A precise single-point positioning method, system, device, and medium for marine scenarios.

Abstract

This invention provides a precise point positioning method, system, device, and medium in marine scenarios, belonging to the field of satellite navigation and positioning. It includes acquiring IGS-ZTD and GGOS-ZTD data from different land stations, calculating the time-series daily average deviation data and inputting it into a fitting model. After fitting, spherical harmonic expansion is performed to obtain a spherical harmonic function deviation model for the land area. The latitude and longitude of the marine stations are acquired, and the spherical harmonic function deviation model is used for fitting calculations. A dynamic order selection mechanism at a monthly scale is introduced to obtain the monthly correction deviation value of the marine stations. Fitted ZTD data is obtained based on the correction deviation value. This invention overcomes the regional limitations of GNSS stations by constructing a collaborative correction framework to improve the positioning accuracy of GGOS-ZTD data in marine areas. The fitted ZTD data is applied to precise point positioning in marine scenarios to achieve high-precision positioning of measurement points in marine areas.

Description

Technical Field

[0001] This invention belongs to the field of satellite navigation and positioning, specifically relating to a precise single-point positioning method, system, device, and medium in marine scenarios. Background Technology

[0002] Tropospheric delay refers to the phenomenon where electromagnetic waves are refracted and their paths bend due to atmospheric effects as GNSS signals pass through the troposphere. The projection of this signal onto the zenith of the station is called the Zenith Tropospheric Delay (ZTD). ZTD exhibits strong spatial inhomogeneity and temporal variability, and is one of the main sources of error in GNSS positioning applications.

[0003] Spherical harmonic functions, as orthogonal basis functions defined in a spherical coordinate system, construct a spatially continuous field through multi-order mathematical expansions, providing a core mathematical tool for modeling surface atmospheric parameters. Their spatial decomposition characteristics are reflected in the following: the spherical harmonic function system constructed based on Legendre polynomials possesses complete orthogonality; by adjusting the order parameter, multi-scale representation of the spatial distribution of ZTDs can be achieved—lower-order components characterize large-scale geographical trends, while higher-order components finely analyze local features. This multi-resolution fusion characteristic enables the reconstruction of discretely distributed ZTD observations from land and sea stations into continuous spatial functions. While maintaining the continuous smoothness of the global field, adaptive expression of spatial resolution is achieved through order adjustment, laying the mathematical foundation for establishing a globally unified spatial tropospheric delay correction model.

[0004] In traditional ZTD modeling techniques, the defects of spherical harmonic function models restrict the improvement of positioning accuracy in marine scenes. Existing spherical harmonic function models usually adopt a globally fixed order expansion strategy, which results in insufficient model adaptability in areas with drastic water vapor changes. Furthermore, real-time dynamic modeling is highly dependent on computing resources, making it difficult to meet the timeliness requirements of marine positioning, leading to large ZTD errors in marine scenes. Summary of the Invention

[0005] To address the shortcomings of existing spherical harmonic function models in ZTD estimation for marine scenarios, this invention provides a precise single-point positioning method, system, device, and medium for marine scenarios.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] A precise single-point positioning method in marine scenarios includes the following steps:

[0008] Acquire IGS-ZTD and GGOS-ZTD data from different land stations; calculate the time-series daily average deviation data using the IGS-ZTD and GGOS-ZTD data; wherein, the IGS-ZTD data is the total zenith delay data measured on GNSS stations by the International Global Navigation Satellite Service; and the GGOS-ZTD data is the zenith tropospheric delay grid data provided by the Global Geodetic Observation System.

[0009] The time series daily average deviation data is input into the station data fitting model for fitting. Multiple deviation change values ​​in the station data fitting model are expanded using spherical harmonic functions to obtain the spherical harmonic function deviation model of the land area.

[0010] The latitude and longitude of the marine station are obtained; the latitude and longitude of the marine station are input into the spherical harmonic function deviation model of the land area for fitting calculation, and a dynamic order screening mechanism at the monthly scale is introduced to obtain the monthly correction deviation value of the marine station; the monthly correction deviation value is superimposed on the ZTD data of the marine station to obtain the fitted ZTD data; the fitted ZTD data is applied to Precise Point Positioning (PPP) to obtain the positioning accuracy and convergence time, thereby realizing the positioning of the measuring point in the marine area.

