Method, device, storage medium and program product for multi-path signal snow thickness inversion
By calculating the inter-satellite single difference and the ionospheric and tropospheric delay corrections, the problem of inaccurate snow thickness determination in GNSS-R snow thickness inversion was solved, and real-time and accurate snow thickness measurement was achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- ZHEJIANG GEELY HLDG GRP CO LTD
- Filing Date
- 2026-02-27
- Publication Date
- 2026-05-29
AI Technical Summary
In existing technologies, the GNSS-R snow thickness inversion method cannot accurately and effectively determine snow thickness, and there are problems with improper signal-to-noise ratio multipath signal processing.
By acquiring carrier observations from multiple satellites, calculating inter-satellite single differences, eliminating common errors such as receiver clock errors, and using slant path ionospheric delay and tropospheric wet delay corrections, the multipath effect is clearly separated, and the snow thickness is calculated.
It enables non-contact, large-scale snow thickness measurement, and can obtain snow thickness information in real time and accurately, thus improving the accuracy and reliability of snow thickness inversion.
Smart Images
Figure CN122110174A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of satellite measurement technology, and in particular to a method, device, storage medium and program product for multipath signal snow thickness inversion. Background Technology
[0002] Snow depth, as an important climate parameter in the water cycle, can help formulate policies for agricultural development and water resource management. Snow depth is obtained using GNSS-R snow depth inversion technology.
[0003] Among related technologies, the GNSS-ReflectometrySnow Depth Retrieval (GNSS-R snow thickness inversion) method obtains the signal-to-noise ratio (SNR) signal through polynomial fitting, and then uses lomb spectrum analysis to extract the dominant frequency for snow thickness inversion. However, this method has the problem of not being able to accurately and effectively determine the snow thickness.
[0004] Based on this, a multipath signal snow thickness inversion method that can accurately obtain snow thickness is proposed. Summary of the Invention
[0005] This application provides a method, equipment, storage medium, and program product for multipath signal snow thickness inversion, in order to achieve timely and accurate acquisition of snow thickness.
[0006] In a first aspect, this application provides a multipath signal snow thickness inversion method, including:
[0007] Based on the carrier observations of multiple satellites received by the base station at the same time, the inter-satellite single difference between each target satellite and the reference satellite is obtained;
[0008] Based on the carrier observation value and the projection function of the tropospheric delay wet component corresponding to each target satellite, the slant path ionospheric delay and the zenith slant path tropospheric wet delay correction value corresponding to each target satellite are obtained.
[0009] Based on the ionospheric delay of the oblique path, the tropospheric wet delay correction value of the oblique path corresponding to each target satellite, and the inter-satellite single difference between each target satellite and the reference satellite, the inter-satellite single difference multipath between each target satellite and the reference satellite is obtained;
[0010] The snow thickness is calculated based on the dominant frequency of the inter-satellite single-difference multipath corresponding to the target satellite.
[0011] In one possible implementation, based on the carrier observation value and the projection function of the tropospheric delay wet component corresponding to each target satellite, the slant path ionospheric delay and zenith slant path tropospheric wet delay correction values corresponding to each target satellite are obtained, including:
[0012] Based on the Precise Point Positioning – Real-Time Kinematic service corrections (PPP-RTK service) corrections from multiple grids, the oblique path ionospheric delay residual, oblique path ionospheric delay, zenith tropospheric wet delay residual, and zenith tropospheric wet delay of each satellite are obtained.
[0013] Based on the carrier observations, slant path ionospheric delay residuals, and slant path ionospheric delay corresponding to each target satellite, the slant path ionospheric delay corresponding to each target satellite is obtained.
[0014] Based on the projection function of the tropospheric delay wet component, the zenith tropospheric wet delay residual, and the zenith tropospheric wet delay corresponding to each target satellite, the slant path tropospheric wet delay correction value corresponding to each target satellite is obtained.
[0015] In one possible implementation, based on PPP-RTK service corrections from multiple grids, the oblique path ionospheric delay residual, oblique path ionospheric delay, zenith tropospheric wet delay residual, and zenith tropospheric wet delay for each satellite are obtained, including:
[0016] Based on the PPP-RTK service corrections from multiple grids, the slant path ionospheric delay residual, zenith tropospheric wet delay residual, and vertex weights at the vertices of the target grid are obtained. Additionally, the slant path ionospheric delay, ionospheric correction type, zenith tropospheric wet delay, and tropospheric wet delay correction type of the target grid are obtained. The target grid is the grid where the base station is located among the multiple grids.
[0017] Based on the slant path ionospheric delay residuals at the vertices of the target grid, the slant path ionospheric delay residuals for each target satellite are determined.
[0018] Based on the ionospheric type, the coordinates of the center point of the target grid, and the coordinates of the base station, the slant path ionospheric delay of each target satellite is obtained.
[0019] The zenith tropospheric wet delay residual of each target satellite is obtained based on the zenith tropospheric wet delay residual of each target satellite at the vertex of the target grid and the vertex weight.
[0020] Based on the tropospheric wet delay correction type, the center point coordinates of the target grid, and the coordinates of the base station, the zenith tropospheric wet delay of each target satellite is obtained.
[0021] In one possible implementation, the oblique path ionospheric delay residual of each target satellite is determined based on the oblique path ionospheric delay residual at the vertices of the target grid, including:
[0022] When none of the vertices of the target grid have oblique path ionospheric delay residuals, the oblique path ionospheric delay residual of each target satellite is equal to 0.
[0023] When the vertices of the target grid have corresponding oblique path ionospheric delay residuals, the oblique path ionospheric delay residual of each target satellite is obtained based on the oblique path ionospheric delay residual of each target satellite in the target grid and the vertex weight.
[0024] One possible implementation involves calculating the snow thickness based on the dominant frequency of the inter-satellite single-difference multipath corresponding to the target satellite, including:
[0025] Lomb spectrum analysis was performed on the inter-satellite single-difference multipath corresponding to the target satellite to obtain the dominant frequency of the target satellite;
[0026] The snow thickness is obtained based on the height of the reflector surface during the snowless phase and the dominant frequency corresponding to the target satellite.
[0027] One possible implementation involves obtaining the inter-satellite single difference between each target satellite and a reference target satellite based on carrier observations received by the base station from multiple satellites at the same time, including:
[0028] Based on the carrier observation values of multiple satellites received by the base station at the same time, the pseudorange of the base station corresponding to each satellite is determined;
[0029] The inter-satellite single difference between each target satellite and the reference satellite is obtained by subtracting the pseudorange of the base station corresponding to each target satellite from the pseudorange of the base station corresponding to the reference satellite.
