Method for obtaining optimal tide level inversion value from global navigation satellite system interferometric reflection measurement

Abstract

This paper discloses a method for obtaining the optimal tide inversion value from global navigation satellite system interferometric reflection measurements. This method addresses the low temporal resolution of tide inversion results by focusing on the output frequency of spectrum analysis. Using the LSP method to perform spectrum analysis on a signal-to-noise ratio residual sequence, the first four peaks of the analysis result are obtained, and the optimal GNSS-IR tide inversion result is extracted. This method achieves higher temporal resolution of tide inversion results and greater GNSS data utilization, demonstrating its high scientific research and application value.

Description

Technical Field

[0001] The invention belongs to the field of interferometric reflection remote sensing technology, mainly relates to the application and optimization of GNSS-IR (Global Navigation Satellite System-Interferometric Reflection, global navigation satellite system interferometric reflection measurement), and is a method for obtaining the best tide level inversion value of GNSS-IR interferometric reflection measurement. Background Art

[0002] Global Navigation Satellite System (GNSS) interferometric reflectometry is a technique that uses the interference of direct and reflected signals from the Global Navigation Satellite System (GNSS) to obtain surface information and geophysical parameters of ground objects. In recent years, through extensive research and experimentation, GNSS-IR has been gradually applied to sea surface tide detection, becoming a new altimetry technology. The temporal resolution of tide data directly affects the effectiveness of tidal change information. Therefore, while maintaining a certain level of accuracy, the higher the temporal resolution of tide data, the more accurate the tide analysis and sea level monitoring, and the better the results. Compared to traditional altimetry methods and radar altimetry, GNSS-IR altimetry offers advantages such as a wide monitoring range, minimal environmental impact, all-weather operation, and low cost.

[0003] In practical applications, when using GNSS-IR for tide inversion, it is necessary to estimate the primary frequencies of the signal-to-noise ratio residual sequence. Common spectral analysis methods include Lomb-Scargle spectral analysis (LSP) and wavelet analysis. Spectral analysis results have multiple peaks. When the spectral analysis is reliable, the highest peak is often many times higher than the secondary peaks, resulting in a large peak-to-noise ratio. When the peak-to-noise ratio is low, the signal-to-noise ratio sequence may have multiple primary frequencies. In this case, the frequency corresponding to the highest peak may not carry valid tide information. Therefore, when using the LSP method for tide inversion, the peak-to-noise ratio must be limited, resulting in an insufficient number of tide inversion results. After performing gross errors, inversion points may be missing, making it difficult to characterize tidal fluctuations. When using wavelet analysis for tide inversion, the inversion results of the LSP method must be used to eliminate gross errors, and the LSP method cannot be used independently. Summary of the Invention

[0004] Purpose of the invention: To overcome the defects of the prior art, the present invention provides a method for obtaining the best tide inversion value of global navigation satellite system interferometric reflection measurement.

[0005] Technical solution: To solve the above technical problems, the technical solution adopted by the present invention is:

[0006] In a first aspect, a method for obtaining an optimal tide level inversion value using interferometric reflection measurement of a global navigation satellite system is provided, comprising:

[0007] Step S1: Obtain observation data collected by a GNSS receiver, extract signal-to-noise ratio data from the observation data, and calculate satellite position information and user receiver position information;

[0008] Step S2: Calculating satellite elevation and azimuth using the satellite position information and user receiver position information;

[0009] Step S3: Preliminarily screening signal-to-noise ratio data suitable for tide inversion from the signal-to-noise ratio data based on the calculated satellite elevation angle and azimuth angle and the preset threshold range of the satellite elevation angle and azimuth angle;

[0010] Step S4: fitting the trend term of the signal-to-noise ratio time series of each satellite using a low-order polynomial based on the selected signal-to-noise ratio data, and extracting the residual time series of the reflected signal signal-to-noise ratio caused by the satellite reflected signal through the residual between the selected signal-to-noise ratio data and the fitted trend term;

[0011] Step S5: estimating the first four frequencies of the multipath signal-to-noise ratio based on the reflected signal signal-to-noise ratio residual time series;

[0012] Step S6: Calculating and obtaining a multi-frequency inversion tide value based on the first four frequencies;

[0013] Step S7: Taking satellites as units, the best inverted tide level value is selected from the multi-frequency inverted tide level values using a sliding window fitting method.