[0011] Preferably, the station data fitting model is as follows:

[0012] ;

[0013] in, This represents the daily average deviation value of the i-th time series data at the current station; multiple deviation change values ​​specifically include... , , , , ; , , These are the annual average deviation, the annual periodic variation amplitude, and the semi-annual periodic variation amplitude, respectively. , These are the phase changes of the annual cycle and the phase changes of the semi-annual cycle. Let i be the day of the year corresponding to the i-th time series data.

[0014] Preferably, the spherical harmonic function deviation model for the land area is specifically as follows:

[0015] ;

[0016] Where i represents the parameter of the current spherical harmonic expansion, i = 0, 1, ..., 4; The annual average deviation The annual cycle variation amplitude, For the annual cycle phase, For the amplitude of the six-month cycle, The phase changes in a six-month cycle; K represents the order of the spherical harmonic function, k=1, 2, ..., 15; For Legendre polynomials; and It refers to the longitude and latitude of the current land-based measuring station; and is the spherical harmonic coefficient.

[0017] Preferably, the fitted ZTD data is applied to Precision Point Positioning (PPP) to obtain positioning accuracy and convergence time, thereby realizing the positioning of measurement points in the marine area. Specifically, the fitted ZTD data is embedded into a real-time PPP solution engine for positioning calculation, and positioning is performed based on the calculation results.

[0018] Preferably, the dynamic order selection mechanism at the monthly scale is as follows: the monthly average accuracy at different orders is calculated during the fitting process, and the order corresponding to the highest monthly average accuracy is taken as the best fitting order for that month.

[0019] Preferably, after acquiring the IGS-ZTD data and GGOS-ZTD data, the method further includes preprocessing the IGS-ZTD data and GGOS-ZTD data separately, specifically:

[0020] Set filtering criteria for the IGS-ZTD data and remove data that does not meet the criteria;

[0021] The GGOS-ZTD data is preprocessed using height reduction and bilinear interpolation. The height reduction is specifically performed using the following formula:

[0022] ;

[0023] ;

[0024] in, Represents the daily average value of the GGOS-ZTD grid; the height reduction factor is taken as -1.3737×10. -4 ; H1 represents the height of the grid point on the MSL; H2 and H1 represent the difference between the grid point's orthometric height and the MSL, respectively, and the difference between the IGS station's height and the MSL.

[0025] The result After bilinear interpolation to the location of the IGS station, the calculation is performed. And it is calculated to the height of the IGS station;

[0026] The time-series daily average deviation values ​​were calculated using preprocessed IGS-ZTD and GGOS-ZTD data.

[0027] This invention also provides a precise point positioning system for marine scenarios, specifically comprising:

[0028] The data acquisition module is used to acquire IGS-ZTD and GGOS-ZTD data from different land stations; and to calculate the time-series daily average deviation data using the IGS-ZTD and GGOS-ZTD data; wherein the IGS-ZTD data is the total zenith delay data measured on GNSS stations by the International Global Navigation Satellite Service; and the GGOS-ZTD data is the zenith tropospheric delay grid data provided by the Global Geodetic Observation System.

[0029] The model building module is used to input the time series daily average deviation data into the station data fitting model for fitting, and to expand the multiple deviation change values ​​in the station data fitting model using spherical harmonic functions to obtain the spherical harmonic function deviation model of the land area.

[0030] The positioning module is used to acquire the latitude and longitude of the marine station; the latitude and longitude of the marine station are input into the spherical harmonic function deviation model of the land area for fitting calculation, and a dynamic order screening mechanism at the monthly scale is introduced to obtain the monthly correction deviation value of the marine station; the monthly correction deviation value is superimposed on the ZTD data of the marine station to obtain the fitted ZTD data; the fitted ZTD data is applied to Precise Point Positioning (PPP) to obtain the positioning accuracy and convergence time, thereby realizing the positioning of the measuring point in the marine area.