[0030] Secondly, this application provides a multipath signal snow thickness inversion device, comprising:
[0031] The processing module is used to obtain the inter-satellite single difference between each target satellite and the reference satellite based on the carrier observation values of multiple satellites received by the base station at the same time.
[0032] The inversion module is used to obtain the slant path ionospheric delay and zenith slant path tropospheric wet delay correction values for each target satellite based on the carrier observation values and the projection function of the tropospheric delay wet component. Based on the slant path ionospheric delay, slant path tropospheric wet delay correction values, and the inter-satellite single difference between each target satellite and the reference satellite, the inter-satellite single difference multipath between each target satellite and the reference satellite is obtained. The snow thickness is calculated based on the dominant frequency of the inter-satellite single difference multipath corresponding to the target satellite.
[0033] In one possible implementation, the inversion module is specifically used for:
[0034] Based on the PPP-RTK service corrections from multiple grids, the oblique path ionospheric delay residual, oblique path ionospheric delay, zenith tropospheric wet delay residual, and zenith tropospheric wet delay for each satellite are obtained.
[0035] Based on the carrier observations, slant path ionospheric delay residuals, and slant path ionospheric delay corresponding to each target satellite, the slant path ionospheric delay corresponding to each target satellite is obtained.
[0036] Based on the projection function of the tropospheric delay wet component, the zenith tropospheric wet delay residual, and the zenith tropospheric wet delay corresponding to each target satellite, the slant path tropospheric wet delay correction value corresponding to each target satellite is obtained.
[0037] In one possible implementation, the inversion module is further configured to:
[0038] Based on the PPP-RTK service corrections from multiple grids, the slant path ionospheric delay residual, zenith tropospheric wet delay residual, and vertex weights at the vertices of the target grid are obtained. Additionally, the slant path ionospheric delay, ionospheric correction type, zenith tropospheric wet delay, and tropospheric wet delay correction type of the target grid are obtained. The target grid is the grid where the base station is located among the multiple grids.
[0039] Based on the slant path ionospheric delay residuals at the vertices of the target grid, the slant path ionospheric delay residuals for each target satellite are determined.
[0040] Based on the ionospheric type, the coordinates of the center point of the target grid, and the coordinates of the base station, the slant path ionospheric delay of each target satellite is obtained.
[0041] The zenith tropospheric wet delay residual of each target satellite is obtained based on the zenith tropospheric wet delay residual of each target satellite at the vertex of the target grid and the vertex weight.
[0042] Based on the tropospheric wet delay correction type, the center point coordinates of the target grid, and the coordinates of the base station, the zenith tropospheric wet delay of each target satellite is obtained.
[0043] In one possible implementation, the inversion module is further configured to:
[0044] When none of the vertices of the target grid have oblique path ionospheric delay residuals, the oblique path ionospheric delay residual of each target satellite is equal to 0.
[0045] When the vertices of the target grid have corresponding oblique path ionospheric delay residuals, the oblique path ionospheric delay residual of each target satellite is obtained based on the oblique path ionospheric delay residual of each target satellite in the target grid and the vertex weight.
[0046] In one possible implementation, the inversion module is specifically used for:
[0047] Lomb spectrum analysis was performed on the inter-satellite single-difference multipath corresponding to the target satellite to obtain the dominant frequency of the target satellite;
[0048] The snow thickness is obtained based on the height of the reflector surface during the snowless phase and the dominant frequency corresponding to the target satellite.
[0049] In one possible implementation, the acquisition module is specifically used for:
[0050] Based on the carrier observation values of multiple satellites received by the base station at the same time, the pseudorange of the base station corresponding to each satellite is determined;
[0051] The inter-satellite single difference between each target satellite and the reference satellite is obtained by subtracting the pseudorange of the base station corresponding to each target satellite from the pseudorange of the base station corresponding to the reference satellite.
[0052] Thirdly, this application provides an electronic device, including: a memory and a processor;
[0053] The memory stores the instructions that the computer executes;
[0054] The processor executes computer execution instructions stored in memory, causing the processor to perform the first aspect and / or various possible implementations of the first aspect as described above.
[0055] Fourthly, this application provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the first aspect and / or various possible embodiments of the first aspect.
[0056] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the first aspect and / or various possible implementations of the first aspect.
[0057] The multipath signal snow thickness inversion method, equipment, storage medium, and program products provided in this application obtain the inter-satellite single difference between each target satellite and the reference satellite by using carrier observations received by the base station from multiple satellites at the same time. This eliminates common errors such as receiver clock bias, reduces the impact of error sources on snow thickness inversion results, and improves the accuracy and reliability of snow thickness determination. Based on the carrier observations corresponding to each target satellite and the projection function of the tropospheric delay wet component, the slant path ionospheric delay and zenith slant path tropospheric wet delay correction values for each target satellite are obtained. This effectively eliminates interference from the ionosphere and troposphere on the signal propagation of the target satellite, further improving the accuracy of snow thickness inversion. Based on the slant path ionospheric delay, slant path tropospheric wet delay correction values, and the inter-satellite single difference between each target satellite and the reference satellite, systematic errors from the ionosphere and troposphere can be subtracted from the inter-satellite single difference, thus clearly separating the signal changes caused by multipath effects and effectively obtaining the inter-satellite single difference multipath between each target satellite and the reference satellite. Based on the dominant frequency of the inter-satellite single-difference multipath corresponding to the target satellite, the snow thickness is calculated, which can realize non-contact, large-scale snow thickness measurement and obtain snow thickness information in real time and accurately. Attached Figure Description
[0058] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.
[0059] Figure 1 A schematic diagram illustrating a scenario for the multipath signal snow thickness inversion method provided in this application embodiment;
[0060] Figure 2 A flowchart illustrating the multipath signal snow thickness inversion method provided in this application embodiment. Figure 1 ;
[0061] Figure 3 A flowchart illustrating the multipath signal snow thickness inversion method provided in this application embodiment. Figure 2 ;
[0062] Figure 4 This is a schematic diagram of the structure of the multipath signal snow thickness inversion device provided in the embodiments of this application;
[0063] Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.
[0064] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation
[0065] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.
[0066] Related technique 1 involves removing the trend term of the direct signal from the SNR path signal using polynomial fitting to obtain the SNR multipath signal. However, when using Lomb spectrum analysis to extract the dominant frequency for snow thickness inversion, the trend term cannot be completely removed, thus affecting the accuracy of the snow thickness inversion. Related technique 2 involves using L4 combined observations of pseudorange or carrier waves for snow thickness inversion. Ionospheric errors are added to the multipath error, making it impossible to cleanly eliminate ionospheric errors and leading to problems in accurately and effectively determining snow thickness.
