trajectory in the short term (limited to a maximum of 1 hour) is treated as a series of stations for unified processing. The horizontal resolution of the tomography model can be determined by the aircraft's horizontal flight speed and the observation sampling interval. For example, with a speed of 100 m / s and a sampling interval of 10 seconds, the horizontal resolution is 1 km. Considering the complex aircraft motion, it is more robust to reduce the horizontal resolution slightly; here, it is recommended to multiply by 2, i.e., use a horizontal resolution of 2 km. Then, the vertical resolution of the tomography model can be determined by setting a fixed constant value or based on the actual vertical distribution of water vapor in the meteorological profile data. Since the aircraft flies at high altitudes most of the time, and the GNSS ray path used for tomography is located above the aircraft, after determining the vertical resolution of the tomography layers, the first layer of the airborne tomography model in the current flight cycle can be set to the layer closest to the lowest flight altitude. After determining the height of each layer of the tomography model, the intersection point of the GNSS ray with each layer is calculated based on the aircraft position, satellite elevation angle, and azimuth angle. For the first... i Layer, determine the maximum and minimum latitude and longitude of all these ray intersections, and starting from the minimum value, delineate each tomographic node outwards with a horizontal resolution step size until all piercing intersections are included. This process defines the ( ) layer. i -1) Layer boundary range. Following the steps above, determine the boundary of each layer of the entire airborne 3D tomographic model sequentially from the first layer to the top layer. Considering that the central ray coverage of the tomographic region is dense while the edge is sparse, and the atmospheric water vapor content is low above 5 kilometers, in order to reduce the number of upper-level voxels, the extra extended boundary region above 5 kilometers was removed when outputting the tomographic results.

[0029] Further, in step 400, the Newton-Cotes integral formula is used to approximate the SWD or SWV integral value, establishing a functional relationship between the wet refractive index or water vapor density parameter of each node and the wet refractive index or water vapor density of the Newton-Cotes interpolation point on the BeiDou signal path. This ultimately determines a tomographic observation equation corresponding to a BeiDou signal after node-based parameterization. Multiple BeiDou signals within the same tomographic epoch can then form a corresponding set of tomographic observation equations based on node parameterization. (4) In the formula, A is a coefficient matrix composed of the intercepts of the observed values ​​across the grid; X is a parameter matrix representing the wet refractive index or water vapor density at each grid point. m It is the number of rows in the observation equation. n It is the number of observation equations, that is, the number of unknown parameters.

[0030] Furthermore, in step 500, the adaptive algebraic reconstruction algorithm is shown in the following formula: (5) In the formula, Indicates the first k In the nth iteration j The wet refractive index or water vapor density value of each node basis; λ This represents the relaxation factor, which typically ranges from 0.05 to 2. Represents the first in the coefficient matrix i The first observation value j One parameter; m This indicates the number of rows in all observation equations, including the input SWD or SWV observation equations; n This indicates the number of rows in the observation equation, i.e., the number of unknown parameters.

[0031] Furthermore, in step 600, the Gaussian filtering method is used to smooth the tomographic horizontal grid results. (6) In the formula, Indicates the ( ) i 0, j 0, k 0) Filtered wet refractive index or water vapor density at each node; s Voxels ( i , j , k 0) and ( i 0, j 0, k The distance between 0). σ This indicates that the standard deviation parameter of the filter intensity can be adjusted, with values ​​of 0.3 and 1 for voxels that have passed through and those that have not passed through any signal rays, respectively.

[0032] Based on the above embodiments, the present invention will be further described in conjunction with specific embodiments.

[0033] like Figure 2 As shown, we first assume that the aircraft's location per second in the short term is considered a static GNSS station, as indicated by the red dots on the aircraft's flight path in the figure. The aircraft's flight path over a short period is then treated as a series of stations for unified processing. The latitude / longitude resolution is determined as λ based on the aircraft's horizontal flight speed. lat / λ lon Next, the height of each layer is set, and the coordinates of the puncture points of each BeiDou signal within the vertical layering interval are calculated. This allows for the acquisition of the maximum and minimum longitude and latitude of all puncture points in each layer. For the first... i Layer, determine the maximum and minimum latitude and longitude of all these ray intersections, starting from the minimum value with a fixed horizontal step size λ. lat / λ lon Delineate each tomographic node outwards until all puncture intersections are included, such as... Figure 2As shown in the horizontal grid on the right, this process defines the ( ) i -1) Layer boundary range. Following the steps above, determine the boundary of each layer of the entire airborne 3D tomographic model sequentially from the first layer to the top layer. Considering that the central ray coverage of the tomographic region is dense while the edge is sparse, and the atmospheric water vapor content is low above 5 kilometers, in order to reduce the number of upper-level voxels, the extra extended boundary region above 5 kilometers was removed when outputting the tomographic results.