[0031] The present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps described in the precise single-point positioning method in a marine scenario.

[0032] The present invention also provides a computer-readable storage medium storing a computer program that, when loaded by a processor, can execute the steps described in the precise single-point positioning method in a marine setting.

[0033] The precise single-point positioning method in a marine setting provided by this invention has the following beneficial effects:

[0034] This invention acquires IGS-ZTD and GGOS-ZTD data from different land stations, performs deviation calculation and fitting processing, and obtains a spherical harmonic function deviation model for the land region. It dynamically corrects the zenith direction delay along the signal propagation path and adjusts the ZTD data based on the model output. A monthly-scale dynamic selection mechanism is introduced based on the spherical harmonic function deviation model for the land region to address the regional differences in marine atmospheric activity. This improves the problem of accumulated delay compensation errors caused by the fixed tropospheric model in traditional PPP algorithms in marine scenarios, enabling automatic matching of optimal ZTD parameters according to latitude, longitude, and month, enhancing the model's adaptability to different sea areas. Precise single-point positioning calculation is performed using the fitted ZTD data as input, achieving positioning of measurement points in the marine region without equipment dependence and with low real-time computational burden. This reduces the computational impact of limited computing resources on marine platforms and enhances the timeliness of positioning. Attached Figure Description

[0035] To more clearly illustrate the embodiments and design schemes of the present invention, the accompanying drawings required for this embodiment will be briefly described below. The drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0036] Figure 1 This is a flowchart of a precise single-point positioning method in a marine setting according to an embodiment of the present invention.

[0037] Figure 2 This is a frequency statistics chart of the best-fit order of global IGS oceanographic stations in an embodiment of the present invention.

[0038] Figure 3 This is a diagram showing the time distribution verification results of global IGS oceanographic stations in an embodiment of the present invention.

[0039] Figure 4 This is a diagram showing the spatial distribution verification results of global IGS oceanographic stations in an embodiment of the present invention.

[0040] Figure 5 This is a comparison chart of the annual average accuracy of IGS oceanographic stations in the East, North, and Up directions under two strategies: enhanced PPP and estimated PPP, as shown in this embodiment of the invention.

[0041] Figure 6 This is a graph showing the relationship between the annual average Up direction accuracy improvement rate and latitude for ocean stations in the Northern and Southern Hemispheres in this embodiment of the invention.

[0042] Figure 7 This is a comparison of the convergence times of IGS oceanographic stations in the East, North, and Up directions under two strategies: enhanced PPP and estimated PPP, as shown in this embodiment of the invention.

[0043] Figure 8 This section shows the convergence results of the first hour for the ASCG, KOKB, SEY2, and THTG stations under two strategies: enhanced PPP and estimated PPP, in this embodiment of the invention. Figure 8 (a) shows the convergence of the marine station ASCG in the first hour under the two strategies; Figure 8 (b) shows the convergence of the KOKB marine station in the first hour under the two strategies; Figure 8 (c) shows the convergence of the marine station SEY2 in the first hour under the two strategies; Figure 8 (d) represents the convergence of the THTG marine station in the first hour under both strategies. Detailed Implementation

[0044] To enable those skilled in the art to better understand and implement the technical solutions of the present invention, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. The following embodiments are only used to more clearly illustrate the technical solutions of the present invention and should not be construed as limiting the scope of protection of the present invention.

[0045] Example

[0046] This invention provides a precise single-point positioning method in marine scenarios, such as... Figure 1 As shown, the specific steps include:

[0047] Step 1: Acquire IGS-ZTD and GGOS-ZTD data from different land stations. Preprocess the acquired data and calculate the time-series daily average deviation values ​​using the IGS-ZTD and GGOS-ZTD data. IGS-ZTD data is based on Zenith Total Delay (ZTD) data measured at GNSS stations by the International GNSS Service (IGS). GGOS-ZTD data is global grid data of Zenith ZTD provided by the Global Geodetic Observing System (GGOS).

[0048] S11: Regarding the data processing of IGS-ZTD, firstly, statistics are performed on each station. For the 288 ZTD data generated every 5 minutes in a day, the average value is calculated as the daily average value of the corresponding station.