[0067] The multipath signal snow thickness inversion method provided in this application obtains the inter-satellite single difference between each target satellite and the reference satellite by using carrier observations from multiple satellites received by the base station at the same time. This eliminates common errors such as receiver clock bias, reduces the impact of error sources on the snow thickness inversion results, and improves the accuracy and reliability of snow thickness determination. Based on the carrier observations corresponding to each target satellite and the projection function of the tropospheric delay wet component, the slant path ionospheric delay and zenith slant path tropospheric wet delay correction values for each target satellite are obtained. This effectively eliminates interference from the ionosphere and troposphere on the signal propagation of the target satellite, further improving the accuracy of snow thickness inversion. Based on the slant path ionospheric delay, slant path tropospheric wet delay correction values, and the inter-satellite single difference between each target satellite and the reference satellite, systematic errors from the ionosphere and troposphere can be subtracted from the inter-satellite single difference, thereby clearly separating the signal changes caused by multipath effects and effectively obtaining the inter-satellite single difference multipath between each target satellite and the reference satellite. Based on the dominant frequency of the inter-satellite single-difference multipath corresponding to the target satellite, the snow thickness is calculated, which can realize non-contact, large-scale snow thickness measurement and obtain snow thickness information in real time and accurately.
[0068] Figure 1 This is a schematic diagram illustrating a scenario for the multipath signal snow thickness inversion method provided in an embodiment of this application. Figure 1 As shown, the specific application scenarios of this application include base station 11, multiple satellites 12, and snow accumulation 13, wherein:
[0069] Multiple satellites 12 transmit satellite signals containing time, frequency, and orbit information to ground station 11. When the satellite signals encounter the surface of snow 13 during propagation, they are reflected, creating multipath signals.
[0070] Base station 11 receives the direct signal transmitted by satellite 12 and the multipath signal reflected by snow 13 through its antenna, obtaining a mixed signal. Base station 11 filters, reduces noise, and performs preliminary separation on the received mixed signal, extracts the multipath signal, and performs snow thickness inversion based on the multipath signal to obtain the thickness information of snow 13.
[0071] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.
[0072] Figure 2 A flowchart illustrating the multipath signal snow thickness inversion method provided in this application embodiment. Figure 1 .like Figure 2 As shown, the method includes:
[0073] S201. Based on the carrier observation values of multiple satellites received by the base station at the same time, obtain the inter-satellite single difference between each target satellite and the reference satellite.
[0074] Inter-satellite single difference (ISD) is the difference between the carrier observations of a target satellite and a reference satellite, calculated by comparing carrier observations from different satellites at the same time. ISD can eliminate receiver-related common errors such as receiver clock bias, improving positioning accuracy. Carrier observations are the parameter values received by the receiver during the propagation of the carrier signal transmitted by the satellite. Carrier observations allow for the measurement of information such as the distance between the satellite and the receiver. Optionally, carrier observations may include carrier phase.
[0075] The base station receives carrier signals from multiple satellites simultaneously. The base station extracts the carrier observation value for each satellite from its carrier signal. Then, based on reference conditions, one satellite is selected as the reference satellite from among the multiple satellites. Based on target conditions, the target satellite is selected from among the multiple satellites. The carrier observation value of the target satellite is subtracted from the carrier observation value of the reference satellite to obtain the inter-satellite single difference between each target satellite and the reference satellite. By calculating the inter-satellite single difference, common errors such as receiver clock bias can be eliminated, reducing the impact of error sources on the snow thickness inversion results and improving the accuracy and reliability of snow thickness determination. Optionally, the number of multiple satellites is at least two. For example, multiple satellites may consist of two satellites.
[0076] A reference satellite is any satellite among multiple satellites that meets the reference criteria. The reference criteria are used to determine the reference satellite among multiple satellites.
[0077] Optionally, the number of target satellites can be multiple.
[0078] Optionally, the baseline condition is: any satellite whose elevation angle is between [5 degrees and 25 degrees].
[0079] Optionally, the target condition is: satellites that do not meet the baseline conditions. For example, if the baseline condition is any satellite with an elevation angle between [5 degrees and 25 degrees], the target condition can be expressed as: the satellite's elevation angle is less than 5 degrees, or the elevation angle is greater than 25 degrees.
[0080] Setting satellites with elevation angles within the range of [5 degrees, 25 degrees] as reference satellites allows for the identification of a relatively rich set of inter-satellite single-difference multipaths, which helps reveal the variation patterns of multipath effects under the influence of snow cover. Meanwhile, when the elevation angles of other satellites are within the range of [5 degrees, 25 degrees], the inter-satellite single-difference multipaths between these satellites and the reference satellite are approximately zero. By excluding satellites with elevation angles within the range of [5 degrees, 25 degrees] from the target satellites, computational load can be reduced and computational efficiency improved.
[0081] S202. Based on the carrier observation value and the projection function of the tropospheric delay wet component corresponding to each target satellite, obtain the slant path ionospheric delay and zenith slant path tropospheric wet delay correction value corresponding to each target satellite.
[0082] The tropospheric delay wet component projection function projects the moisture component of the tropospheric delay onto the target satellite signal propagation path according to a certain mathematical model. This allows for a more accurate calculation of the impact of tropospheric delay on the target satellite signal. The slant-path ionospheric delay represents the delay along the slant-path direction of the target satellite signal caused by changes in the signal propagation path due to factors such as uneven electron density in the ionosphere. The zenith slant-path tropospheric wet delay correction represents the correction value for the delay caused by water vapor in the troposphere in the vertical direction and along the slant-path direction of the target satellite signal; it is used to eliminate the influence of the tropospheric moisture component on the propagation of the target satellite signal.
[0083] Optionally, the Earth's surface can be divided into multiple small regions according to certain rules, each region being a grid. Multiple grids encompass multiple small regions. In satellite positioning and atmospheric correction, grids allow for independent processing and analysis of atmospheric parameters in different regions, improving accuracy. By analyzing the correlation between the tropospheric delay wet component projection function and carrier observation quality control of the grid through which the propagation between each target satellite and the base station occurs, the oblique path ionospheric delay and zenith oblique path tropospheric wet delay correction values for each target satellite can be obtained. Optionally, the tropospheric delay wet component projection function within each grid can be determined based on the PPP-RTK service corrections.
[0084] Based on the carrier observations and the projection function of the tropospheric delay wet component for each target satellite, specific mathematical models and algorithms are used to accurately calculate the slant path ionospheric delay and zenith slant path tropospheric wet delay corrections for each target satellite. These calculated slant path ionospheric delay and zenith slant path tropospheric wet delay corrections effectively eliminate interference from the ionosphere and troposphere on the signal propagation of the target satellite, improving the accuracy of snow thickness inversion.