[0034] Figure 3 The second flowchart illustrates the three-dimensional atmospheric tomography method for low-altitude airborne BeiDou observation provided by this invention. The process of the three-dimensional atmospheric tomography method for airborne BeiDou observation is as follows: First, raw airborne BeiDou observation data, BeiDou / GNSS orbit and clock bias product data, and satellite catalog data were acquired. The airborne BeiDou data was processed using PANDA data processing software to obtain zenith tropospheric delay (ZTD), horizontal gradient, and phase residual information. Combined with air pressure and temperature observations at the aircraft, airborne oblique path wet delay (SWD) and oblique path water vapor SWV information were obtained. The spatial location of the dynamic tomographic boundary of the airborne BeiDou signal was determined using an airborne dynamic adaptive tomographic boundary optimization algorithm. Then, based on the spatial relationship between the BeiDou signal and the dynamic tomographic boundary, BeiDou atmospheric tomography observation equations were constructed. Spatiotemporal interpolation using meteorological numerical forecast data was used to construct constraint equations, which were then combined with previous tomographic observations and solved using an adaptive algebraic reconstruction algorithm. Finally, Gaussian filtering was used to smooth the tomographic horizontal grid results to obtain the final airborne three-dimensional atmospheric tomography results.

[0035] Figure 4 This invention provides actual aircraft flight information and a distribution map of the ground stations used. Figure 4 The flight area shown in (a) and (b) is located in a certain city. The red cubes represent GNSS ground observation stations, the yellow circles represent radiosonde stations, and the flight path is represented by blue lines. The flight time was from 06:32 to 08:40 on June 4, 2021. Figure 4 (c) represents the change in altitude during the aircraft's flight. Figure 4 In the equation (d), (e), and (f), the flight speeds are in the east, north, and zenith directions, respectively.

[0036] Figure 5 Provided by the present invention Figure 4 The airborne tomography results of the Chinese aircraft flight case are shown in the figure. Figure 4The displayed average horizontal speed of the aircraft is approximately 100 m / s. The aircraft sampling interval is 10 seconds. Therefore, according to the airborne dynamic adaptive tomography boundary optimization algorithm, the horizontal resolution is set to 0.02° (approximately 2 km), and the vertical resolution is set to commonly used empirical values. Specifically, the highest altitude of the tomography model is selected as 11 km. Vertical stratification is performed as follows: from 0 km to 3 km, one layer is taken every 0.3 km; from 3 to 5 km, one layer is taken every 0.5 km; and from 5 to 11 km, one layer is taken every 1 km. A tomography cycle is set to 5 minutes. Taking the tomography cycle from 07:35 to 07:40 as an example... Figure 5 (a) shows the distribution of the tomographic plane nodes in the first layer, along with 10 seconds of aircraft position sampling points (red dots). Figure 5 (b) shows the corresponding airborne tomography model and GNSS ray signal distribution. Figure 5 (c) in the figure represents the airborne three-dimensional chromatographic water vapor density distribution calculated at that time. Figure 5 In the figure, (d) and (e) represent the ratio of nodes involved in parameter estimation to the total number of nodes in airborne tomography at different tomography cycles and altitudes. The results show that the airborne dynamic adaptive tomography boundary optimization algorithm can achieve good node utilization, with an average value of 82.5%, and the utilization rate exceeds 70% at all altitudes. Using the ground tomography results obtained from ground GNSS calculations as a reference, and ERA5 meteorological numerical data as the background field for airborne tomography, the calculated airborne tomography water vapor density results and their corresponding background field at each time step are obtained from... Figure 5 As shown in (f), Figure 5 In the figure, (g) represents the average root mean square error distribution at different altitudes. The results show that the average root mean square error of the airborne chromatography water vapor density is 0.41 g / m³ in the altitude range of 1.2 to 11 km. 3 .joint Figure 4 subgraph (c) and Figure 5 Analysis of subplot (f) shows that when the aircraft's flight altitude is stable, the onboard tomography results are better than the background field. However, when there are drastic changes in flight altitude, the onboard tomography results are poor due to dynamic precision single-point positioning errors. Figure 5 As shown in result (g), the accuracy of airborne tomography in the lower troposphere is significantly better than that of background data. Based on these results, it is recommended that when using aircraft for three-dimensional atmospheric tomography, the aircraft should fly smoothly at the same low altitude to obtain richer atmospheric information and better airborne tomography results.