[0049] Meanwhile, to ensure the reliability of the data, the daily ZTD data needs to be screened for gross errors. The standard is: if the daily average ZTD value is greater than 3m or the standard deviation is greater than 3.5mm, the data is removed.

[0050] S12: Regarding data processing for GGOS-ZTD: First, calculate the daily average value of the data generated daily at 0:00, 6:00, 12:00, and 18:00 from global grid points.

[0051] To unify the spatial positions of the two ZTDs, the daily average values ​​of GGOS-ZTD grid points were subjected to height reduction and bilinear interpolation. The specific height reduction calculation formula is as follows:

[0052] (1)

[0053] (2)

[0054] The GGOS-ZTD grid data is uniformly reduced to the Mean Sea Level (MSL) using equation (1). Represents the daily average value of the GGOS-ZTD grid; the height reduction factor is taken as -1.3737×10. -4 H1 is the difference between the grid point elevation and MSL. The result obtained from equation (1) is bilinearly interpolated to the location of the IGS station, and then the data is reduced to the height of the IGS station using equation (2). H1 represents the height of the grid point on the MSL; H2 is the difference between the height of the IGS station and the MSL.

[0055] S13: After spatiotemporal matching, the daily average deviation values ​​of the spatiotemporally matched IGS-ZTD and GGOS-ZTD are subtracted to calculate the annual time-series daily average deviation data. The ZTD accuracy data of each IGS station is compared with the ZTD accuracy after subsequent spherical harmonic fitting, and the accuracy improvement rate is calculated to quantify the model correction effect, such as... Figure 3 and Figure 4 As shown.

[0056] Step Two: For the annual time-series daily average deviation data series obtained from land stations, fit the data using the station data fitting model. The specific station data fitting model is as follows:

[0057] ;

[0058] in, This represents the daily average deviation value of the i-th time series data at the current station; multiple deviation change values ​​specifically include... , , , , ; , , These are the annual average deviation, the annual periodic variation amplitude, and the semi-annual periodic variation amplitude, respectively. , These are the phase changes of the annual cycle and the phase changes of the semi-annual cycle. Let i be the day of the year corresponding to the i-th time series data.

[0059] Based on 5 deviation variation values ​​for each land station , , , , Spherical harmonic expansions of spherical harmonic functions from order 1 to 15 were used to establish MBE spherical harmonic function models, which are the spherical harmonic function deviation models for the land region. Specifically:

[0060] ;

[0061] Where i represents the parameters of the current spherical harmonic expansion, i = 0, 1, ..., 4; K represents the order of the spherical harmonic function, k = 1, 2, ..., 15; For Legendre polynomials; and It refers to the longitude and latitude of the current land-based measuring station; and is the spherical harmonic coefficient.

[0062] Step 3: Obtain data from 59 IGS marine stations to form an independent validation set. Based on the spherical harmonic deviation model for land regions, input the latitude, longitude, and day of year of the marine stations to perform ZTD (Zero Tolerancing Time-Digital) fitting at sea. The output is the corrected MBE (Mean Differential Equivalent) for the corresponding day of year at each station, thus extending the model to the ocean.

[0063] In the process of calculating the ZTD (Zero-Temperature Detection) using spherical harmonics at sea, a dynamic monthly-scale order selection is introduced. The rule is: if the monthly average accuracy of a certain order of the spherical harmonic function is the highest, then it is considered the best fitting order for the current station in that month. The correction deviation values ​​for each month at each station are obtained using the best fitting order, and then superimposed on the original GGOS-ZTD to obtain the fitted ZTD data.

[0064] Step 4: Apply the fitted ZTD data to Precision Point Positioning (PPP) to obtain the positioning accuracy and convergence time, and verify the actual positioning effect.

[0065] Two PPP experiments were designed: the first experiment used spherical harmonic data of the best-fit order as prior constraints to enhance PPP; the second experiment relied on the local observation data of each station to estimate PPP. The positioning accuracy and convergence time of the two methods were compared and analyzed to evaluate the actual effect of GGOS-ZTD spherical harmonic correction data on improving PPP performance.