[0085] S203. Based on the ionospheric delay of the oblique path, the tropospheric wet delay correction value of the oblique path corresponding to each target satellite, and the inter-satellite single difference between each target satellite and the reference satellite, the inter-satellite single difference multipath between each target satellite and the reference satellite is obtained.
[0086] Inter-satellite single-difference multipath refers to the error component related to multipath propagation caused by the multipath effect resulting from the target satellite signal encountering reflective objects during propagation. Inter-satellite single-difference multipath reflects the degree of influence of multipath effects on inter-satellite single-difference.
[0087] In snow thickness inversion, the snow surface acts as a reflector, and the physical properties of the snow alter the amplitude, phase, and polarization of the target satellite signal, thus affecting the multipath effect and resulting in the presence of snow physical characteristics in the inter-satellite single-difference multipath. By analyzing the inter-satellite single-difference multipath between each target satellite and the reference satellite, the physical characteristics of the snow can be inferred, thereby achieving snow thickness inversion.
[0088] By calculating the ionospheric delay and tropospheric wet delay corrections for oblique paths, systematic errors in the ionosphere and troposphere can be subtracted from the inter-satellite single-difference multipath, thus clearly separating the signal variations caused by multipath effects. This allows for the effective acquisition of inter-satellite single-difference multipath between each target satellite and the reference satellite. Consequently, the inter-satellite single-difference multipath can be accurately obtained, providing a deeper understanding of the multipath effects during target satellite signal propagation, leading to more accurate snow thickness inversion and precise determination of snow cover thickness.
[0089] For example, assume the inter-satellite single difference between target satellite j and reference satellite i. for:
[0090]
[0091] in, This represents the geometric distance from the base station to the target satellite i; This represents the geometric distance from the base station to the target satellite j; This represents the target satellite clock difference between the base station and target satellite i; This represents the target satellite clock difference between the base station and target satellite j; Indicates the slant path ionospheric delay of target satellite i; Indicates the slant path ionospheric delay of target satellite j; This represents the tropospheric wet delay correction value for the slant path of target satellite i; This represents the tropospheric wet delay correction value for the slant path of target satellite j; This indicates the inter-satellite single-difference multipath for target satellite i; This indicates the inter-satellite single-difference multipath for target satellite j; This represents pseudorange noise.
[0092] Furthermore, using the carrier observations of each target satellite and the pseudorange equation, the geometric distance between the base station and each target satellite can be calculated. Using the carrier observations of each target satellite, the clock drift and clock bias of each target satellite can be obtained. By performing polynomial fitting on the clock drift and clock bias of each target satellite, the target satellite clock bias between the base station and each target satellite can be calculated. That is, the geometric distance from the base station to target satellite i. Geometric distance from the base station to the target satellite j Target satellite clock bias between base station and target satellite i Target satellite clock difference between base station and target satellite j This can be calculated based on carrier observations of target satellite i or target satellite j. Based on the above, the inter-satellite single difference between target satellite j and reference satellite i is... After simplification, the inter-satellite single difference is obtained. It can be represented as:
[0093]
[0094] Subsequently, based on the slant path ionospheric delay corresponding to target satellite j , oblique path tropospheric wet delay correction , Slant path ionospheric delay corresponding to reference satellite i and oblique path tropospheric wet delay correction The inter-satellite single-difference multipath between each target satellite j and the reference satellite i is obtained. for:
[0095]
[0096] Furthermore, assume that the reflector height of target satellite j is... The reflector height of reference satellite i is Then, the inter-satellite single-difference multipath between each target satellite j and the reference satellite i can be calculated. Represented as:
[0097]
[0098] in, This indicates the inter-satellite single-difference multipath between target satellite j and reference satellite i; The geometric portion representing the phase delay of the reflected signal of target satellite i relative to the direct signal. , The wavelength representing the carrier observation, Indicates the elevation angle of target satellite i; The geometric part representing the phase delay of the reflected signal of target satellite j relative to the direct signal. , The wavelength representing the carrier observation, Indicates the elevation angle of target satellite i; This represents the path delay of the reflected signal from target satellite i. ; This represents the path delay of the reflected signal from target satellite j. .
[0099] S204. The snow thickness is calculated based on the dominant frequency of the inter-satellite single-difference multipath corresponding to the target satellite.
[0100] The dominant frequency is the frequency component with the most concentrated energy in inter-satellite single-difference multipath signals. It reflects the main characteristic frequency of the multipath effect and is closely related to the impact of snow thickness on satellite signals. Snow thickness is the vertical height of snow accumulation on the ground.
[0101] First, spectral analysis is performed on the inter-satellite single-difference multipath signal corresponding to the target satellite to identify the dominant frequency. Based on the dominant frequency, the snow thickness is obtained using the snow thickness determination formula. By using the dominant frequency of the inter-satellite single-difference multipath signal to calculate snow thickness, non-contact, large-area snow thickness measurement can be achieved, enabling real-time and accurate acquisition of snow thickness information.
[0102] The multipath signal snow thickness inversion method provided in this application obtains the inter-satellite single difference between each target satellite and the reference satellite by using carrier observations from multiple satellites received by the base station at the same time. This eliminates common errors such as receiver clock bias, reduces the impact of error sources on the snow thickness inversion results, and improves the accuracy and reliability of snow thickness determination. Based on the carrier observations corresponding to each target satellite and the projection function of the tropospheric delay wet component, the slant path ionospheric delay and zenith slant path tropospheric wet delay correction values for each target satellite are obtained. This effectively eliminates interference from the ionosphere and troposphere on the signal propagation of the target satellite, further improving the accuracy of snow thickness inversion. Based on the slant path ionospheric delay, slant path tropospheric wet delay correction values, and the inter-satellite single difference between each target satellite and the reference satellite, systematic errors from the ionosphere and troposphere can be subtracted from the inter-satellite single difference, thereby clearly separating the signal changes caused by multipath effects and effectively obtaining the inter-satellite single difference multipath between each target satellite and the reference satellite. Based on the dominant frequency of the inter-satellite single-difference multipath corresponding to the target satellite, the snow thickness is calculated, which can realize non-contact, large-scale snow thickness measurement and obtain snow thickness information in real time and accurately.