[0037] The following describes the three-dimensional atmospheric tomography system for low-altitude airborne BeiDou observation provided by this invention. The three-dimensional atmospheric tomography system for low-altitude airborne BeiDou observation described below can be referred to in correspondence with the three-dimensional atmospheric tomography method for low-altitude airborne BeiDou observation described above.

[0038] Figure 6 This is a schematic diagram of the structure of the three-dimensional atmospheric tomography system for low-altitude airborne BeiDou observation provided in an embodiment of the present invention, as shown below. Figure 6 As shown, it includes: an acquisition module 61, a projection module 62, a determination module 63, a construction module 64, a solution module 65, and a processing module 66, wherein: The acquisition module 61 acquires dynamic airborne BeiDou observation data and processes it using preset GNSS processing software to obtain aircraft position information, BeiDou zenith tropospheric delay information, signal elevation angle and azimuth angle information, and phase residual information. The projection module 62 projects the BeiDou zenith tropospheric delay information onto the oblique path using the signal elevation angle and azimuth angle information to obtain BeiDou oblique path tropospheric delay information. The determination module 63 determines the boundary spatial position, horizontal resolution, and vertical resolution of each layer of the tomography based on the aircraft position information and the BeiDou oblique path tropospheric delay information using a dynamic adaptive tomography boundary optimization algorithm. The construction module 64 constructs the BeiDou atmospheric tomography observation equations based on the intercept information of the BeiDou signal in the tomography voxel block. The solution module 65 solves the tomography equations using meteorological background field data through an adaptive algebraic reconstruction algorithm. The processing module 66 smooths the horizontal grid results of the tomography using a Gaussian filtering method to obtain the final three-dimensional atmospheric tomography results.

[0039] Figure 7 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 7As shown, the electronic device may include: a processor 710, a communication interface 720, a memory 730, and a communication bus 740, wherein the processor 710, the communication interface 720, and the memory 730 communicate with each other through the communication bus 740. The processor 710 can call logic instructions in the memory 730 to execute a three-dimensional atmospheric tomography method for low-altitude airborne BeiDou observations. This method includes: acquiring dynamic airborne BeiDou observation data; processing the dynamic airborne BeiDou observation data using pre-set GNSS processing software to obtain aircraft position information, BeiDou zenith tropospheric delay information, signal elevation angle and azimuth angle information, and phase residual information; projecting the BeiDou zenith tropospheric delay information onto an oblique path using the signal elevation angle and azimuth angle information to obtain BeiDou oblique path tropospheric delay information; determining the boundary spatial position, horizontal resolution, and vertical resolution of each tomographic layer using a dynamic adaptive tomographic boundary optimization algorithm based on the aircraft position information and the BeiDou oblique path tropospheric delay information; constructing BeiDou atmospheric tomography observation equations based on the intercept information of the BeiDou signal in the tomographic voxel block; solving the tomographic equation set using an adaptive algebraic reconstruction algorithm with meteorological background field data; and smoothing the tomographic horizontal grid results using a Gaussian filtering method to obtain the final three-dimensional atmospheric tomography result.

[0040] Furthermore, the logical instructions in the aforementioned memory 730 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, essentially, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0041] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the three-dimensional atmospheric tomography method for low-altitude airborne BeiDou observation provided by the above methods. This method includes: acquiring dynamic airborne BeiDou observation data; processing the dynamic airborne BeiDou observation data using preset GNSS processing software to obtain aircraft position information, BeiDou zenith tropospheric delay information, signal elevation angle and azimuth angle information, and phase residual information; and using the signal... Elevation and azimuth information are used to project the BeiDou zenith tropospheric delay information onto the oblique path to obtain the BeiDou oblique path tropospheric delay information. Based on the aircraft position information and the BeiDou oblique path tropospheric delay information, the boundary spatial position, horizontal resolution, and vertical resolution of each tomographic layer are determined through a dynamic adaptive tomographic boundary optimization algorithm. The BeiDou atmospheric tomography observation equations are constructed based on the intercept information of the BeiDou signal in the tomographic voxel block. The tomographic equations are solved using meteorological background field data through an adaptive algebraic reconstruction algorithm. The tomographic horizontal grid results are smoothed using a Gaussian filtering method to obtain the final three-dimensional atmospheric tomography results.