[0066] The specific results of the experimental treatment are as follows:

[0067] 1: GGOS-ZTD accuracy verification.

[0068] (1) The inspection is met within the land area.

[0069] This section will use established spherical harmonic function models of orders 1 to 15 to fit time-series data from 396 land stations in 2023 and solve for accuracy information. To evaluate the effectiveness of the spherical harmonic function models, this invention introduces the accuracy improvement rate as an evaluation index, which refers to the degree of improvement in accuracy of the fitted GGOS-ZTD data compared to its unprocessed data.

[0070] Table 1. Annual average accuracy of ZTD fitting of spherical harmonic function at land stations (unit: mm) and its improvement rate

[0071]

[0072] Table 1 shows that the accuracy of the spherical harmonic fitting data and the improvement rate of accuracy compared to the original data, from an overall analysis, all data accuracy was improved to varying degrees in the fitting of spherical harmonic functions from the 1st to the 15th order. Among them, the IGS land station achieved the best overall fitting effect in the 12th order spherical harmonic function model, which reduced the MAE and RMS of GGOS-ZTD by 10.1% and 8.9%, respectively.

[0073] (2) Compliance inspection outside the marine area.

[0074] like Figure 2 This shows the frequency of best-fit orders used monthly by the IGS oceanographic station. It can be visually observed that the frequency generally decreases as the order increases. Best-fit orders between 1 and 6 account for 79.5% of the total.

[0075] The annual average accuracy of the spherical harmonic fitting data was statistically analyzed, calculating its mean, maximum, and minimum values, and the accuracy improvement rate compared to the original data was analyzed. After model fitting, the overall systematic bias of the IGS oceanographic station was corrected to a certain extent, decreasing from -6.79 mm to -1.98 mm. The RMS index reflects an overall improvement of 10.3% in the ZTD of the marine area. Figure 3 and Figure 4 As shown, this is a verification map of the temporal distribution of global IGS oceanographic stations and a verification map of the spatial distribution of global IGS oceanographic stations.

[0076] Table 2. Annual average accuracy (unit: mm) and improvement rate of spherical harmonic fitting data from marine stations

[0077]

[0078] 2: Application of PPP in marine scenarios.

[0079] (1) Positioning accuracy. First, based on the spatial perspective, the annual average RMS of each marine station in the first 5 minutes in the East, North and Up directions is calculated using two strategies: enhanced PPP and estimated PPP.

[0080] like Figure 5 As shown, when evaluating the PPP effect, the accuracy improvement rate refers to the proportion of improvement in fitting accuracy of the enhanced model compared to the traditional estimation model, which is mainly reflected in the Up direction. Therefore, the accuracy improvement rate is only calculated in this direction. Figure 6 The monthly average accuracy of all oceanographic stations in the East, North, and Up directions for the first 5 minutes was statistically analyzed. Among all stations, only one station showed a slight decrease in ZTD data accuracy after spherical harmonic fitting (-0.9%), while the accuracy of the remaining 47 stations improved to varying degrees, ranging from 1.4% to 51.1%. The average positioning accuracy of all oceanographic stations improved by 19.9%.

[0081] Figure 6 The relationship between the annual average up-direction accuracy improvement rate and latitude for oceanographic stations in the Northern and Southern Hemispheres was calculated. In the Northern Hemisphere, the correlation coefficient between the enhanced PPP accuracy improvement rate and latitude in the first five minutes was 0.73, while in the Southern Hemisphere it was 0.68.

[0082] Table 3. Monthly average change in the improvement rate of enhanced PPP positioning accuracy in the Northern and Southern Hemispheres (unit: %)

[0083]

[0084] Table 3 compares the monthly average accuracy improvement rate of enhanced PPP in the Northern and Southern Hemispheres within the first five minutes. The Northern Hemisphere outperforms the Southern Hemisphere in terms of ZTD fitting accuracy improvement, while the Southern Hemisphere performs better in terms of enhanced PPP accuracy improvement. PPP positioning accuracy is constrained by factors such as satellite geometry, surface conditions, and atmospheric circulation. Therefore, although ZTD correction can systematically improve positioning accuracy, the interaction of these dynamic interference factors leads to irregular fluctuations in actual positioning results.