[0103] Figure 3 A flowchart illustrating the multipath signal snow thickness inversion method provided in this application embodiment. Figure 2 .like Figure 3 As shown, in this embodiment... Figure 2 Based on the examples, the multipath signal snow thickness inversion method is described in detail, which includes:
[0104] In one possible implementation, step S201 may further include:
[0105] S2011. Based on the carrier observation values of multiple satellites received by the base station at the same time, determine the pseudorange of the base station corresponding to each satellite.
[0106] Base station pseudorange is the distance between a target satellite and a base station obtained by measuring the propagation time of a satellite signal from transmission to reception at the base station and multiplying it by the speed of light.
[0107] The base station simultaneously receives carrier wave observations transmitted by multiple satellites, records the signal reception time using its internal clock, and simultaneously acquires the transmission time of each satellite's signal. The difference between the reception and transmission times of each satellite is multiplied by the speed of light to obtain the pseudorange between each satellite and the base station.
[0108] Optionally, the distance value can be corrected by taking into account satellite clock bias, receiver clock bias, ionospheric delay, tropospheric delay, etc., and the corrected distance value can be used as the base station pseudorange between each satellite and the base station.
[0109] For example, taking the first frequency point of satellite i as an example, based on the carrier observation values of multiple satellites received by the base station at the same time, the pseudorange of the base station corresponding to satellite i is determined as follows:
[0110]
[0111] in, This represents the geometric distance from the base station to the target satellite i; This represents the target satellite clock difference between the base station and target satellite i; Indicates the slant path ionospheric delay of target satellite i; This represents the tropospheric wet delay correction value for the slant path of target satellite i; This indicates the inter-satellite single-difference multipath for target satellite i; This represents the pseudorange noise of satellite i.
[0112] S2012. The difference between the pseudorange of the base station corresponding to each target satellite and the pseudorange of the base station corresponding to the reference satellite is obtained to obtain the inter-satellite single difference between each target satellite and the reference satellite.
[0113] Based on the pseudorange of the base station corresponding to each satellite, the pseudorange of the base station corresponding to the target satellite and the pseudorange of the base station corresponding to the reference satellite are obtained. Then, the difference between the pseudorange of the base station corresponding to each target satellite and the pseudorange of the base station corresponding to the reference satellite is calculated to obtain the inter-satellite single difference between each target satellite and the reference satellite. This inter-satellite single difference effectively eliminates the influence of common errors related to the propagation path, such as receiver clock error, ionospheric delay, and tropospheric delay, improving the accuracy and reliability of positioning and making the positioning results more stable.
[0114] For example, if the pseudorange of the base station corresponding to satellite i for: The pseudorange of the base station corresponding to satellite j for: .in, This represents the geometric distance from the base station to the target satellite i; This represents the geometric distance from the base station to the target satellite j; This represents the target satellite clock difference between the base station and target satellite i; This represents the target satellite clock difference between the base station and target satellite j; Indicates the slant path ionospheric delay of target satellite i; Indicates the slant path ionospheric delay of target satellite j; This represents the tropospheric wet delay correction value for the slant path of target satellite i; This represents the tropospheric wet delay correction value for the slant path of target satellite j; This indicates the inter-satellite single-difference multipath for target satellite i; This indicates the inter-satellite single-difference multipath for target satellite j; This represents the pseudorange noise of satellite i; This represents the pseudorange noise of satellite j.
[0115] Since the hardware delay of pseudorange is stable over a long period, it does not affect the extraction of the main frequency of the multipath signal. Therefore, the hardware delay of pseudorange is ignored to simplify the analysis. The pseudorange of the base station corresponding to satellite i at the same time is... The pseudorange of the base station corresponding to satellite j By subtracting the differences, we can obtain the inter-satellite single difference as follows:
[0116]
[0117] In one possible implementation, step S202 may further include:
[0118] S2021. Based on the PPP-RTK service corrections from multiple grids, the oblique path ionospheric delay residual, oblique path ionospheric delay, zenith tropospheric wet delay residual, and zenith tropospheric wet delay of each satellite are obtained.
[0119] PPP-RKT service corrections combine Precise Point Positioning (PPP) with Real-Time Kinematic Service Corrections (RTK). Each grid cell has its own PPP-RKT service corrections. By acquiring PPP-RKT service corrections from multiple grid cells, a series of parameter values can be provided to correct errors in the propagation of target satellite signals. Using PPP-RKT service corrections from multiple grid cells can improve positioning accuracy.
[0120] The oblique path ionospheric delay residual represents the portion of the delay error that occurs when a satellite signal propagates along an oblique path in the ionosphere. It reflects the remaining effect of ionospheric delay after initial correction. The oblique path ionospheric delay directly reflects the delay effect experienced by a satellite signal as it passes through the ionosphere along an oblique path.
[0121] Zenith tropospheric wet delay residuals represent the portion of the delay caused by water vapor in the troposphere that remains in the vertical direction after PPP-RKT service correction. Zenith tropospheric wet delay directly reflects the delay impact experienced by satellite signals as they travel along a vertical path through the troposphere.
[0122] Based on the PPP-RTK service corrections from multiple grids, the oblique path ionospheric delay residual, oblique path ionospheric delay, zenith tropospheric wet delay residual, and zenith tropospheric wet delay for each satellite are determined. This allows for a more detailed consideration of the differences in atmospheric characteristics between different grids, thereby improving the accuracy of corrections for ionospheric and tropospheric delay errors.
[0123] S2022. Based on the carrier observation value, slant path ionospheric delay residual and slant path ionospheric delay corresponding to each target satellite, the slant path ionospheric delay corresponding to each target satellite is obtained.
[0124] Based on the carrier observations, slant path ionospheric delay residuals, and slant path ionospheric delay corresponding to each target satellite, the formula for determining the slant path ionospheric delay of the satellite is solved, and the slant path ionospheric delay corresponding to each target satellite is obtained. This can effectively eliminate the main influence of the ionosphere on the satellite signal propagation of the target satellite and improve the accuracy of satellite positioning and related measurements.
[0125] For example, the formula for determining the ionospheric delay of the target satellite's oblique path can be expressed as:
[0126]
[0127] in, The slant path ionospheric delay corresponding to target location i is represented in meters. This represents the carrier observation value corresponding to target satellite i, in Hertz; The slant path ionospheric delay residual of satellite i in grid k is expressed in units of total electron content. This represents the slant path ionospheric delay of satellite i in grid k, expressed in units of total electron content. Optionally, grid k can be any grid containing the base station.
[0128] S2023. Based on the projection function of the tropospheric delay wet component, the zenith tropospheric wet delay residual, and the zenith tropospheric wet delay corresponding to each target satellite, the slant path tropospheric wet delay correction value corresponding to each target satellite is obtained.