[0042] In another aspect, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon. When executed by a processor, the computer program implements a three-dimensional atmospheric tomography method for low-altitude airborne BeiDou observations provided by the methods described above. This method includes: acquiring dynamic airborne BeiDou observation data; processing the dynamic airborne BeiDou observation data using pre-set GNSS processing software to obtain aircraft position information, BeiDou zenith tropospheric delay information, signal elevation angle and azimuth angle information, and phase residual information; projecting the BeiDou zenith tropospheric delay information onto an oblique path using the signal elevation angle and azimuth angle information to obtain BeiDou oblique path tropospheric delay information; determining the boundary spatial position, horizontal resolution, and vertical resolution of each tomographic layer using a dynamic adaptive tomographic boundary optimization algorithm based on the aircraft position information and the BeiDou oblique path tropospheric delay information; constructing BeiDou atmospheric tomography observation equations based on the intercept information of the BeiDou signal in the tomographic voxel block; solving the tomographic equation set using an adaptive algebraic reconstruction algorithm with meteorological background field data; and smoothing the tomographic horizontal grid results using a Gaussian filtering method to obtain the final three-dimensional atmospheric tomography result.

[0043] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0044] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0045] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A three-dimensional atmospheric tomography method for low-altitude airborne BeiDou observations, characterized in that, include: Acquire dynamic airborne BeiDou observation data, process the dynamic airborne BeiDou observation data using preset GNSS processing software, and obtain aircraft position information, BeiDou zenith tropospheric delay information, signal elevation angle and azimuth angle information, and phase residual information. By projecting the BeiDou zenith tropospheric delay information onto the oblique path using signal elevation angle and azimuth angle information, the oblique path tropospheric delay information of BeiDou is obtained. Based on the aircraft location information and the BeiDou oblique path tropospheric delay information, the boundary spatial position, horizontal resolution and vertical resolution of each tomographic layer are determined by a dynamic adaptive tomographic boundary optimization algorithm. The BeiDou atmospheric tomography observation equation is constructed based on the intercept information of the BeiDou signal in the tomographic voxel block. The tomographic equations were solved using meteorological background field data and an adaptive algebraic reconstruction algorithm. The Gaussian filtering method was used to smooth the horizontal grid results of the tomography to obtain the final three-dimensional atmospheric tomography results.

2. The three-dimensional atmospheric tomography method for low-altitude airborne BeiDou observation according to claim 1, characterized in that, The tropospheric wet delay ZWD is obtained by subtracting the tropospheric dry delay ZHD from the acquired BeiDou zenith tropospheric delay information ZTD. The ZHD is calculated using the following formula: In the formula, P s This is the air pressure value at the aircraft location, in hPa. φ The latitude of the station is expressed in radians. h This refers to the aircraft altitude, measured in km. Considering the influence of the horizontal gradient, the tropospheric wet delay (ZWD) and the tropospheric water vapor SWV along the oblique path are obtained by the following formulas using the tropospheric wet delay (ZWD) and the projection function: In the formula, e and a These are the elevation angle and azimuth angle of the BeiDou signal, respectively. For wet projection functions; and These are the horizontal moisture gradients in the east-west and north-south directions, respectively. The atmospheric weighted average temperature is calculated from the air temperature at the aircraft using a model. The density of liquid water; The constant of water vapor; , These are atmospheric physical parameters, with empirical values ​​as follows: and .