[0085] (2) Convergence time. For example... Figure 7Statistical analysis was performed on the daily convergence times of all oceanographic stations. On average, the convergence times of global IGS oceanographic stations decreased by 0.63 seconds, 2.50 seconds, and 49.38 seconds in the East, North, and Up directions, respectively. Overall, using the enhanced PPP model, the convergence time decreased for 28 stations (47%), remained unchanged for 2 stations, and increased for 18 stations. Subsequently, some stations were selected from each of these three scenarios, and the results were calculated using the 68th quantile to demonstrate the convergence process under the enhanced and traditional estimation PPP models. The rule for this statistical method is: at each epoch, the absolute values ​​of the PPP solutions for the current station throughout the year are sorted, and the 68th quantile value is taken to represent the positioning error of that station at that epoch.

[0086] Figure 8 This demonstrates the convergence performance of the ASCG, KOKB, SEY2, and THTG stations in the UP direction under two PPP modes within the first hour. Figure 8 (a) shows the convergence status of the marine station ASCG; Figure 8 (b) shows the convergence of the KOKB oceanographic station; Figure 8 (c) shows the convergence status of the marine station SEY2; Figure 8 (d) represents the convergence status of the THTG oceanographic station. The selected ASCG and SEY2 stations represent cases where the augmentation model converges faster, the KOKB station represents cases where the convergence times of the two models are comparable, and THTG represents cases where the estimation model converges faster. Compared to stations with faster convergence speeds in the augmentation model, the KOKB and THTG stations both reflect a common issue: initially, the augmentation PPP convergence is better than the estimation PPP, but as convergence progresses to a certain point, the convergence of the two models becomes roughly equivalent, or the estimation PPP convergence is better. This phenomenon is related to the update frequency of the ZTD data, and its temporal resolution has a significant impact on the convergence speed and positioning accuracy of PPP. In this invention, the temporal resolution of the preprocessed GGOS-ZTD data is 24 hours, providing a spherical harmonic fitting prior value at 00:00 for augmentation PPP calculation. Because ZTD data cannot be updated at high frequencies, it is difficult to accurately reflect the actual atmospheric delay at other times, thus affecting the accuracy of subsequent PPP calculations.

[0087] The method proposed in this invention achieves a ZTD accuracy of 1.72 cm in marine scenarios, representing an improvement of approximately 52% to 66% compared to mainstream models (UNB3m, GPT2w-5, GPT2w-1, and GGZTD accuracies of 5.15 cm, 3.75 cm, 3.69 cm, and 3.62 cm, respectively), thus validating its marine adaptability. Using the ZTD correction value of this invention as prior information, the PPP positioning accuracy in the Up direction can be improved by 19.9%, and the convergence time shortened by 49.38 s. It is hoped that this innovative method will provide more reliable location services for operations in relevant marine fields, especially in the absence of experimental data.

[0088] This invention also provides a precise point positioning system for marine scenarios, specifically comprising:

[0089] The data acquisition module is used to acquire IGS-ZTD and GGOS-ZTD data from different land stations; and to calculate the time-series daily average deviation data using the IGS-ZTD and GGOS-ZTD data. Among them, the IGS-ZTD data is the total zenith delay data measured on GNSS stations by the International Global Navigation Satellite Service; and the GGOS-ZTD data is the zenith tropospheric delay grid data provided by the Global Geodetic Observation System.

[0090] The model building module is used to input the time series daily average deviation data into the station data fitting model for fitting. The multiple deviation changes in the station data fitting model are expanded using spherical harmonic functions to obtain the spherical harmonic function deviation model of the land area.

[0091] The positioning module is used to acquire the latitude and longitude of the marine station; the latitude and longitude of the marine station are input into the spherical harmonic function deviation model of the land area for fitting calculation, and a dynamic order screening mechanism at the monthly scale is introduced to obtain the monthly correction deviation value of the marine station; the monthly correction deviation value is superimposed on the ZTD data of the marine station to obtain the fitted ZTD data; the fitted ZTD data is applied to Precise Point Positioning (PPP) to obtain the positioning accuracy and convergence time, thereby realizing the positioning of the measuring point in the marine area.