[0129] Based on the projection function of the tropospheric delay wet component, the zenith tropospheric wet delay residual, and the zenith tropospheric wet delay corresponding to each target satellite, the formula for determining the tropospheric wet delay correction value of the oblique path is solved, and the oblique path tropospheric wet delay correction value corresponding to each target satellite is obtained. This can effectively eliminate the interference of the tropospheric wet component on the satellite signal propagation and further improve the inversion accuracy of snow cover thickness.
[0130] Optionally, the formula for determining the tropospheric wet delay correction value for the satellite's oblique path can be expressed as:
[0131]
[0132] in, This represents the tropospheric wet delay correction value for the oblique path corresponding to target satellite i; This represents the projection function of the tropospheric delayed wet component corresponding to target satellite i. Indicates the elevation angle of target satellite i; This represents the zenith tropospheric wet delay residual of target satellite i, in meters; The zenith tropospheric wet delay of target satellite i is expressed in meters.
[0133] In one possible implementation, step S204 may further include:
[0134] S2041. Perform Lomb spectrum analysis on the inter-satellite single-difference multipath corresponding to the target satellite to obtain the dominant frequency corresponding to the target satellite.
[0135] Lomb spectrum analysis can accurately estimate the frequency components of inter-satellite single-difference multipaths and identify the dominant frequency in the inter-satellite single-difference multipath.
[0136] By employing Lomb spectral analysis on the inter-satellite single-difference multipath for the target satellite, the signal power spectral density at different frequencies within the inter-satellite single-difference multipath is obtained. The frequency points of this power spectral density are then used as the dominant frequency for the target satellite. Obtaining the dominant frequency of the target satellite through Lomb spectral analysis provides a deeper understanding of the frequency characteristics of the inter-satellite single-difference multipath signal. The dominant frequency also helps identify the main reflector features in the signal propagation environment. In snow thickness inversion, the dominant frequency can aid in calculating snow cover thickness.
[0137] S2042. The snow thickness is obtained based on the height of the reflector surface during the snowless phase and the main frequency corresponding to the target satellite.
[0138] The reflector height during the snowless phase refers to the height of the satellite signal reflector surface when there is no snow cover. Specifically, when there is no snow on the ground, the ground itself acts as the signal reflector surface. For example, the vertical distance from the geoid to the ground surface is the reflector height during the snowless phase.
[0139] Based on the reflector height during the snowless phase and the corresponding dominant frequency of the target satellite, the snow thickness is determined using a formula. This enables non-contact, real-time, and large-scale snow thickness inversion, avoiding the limitations of traditional artificial snow thickness methods. It allows for timely and accurate acquisition of snow thickness information, which helps improve the ability to respond to snow-related disasters.
[0140] For example, the formula for determining snow thickness can be expressed as:
[0141]
[0142] in, Indicates the thickness of the snow cover; Indicates the height of the reflective surface during the snowless phase; The wavelength representing the carrier observation; Indicates the clock speed.
[0143] In one possible implementation, step S2021 may further include:
[0144] Step A: Based on the PPP-RTK service corrections from multiple grids, obtain the slant path ionospheric delay residual, zenith tropospheric wet delay residual, and vertex weights at the vertices of the target grid. Also, obtain the slant path ionospheric delay, ionospheric correction type, zenith tropospheric wet delay, and tropospheric wet delay correction type of the target grid. The target grid is the grid where the base station is located among multiple grids.
[0145] Based on the coordinates of the base station and the coordinates of each corresponding grid cell, the grid cell containing the base station is determined. This grid cell is then designated as the target grid cell. Next, based on the coordinates of the base station and the vertex coordinates of the target grid cell, the vertex weights of each vertex within the target grid cell are determined using the inverse distance method. Optionally, the target grid cell can have 1-4 vertices.
[0146] In the PPP-RTK service correction data of multiple grids, the oblique path ionospheric delay residual and the zenith tropospheric wet delay residual at each vertex of the target grid are found. In the PPP-RTK service correction data of multiple grids, the oblique path ionospheric delay, ionospheric correction type, zenith tropospheric wet delay, and tropospheric wet delay correction type of the target grid are found.
[0147] Step B: Determine the oblique path ionospheric delay residual for each target satellite based on the oblique path ionospheric delay residual at the vertices of the target grid.
[0148] By analyzing the slant path ionospheric delay residuals at the vertices of the target grid where the base station sits, the slant path ionospheric delay residuals of each target satellite are determined.
[0149] Specifically, when there is no oblique path ionospheric delay residual at any vertex of the target grid, the oblique path ionospheric delay residual of each target satellite is equal to 0; when there is a corresponding oblique path ionospheric delay residual at each vertex of the target grid, the oblique path ionospheric delay residual of each target satellite is obtained based on the oblique path ionospheric delay residual of each target satellite in the target grid and the vertex weight.
[0150] If there is no oblique path ionospheric delay residual data at any vertex of the target grid, and no available measurement data is available to calculate the oblique path ionospheric delay residual of the satellite, for the purpose of simplifying the processing, the oblique path ionospheric delay residual of each target satellite is directly determined to be equal to 0.
[0151] When the vertices of the target grid have corresponding oblique path ionospheric delay residuals, the oblique path ionospheric delay residuals of each target satellite within the target grid and the vertex weights are used to obtain the oblique path ionospheric delay residuals of each target satellite through the satellite oblique path ionospheric delay residual determination formula. This can more accurately reflect the oblique path ionospheric delay of the target satellite in the entire target grid area, reduce measurement errors caused by local ionospheric environment differences, and improve the accuracy of oblique path ionospheric delay residual calculation.
[0152] Meanwhile, the differences in ionospheric delay residuals with and without oblique paths at the target grid vertices are processed separately, which can adapt to various data conditions and improve the stability and reliability of the entire snow thickness inversion under different environments.
[0153] Optionally, the formula for determining the satellite slant path ionospheric delay residual can be expressed as:
[0154]
[0155] in, This represents the slant path ionospheric delay residual of target satellite i at vertex k of the target grid; This represents the vertex weight corresponding to vertex k in the target grid. The slant path ionospheric delay residual of target satellite i is represented; N represents the number of vertices of the target grid, with a value of 1-4.
[0156] Step C: Based on the ionospheric type, the center point coordinates of the target grid, and the coordinates of the base station, the slant path ionospheric delay of each target satellite is obtained.
[0157] Based on the ionospheric type, a suitable formula for determining the oblique path ionospheric delay is selected as the target oblique path ionospheric delay determination formula. Then, the coordinates of the target grid center point and the base station coordinates are substituted into the target oblique path ionospheric delay determination formula and solved to obtain the oblique path ionospheric delay for each target satellite.