3. The three-dimensional atmospheric tomography method for low-altitude airborne BeiDou observation according to claim 1, characterized in that, The dynamic adaptive tomographic boundary optimization algorithm includes: The location of the aircraft per second within a preset short time period is determined as a static GNSS station, and the aircraft flight trajectory within the preset short time period is treated as a series of stations for unified processing. The horizontal resolution of the tomography model is determined by the aircraft's horizontal flight speed and the observation sampling interval, while the vertical resolution of the tomography model is determined by fixing constant values ​​or based on the actual vertical distribution of water vapor in meteorological profile data. Set the first layer of the airborne tomography model in the current flight cycle to the layer closest to the minimum flight altitude; Determine the height of each layer in the tomographic model, and calculate the intersection point of the GNSS rays with each layer based on the aircraft position, satellite elevation angle, and azimuth angle; For any given layer, determine the maximum and minimum latitude and longitude of all ray intersections. Starting from the minimum latitude and longitude, delineate each tomographic node outwards with a horizontal resolution step size until all piercing intersections are included. The boundaries of each layer of the entire airborne 3D tomographic model are determined sequentially from the first layer to the top layer.

4. The three-dimensional atmospheric tomography method for low-altitude airborne BeiDou observation according to claim 1, characterized in that, Based on the intercept information of BeiDou signals in the tomographic voxel block, the BeiDou atmospheric tomography observation equation is constructed, including: The integral values ​​of SWD or SWV are approximated by the Newton-Cotes integral formula, and a functional relationship is established between the wet refractive index or water vapor density parameters of each node and the wet refractive index or water vapor density of the Newton-Cotes interpolation points on the BeiDou signal path. The tomographic observation equations corresponding to BeiDou signals after node-based parameterization are determined, and multiple BeiDou signals within the same tomographic epoch form corresponding BeiDou atmospheric tomographic observation equations based on node parameterization: In the formula, A is a coefficient matrix composed of the intercepts of the observed values ​​across the grid; X is a parameter matrix representing the wet refractive index or water vapor density at each grid point. m It is the number of rows in the observation equation. n It is the number of observation equations, that is, the number of unknown parameters.

5. The three-dimensional atmospheric tomography method for low-altitude airborne BeiDou observation according to claim 1, characterized in that, The adaptive algebraic reconstruction algorithm includes: In the formula, Indicates the first k In the nth iteration j The wet refractive index or water vapor density value of each node basis; λ Indicates the relaxation factor; Represents the first in the coefficient matrix i The first observation value j One parameter; m This indicates the number of rows in all observation equations, including the input SWD or SWV observation equations; n This indicates the number of rows in the observation equation, i.e., the number of unknown parameters.

6. The three-dimensional atmospheric tomography method for low-altitude airborne BeiDou observation according to claim 1, characterized in that, The Gaussian filtering method is used to smooth the horizontal grid results of the tomography to obtain the final three-dimensional atmospheric tomography results, including: In the formula, Indicates the ( ) i 0, j 0, k 0) Filtered wet refractive index or water vapor density at each node; s Voxels ( i , j , k 0) and ( i 0, j 0, k The distance between 0); σ This parameter represents the standard deviation of the adjusted filter intensity.

7. A three-dimensional atmospheric tomography system for low-altitude airborne BeiDou observation, characterized in that, include: The acquisition module is used to acquire dynamic airborne BeiDou observation data. It uses preset GNSS processing software to process the dynamic airborne BeiDou observation data to obtain aircraft position information, BeiDou zenith tropospheric delay information, signal elevation angle and azimuth angle information, and phase residual information. The projection module is used to project the BeiDou zenith tropospheric delay information onto the oblique path using the signal elevation angle and azimuth angle information to obtain the BeiDou oblique path tropospheric delay information. The determination module is used to determine the boundary spatial position, horizontal resolution, and vertical resolution of each tomographic layer based on the aircraft position information and the BeiDou oblique path tropospheric delay information, using a dynamic adaptive tomographic boundary optimization algorithm. The module is used to construct the BeiDou atmospheric tomography observation equation based on the intercept information of the BeiDou signal in the tomographic voxel block; The solution module is used to solve the tomographic equations using meteorological background field data through an adaptive algebraic reconstruction algorithm. The processing module is used to smooth the horizontal grid results of the tomography using a Gaussian filtering method to obtain the final three-dimensional atmospheric tomography results.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the three-dimensional atmospheric tomography method for low-altitude airborne BeiDou observation as described in any one of claims 1 to 6.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the three-dimensional atmospheric tomography method for low-altitude airborne BeiDou observation as described in any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the three-dimensional atmospheric tomography method for low-altitude airborne BeiDou observation as described in any one of claims 1 to 6.

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