[0092] The modules in the aforementioned precise point positioning system for a marine scenario can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0093] The present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory. The processor executes the computer program to implement the steps in an embodiment of a precise single-point positioning method in a marine setting. Specific implementation methods can be found in the method embodiments, and will not be repeated here.

[0094] Furthermore, the present invention also provides a non-transitory computer-readable storage medium containing instructions, on which a computer program is stored. For example, a memory containing instructions that can be executed by a processor of a computer device to perform the above-described method. For example, the non-transitory computer-readable storage medium may be a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc. When the computer program is executed by the processor, it can implement the steps in an embodiment of a precise single-point positioning method in a marine setting. Specific implementation methods can be found in the method embodiments, which will not be repeated here.

[0095] Those skilled in the art will understand that embodiments of the present invention can provide methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0096] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, as well as combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0097] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0098] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0099] It should be noted that the specific embodiments described above enable those skilled in the art to more fully understand the present invention, but do not limit the present invention in any way. Therefore, although the present invention has been described in detail in this specification and embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the present invention; and all technical solutions and improvements that do not depart from the spirit and scope of the present invention are covered within the protection scope of the present invention patent. No reference numerals in the claims should be construed as limiting the scope of the claims. Any simple variations or equivalent substitutions of technical solutions that can be readily obtained by those skilled in the art within the scope of the technology disclosed in the present invention are within the protection scope of the present invention.

Claims

1. A precise single-point positioning method in a marine setting, characterized in that, Includes the following steps: IGS-ZTD and GGOS-ZTD data from different land stations are acquired, and the IGS-ZTD and GGOS-ZTD data are preprocessed separately, specifically as follows: Set filtering criteria for the IGS-ZTD data and remove data that does not meet the criteria; The GGOS-ZTD data is preprocessed using height reduction and bilinear interpolation. The height reduction is specifically performed using the following formula: ZTD MSL =ZTD h ·e βH1 ; ZTD=ZTD MSL ·e βH2 ; Among them, ZTD h Represents the daily average value of the GGOS-ZTD grid; β is the height reduction coefficient, taken as -1.3737×10. -4 ZTD MSL H1 represents the height of the grid point on the MSL; H2 and H1 represent the difference between the grid point's orthometric height and the MSL, respectively, and the difference between the IGS station's height and the MSL. The obtained ZTD MSL After bilinear interpolation to the IGS station location, the ZTD is calculated and reduced to the IGS station altitude. The time-series daily average deviation data is obtained using preprocessed IGS-ZTD and GGOS-ZTD data. The IGS-ZTD data is the total zenith delay data measured at GNSS stations by the International Organization for Global Navigation Satellite Systems (IOGS); the GGOS-ZTD data is the zenith tropospheric delay grid data provided by the Global Geodetic Observation System (GGES). The time series daily average deviation data is input into the station data fitting model for fitting. Multiple deviation change values ​​in the station data fitting model are expanded using spherical harmonic functions to obtain the spherical harmonic function deviation model of the land area. The latitude and longitude of the marine station are obtained; the latitude and longitude of the marine station are input into the spherical harmonic function deviation model of the land area for fitting calculation, and a monthly-scale dynamic order selection mechanism is introduced to obtain the monthly correction deviation value of the marine station; the monthly correction deviation value is superimposed on the ZTD data of the marine station to obtain the fitted ZTD data; the fitted ZTD data is applied to Precise Point Positioning (PPP) to obtain the positioning accuracy and convergence time, thereby realizing the positioning of the measuring point in the marine area; the monthly-scale dynamic order selection mechanism is as follows: the monthly average accuracy at different orders is calculated during the fitting process, and the order corresponding to the highest monthly average accuracy is taken as the optimal fitting order for the current month, which improves the accumulation of delay compensation error caused by the fixed tropospheric model in the marine scenario, realizes automatic matching of the optimal ZTD parameters according to latitude and longitude and month, and enhances the adaptability of the model to different sea areas.