[0158] Optionally, the coordinates of the center point of the target grid are: The coordinates of the base station are The ionospheric delay of the oblique path of target satellite i can be expressed as:
[0159]
[0160] in, This represents the formula for determining the ionospheric delay along the target's oblique path; ; .
[0161] Furthermore, when the ionosphere type is changed to type I, the formula for determining the target slant path ionospheric delay is: When the ionosphere is reclassified to type II, the formula for determining the target slant path ionospheric delay is: When the ionosphere is reclassified as Type III, the formula for determining the target slant path ionospheric delay is as follows: .in, , and These represent the delay parameters.
[0162] Step D: Based on the zenith tropospheric wet delay residual of each target satellite at the vertices of the target grid and the vertex weights, obtain the zenith tropospheric wet delay residual of each target satellite.
[0163] Based on the zenith tropospheric wet delay residual of each target satellite at the vertices of the target grid and the vertex weights, the zenith tropospheric wet delay residual of each target satellite is calculated using the zenith tropospheric wet delay residual determination formula.
[0164] Optionally, the formula for determining the wetted delay residual in the zenith troposphere can be expressed as:
[0165]
[0166] in, This represents the zenith tropospheric wet delay residual of target satellite i at vertex k of the target grid; This represents the vertex weight corresponding to vertex k in the target grid. N represents the zenith tropospheric wet delay residual of target satellite i; N represents the number of vertices of the target grid, with a value of 1-4.
[0167] Step E: Based on the tropospheric wet delay correction type, the center point coordinates of the target grid, and the coordinates of the base station, obtain the zenith tropospheric wet delay of each target satellite.
[0168] Based on the type of tropospheric wet delay correction, a suitable zenith tropospheric wet delay determination formula is selected as the target zenith tropospheric wet delay determination formula. Then, the coordinates of the target grid center point and the base station coordinates are substituted into the target zenith tropospheric wet delay determination formula and solved to obtain the target zenith tropospheric wet delay for each target satellite.
[0169] Optionally, the coordinates of the center point of the target grid are: The coordinates of the base station are The zenith tropospheric wet delay of target satellite i can be expressed as:
[0170]
[0171] in, This represents the formula for determining the wetted delay in the troposphere at the zenith of a target. ; .
[0172] Furthermore, when the ionosphere is reclassified to Type I, the formula for determining the wet delay of the target zenith troposphere is as follows: When the ionosphere is reclassified to type II, the formula for determining the wet delay of the target zenith troposphere is as follows: When the ionosphere is reclassified as Type III, the formula for determining the wetted delay of the target zenith troposphere is as follows: .in, , and These represent the delay parameters.
[0173] Figure 4 This is a schematic diagram of the structure of the multipath signal snow thickness inversion device provided in an embodiment of this application. Figure 4 As shown, the multipath signal snow thickness inversion device 40 provided in this embodiment includes:
[0174] The processing module 401 is used to obtain the inter-satellite single difference between each target satellite and the reference satellite based on the carrier observation values of multiple satellites received by the base station at the same time.
[0175] The inversion module 402 is used to obtain the slant path ionospheric delay and zenith slant path tropospheric wet delay correction values for each target satellite based on the carrier observation value and the projection function of the tropospheric delay wet component for each target satellite; to obtain the inter-satellite single-difference multipath between each target satellite and the reference satellite based on the slant path ionospheric delay, the slant path tropospheric wet delay correction value, and the inter-satellite single difference between each target satellite and the reference satellite; and to calculate the snow thickness based on the dominant frequency of the inter-satellite single-difference multipath corresponding to the target satellite.
[0176] In one possible implementation, the inversion module 402 is specifically used for:
[0177] Based on the PPP-RTK service corrections from multiple grids, the oblique path ionospheric delay residual, oblique path ionospheric delay, zenith tropospheric wet delay residual, and zenith tropospheric wet delay for each satellite are obtained.
[0178] Based on the carrier observations, slant path ionospheric delay residuals, and slant path ionospheric delay corresponding to each target satellite, the slant path ionospheric delay corresponding to each target satellite is obtained.
[0179] Based on the projection function of the tropospheric delay wet component, the zenith tropospheric wet delay residual, and the zenith tropospheric wet delay corresponding to each target satellite, the slant path tropospheric wet delay correction value corresponding to each target satellite is obtained.
[0180] In one possible implementation, the inversion module 402 is further configured to:
[0181] Based on the PPP-RTK service corrections from multiple grids, the slant path ionospheric delay residual, zenith tropospheric wet delay residual, and vertex weights at the vertices of the target grid are obtained. Additionally, the slant path ionospheric delay, ionospheric correction type, zenith tropospheric wet delay, and tropospheric wet delay correction type of the target grid are obtained. The target grid is the grid where the base station is located among the multiple grids.
[0182] Based on the slant path ionospheric delay residuals at the vertices of the target grid, the slant path ionospheric delay residuals for each target satellite are determined.
[0183] Based on the ionospheric type, the coordinates of the center point of the target grid, and the coordinates of the base station, the slant path ionospheric delay of each target satellite is obtained.
[0184] The zenith tropospheric wet delay residual of each target satellite is obtained based on the zenith tropospheric wet delay residual of each target satellite at the vertex of the target grid and the vertex weight.
[0185] Based on the tropospheric wet delay correction type, the center point coordinates of the target grid, and the coordinates of the base station, the zenith tropospheric wet delay of each target satellite is obtained.
[0186] In one possible implementation, the inversion module 402 is further configured to:
[0187] When none of the vertices of the target grid have oblique path ionospheric delay residuals, the oblique path ionospheric delay residual of each target satellite is equal to 0.
[0188] When the vertices of the target grid have corresponding oblique path ionospheric delay residuals, the oblique path ionospheric delay residual of each target satellite is obtained based on the oblique path ionospheric delay residual of each target satellite in the target grid and the vertex weight.
[0189] In one possible implementation, the inversion module 402 is specifically used for:
[0190] Lomb spectrum analysis was performed on the inter-satellite single-difference multipath corresponding to the target satellite to obtain the dominant frequency of the target satellite;
[0191] The snow thickness is obtained based on the height of the reflector surface during the snowless phase and the dominant frequency corresponding to the target satellite.
[0192] In one possible implementation, the acquisition module 401 is specifically used for:
[0193] Based on the carrier observation values of multiple satellites received by the base station at the same time, the pseudorange of the base station corresponding to each satellite is determined;
[0194] The inter-satellite single difference between each target satellite and the reference satellite is obtained by subtracting the pseudorange of the base station corresponding to each target satellite from the pseudorange of the base station corresponding to the reference satellite.
[0195] The multipath signal snow thickness inversion device provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail here.
[0196] Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 5 As shown, the electronic device 50 provided in this embodiment includes at least one processor 501 and a memory 502. Optionally, the device 50 further includes a communication component 503. The processor 501, memory 502, and communication component 503 are connected via a bus 504.
[0197] In a specific implementation, at least one processor 501 executes computer execution instructions stored in memory 502, causing at least one processor 501 to perform the above-described method.
[0198] The specific implementation process of processor 501 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.
[0199] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.
[0200] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.
[0201] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings of this application's embodiments are not limited to only one bus or one type of bus.
[0202] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.
[0203] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed, implement any of the methods described above.
[0204] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random-Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.
[0205] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an application-specific integrated circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.
[0206] The division of units is merely a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.
[0207] 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 units can be selected to achieve the purpose of this embodiment according to actual needs.
[0208] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.
[0209] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, 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 of the various embodiments of this 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.
[0210] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.
[0211] Finally, it should be noted that other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.
Claims
1. A method for inverting snow thickness in multipath signals, characterized in that, include: Based on the carrier observations of multiple satellites received by the base station at the same time, the inter-satellite single difference between each target satellite and the reference satellite is obtained; Based on the carrier observation value and the tropospheric delay wet component projection function corresponding to each target satellite, the slant path ionospheric delay and zenith slant path tropospheric wet delay correction values corresponding to each target satellite are obtained. Based on the ionospheric delay of the oblique path, the tropospheric wet delay correction value of the oblique path corresponding to each target satellite, and the inter-satellite single difference between each target satellite and the reference satellite, the inter-satellite single difference multipath between each target satellite and the reference satellite is obtained; The snow thickness is calculated based on the dominant frequency of the inter-satellite single-difference multipath corresponding to the target satellite.
2. The method according to claim 1, characterized in that, The step of obtaining the oblique path ionospheric delay and zenith oblique path tropospheric wet delay correction values for each target satellite based on the carrier observation value and the tropospheric delay wet component projection function corresponding to each target satellite includes: Based on the precise single-point positioning-real-time dynamic PPP-RTK service corrections from multiple grids, the oblique path ionospheric delay residual, oblique path ionospheric delay, zenith tropospheric wet delay residual, and zenith tropospheric wet delay of each satellite are obtained. Based on the carrier observation value corresponding to each target satellite, the slant path ionospheric delay residual, and the slant path ionospheric delay, the slant path ionospheric delay corresponding to each target satellite is obtained; Based on the tropospheric delay wet component projection function, the zenith tropospheric wet delay residual, and the zenith tropospheric wet delay corresponding to each target satellite, the oblique path tropospheric wet delay correction value corresponding to each target satellite is obtained.
3. The method according to claim 2, characterized in that, The PPP-RTK service corrections based on multiple grids yield the oblique path ionospheric delay residual, oblique path ionospheric delay, zenith tropospheric wet delay residual, and zenith tropospheric wet delay for each satellite, including: Based on the PPP-RTK service corrections from multiple grids, the slant path ionospheric delay residual, zenith tropospheric wet delay residual, and vertex weights at the vertices of the target grid are obtained. Additionally, the slant path ionospheric delay, ionospheric correction type, zenith tropospheric wet delay, and tropospheric wet delay correction type of the target grid are obtained. The target grid is the grid where the base station is located among the multiple grids. Based on the slant path ionospheric delay residuals at the vertices of the target grid, the slant path ionospheric delay residuals for each target satellite are determined. Based on the ionospheric type, the coordinates of the center point of the target grid, and the coordinates of the base station, the slant path ionospheric delay of each target satellite is obtained. Based on the zenith tropospheric wet delay residual and vertex weight of each target satellite at the vertex of the target grid, the zenith tropospheric wet delay residual of each target satellite is obtained; Based on the tropospheric wet delay correction type, the center point coordinates of the target grid, and the coordinates of the base station, the zenith tropospheric wet delay of each target satellite is obtained.
4. The method according to claim 3, characterized in that, The determination of the oblique path ionospheric delay residual for each target satellite, based on the oblique path ionospheric delay residual at the vertices of the target grid, includes: When none of the vertices of the target grid have the oblique path ionospheric delay residual, the oblique path ionospheric delay residual of each target satellite is equal to 0; When the vertices of the target grid have corresponding oblique path ionospheric delay residuals, the oblique path ionospheric delay residual of each target satellite is obtained based on the oblique path ionospheric delay residual of each target satellite in the target grid and the vertex weight.
5. The method according to any one of claims 1-4, characterized in that, The calculation of snow thickness based on the dominant frequency of the inter-satellite single-difference multipath corresponding to the target satellite includes: Lomb spectrum analysis is performed on the inter-satellite single-difference multipath corresponding to the target satellite to obtain the dominant frequency corresponding to the target satellite; The snow thickness is obtained based on the height of the reflector surface during the snowless phase and the dominant frequency corresponding to the target satellite.
6. The method according to any one of claims 1-4, characterized in that, The step of obtaining the inter-satellite single difference between each target satellite and the reference target satellite based on the carrier observation values of multiple satellites received by the base station at the same time includes: Based on the carrier observation values of multiple satellites received by the base station at the same time, the pseudorange of the base station corresponding to each satellite is determined; The inter-satellite single difference between each target satellite and the reference satellite is obtained by subtracting the pseudorange of the base station corresponding to each target satellite from the pseudorange of the base station corresponding to the reference satellite.
7. A multipath signal snow thickness inversion device, characterized in that, include: The processing module is used to obtain the inter-satellite single difference between each target satellite and the reference satellite based on the carrier observation values of multiple satellites received by the base station at the same time. The inversion module is used to obtain the slant path ionospheric delay and zenith slant path tropospheric wet delay correction values for each target satellite based on the carrier observation value and the tropospheric delay wet component projection function corresponding to each target satellite. Based on the ionospheric delay of the oblique path corresponding to each target satellite, the tropospheric wet delay correction value of the oblique path, and the inter-satellite single difference between each target satellite and the reference satellite, the inter-satellite single difference multipath between each target satellite and the reference satellite is obtained; the snow thickness is calculated according to the dominant frequency of the inter-satellite single difference multipath corresponding to the target satellite.
8. An electronic device, characterized in that, include: Memory, processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory, causing the processor to perform the method as described in any one of claims 1-6.
9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed, are used to implement the method as described in any one of claims 1-6.
10. A computer program product, characterized in that, Includes a computer program, which, when executed, implements the method according to any one of claims 1-6.