2. The precise single-point positioning method in a marine setting according to claim 1, characterized in that, The data fitting model for the monitoring stations is specifically as follows: Among them, MBE i This represents the daily average deviation value of the i-th time series data at the current station; the multiple deviation change values ​​specifically include the annual average deviation value (a0), the annual periodic variation amplitude (a1), the semi-annual periodic variation amplitude (a3), the annual periodic variation phase (a2), and the semi-annual periodic variation phase (a4); doy i Let i be the day of the year corresponding to the i-th time series data.

3. The precise single-point positioning method in a marine setting according to claim 1, characterized in that, The spherical harmonic function deviation model for the land region is as follows: Among them, a i For multiple deviation variation values, i represents the parameters of the current spherical harmonic expansion, i = 0, 1, ..., 4; K represents the order of the spherical harmonic function, k = 1, 2, ..., 15; P nm For Legendre polynomials; λ and φ are the longitude and latitude of the current land station; and is the spherical harmonic coefficient.

4. The precise single-point positioning method in a marine setting according to claim 1, characterized in that, The fitted ZTD data is applied to Precision Point Positioning (PPP) to obtain positioning accuracy and convergence time, thereby realizing the positioning of measurement points in the marine area. Specifically, the fitted ZTD data is embedded into a real-time PPP solution engine for positioning calculation, and positioning is performed based on the calculation results.

5. A precise single-point positioning system for marine scenarios, characterized in that, include: The data acquisition module is used to acquire IGS-ZTD and GGOS-ZTD data from different land stations; the IGS-ZTD and GGOS-ZTD data are preprocessed separately, specifically as follows: Set filtering criteria for the IGS-ZTD data and remove data that does not meet the criteria; The GGOS-ZTD data is preprocessed using height reduction and bilinear interpolation. The height reduction is specifically performed using the following formula: ZTD MSL =ZTD h ·e βH1 ; ZTD=ZTD MSL ·e βH2 ; Among them, ZTD h Represents the daily average value of the GGOS-ZTD grid; β is the height reduction coefficient, taken as -1.3737×10. -4 ZTD MSL H1 represents the height of the grid point on the MSL; H2 and H1 represent the difference between the grid point's orthometric height and the MSL, respectively, and the difference between the IGS station's height and the MSL. The obtained ZTD MSL After bilinear interpolation to the IGS station location, the ZTD is calculated and reduced to the IGS station altitude. The time-series daily average deviation data is obtained using preprocessed IGS-ZTD and GGOS-ZTD data. The IGS-ZTD data is the total zenith delay data measured at GNSS stations by the International Organization for Global Navigation Satellite Systems (IOGS); the GGOS-ZTD data is the zenith tropospheric delay grid data provided by the Global Geodetic Observation System (GGES). The model building module is used to input the time series daily average deviation data into the station data fitting model for fitting, and to expand the multiple deviation change values ​​in the station data fitting model using spherical harmonic functions to obtain the spherical harmonic function deviation model of the land area. The positioning module is used to acquire the latitude and longitude of the marine station; the latitude and longitude of the marine station are input into the spherical harmonic function deviation model of the land area for fitting calculation, and a monthly-scale dynamic order selection mechanism is introduced to obtain the monthly correction deviation value of the marine station; the monthly correction deviation value is superimposed on the ZTD data of the marine station to obtain the fitted ZTD data; the fitted ZTD data is applied to Precise Point Positioning (PPP) to obtain the positioning accuracy and convergence time, thereby realizing the positioning of the measuring point in the marine area; the monthly-scale dynamic order selection mechanism specifically involves: calculating the monthly average accuracy at different orders during the fitting process, and using the order corresponding to the highest monthly average accuracy as the optimal fitting order for the current month, which improves the accumulation of delay compensation errors caused by the fixed tropospheric model in the marine scenario, realizes automatic matching of the optimal ZTD parameters according to latitude and longitude and month, and enhances the adaptability of the model to different sea areas.

6. A computer device, comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is loaded by the processor, it is able to perform the steps of the method according to any one of claims 1 to 4.

Images