[0014] In some embodiments, step S5 includes:

[0015] S51. reordering the reflected signal signal-to-noise ratio residual time series according to the sine value of the satellite elevation angle of the corresponding epoch;

[0016] S52, performing spectrum analysis on the reordered reflected signal signal-to-noise ratio residual sequence using an LSP spectrum analysis method to obtain a spectrum analysis result, and determining the first four frequencies of the spectrum analysis result;

[0017] S53 . Based on the first four frequencies of the spectrum analysis result and a preset peak-to-noise ratio threshold, output the values of the frequencies f1 , f2 , f3 , and f4 according to preset conditions.

[0018] Furthermore, the step S53 includes:

[0019] When f1 / f2 is greater than or equal to the peak-to-noise ratio threshold, f2, f3, and f4 are assigned to 0;

[0020] When f1 / f2 is less than the peak-to-noise ratio threshold and f2 / f3 is greater than or equal to the peak-to-noise ratio threshold, f3 and f4 are assigned to 0;

[0021] When f1 / f2 and f2 / f3 are less than the peak-to-noise ratio threshold and f3 / f4 is greater than or equal to the peak-to-noise ratio threshold, f4 is assigned a value of 0;

[0022] In addition, f1 to f4 are all output as original values.

[0023] In some embodiments, the peak-to-noise ratio threshold is greater than 1, preferably 1.5-3, and more preferably 2.

[0024] In some embodiments, step S6 includes:

[0025] S61, using the first four frequencies to calculate the corresponding inversion tide value h i :

[0026]

[0027] Where i is the frequency order, λ is the wavelength of the carrier signal, and f i is the frequency value.

[0028] In some embodiments, step S6 further includes:

[0029] S62. Eliminate gross errors in the multi-frequency tide level inversion value based on the first four frequencies obtained, and obtain the multi-frequency tide level inversion value after eliminating gross errors:

[0030] S621. Eliminate the inverted tide level value calculated by assigning a frequency value of 0;

[0031] S622. Outputting a first threshold range according to the density distribution of the inverted tide values, and removing inverted tide values outside the first threshold range;

[0032] S623. If there is historical tide level data near the observation station, according to the second threshold range preset by the historical tide level data, the first threshold range and the second threshold range are intersected to obtain a third threshold range, and the inverted tide level values outside the third threshold range are eliminated.

[0033] In some embodiments, step S7 includes:

[0034] S71. Set the priority of tide level inversion values to h1>h2>h3>h4 to determine the initial inversion value sequence x(n). There are two cases when determining the initial inversion value sequence:

[0035] Case 1: When there is only one tide inversion value corresponding to a certain section of signal-to-noise ratio data, it is directly used as the initial inversion value, and the initial inversion value is taken as the optimal tide inversion value;

[0036] Case 2: When there are multiple tide inversion values corresponding to a certain section of signal-to-noise ratio data, the inversion value with the highest priority is selected as the initial inversion value according to the set priority.

[0037] S72. Based on a preset window radius M and sliding step size, select, in the order of the initial inversion value sequence, a window containing 2M+1 data points centered at one of the initial inversion values x(i) in x(n), and construct a p-order polynomial to fit the 2M+1 data points in the window to obtain a fitting curve q(n) in the window:

[0038] S73, based on the initial inversion value sequence x(n) and q(n), calculate the fitting residual δ; determine the polynomial fitting coefficient a when δ is minimized m , obtain the fitting curve q(n) in the window with the minimum δ, and determine the fitting tide level inversion value at the center point of the fitting curve in the window with the minimum δ;

[0039] S74, obtaining all fitted tide level inversion values of the initial inversion value by sliding the window according to a preset sliding step size;

[0040] S75, calculating the difference between a plurality of tide level inversion values and the corresponding fitted tide level inversion values; in response to the minimum difference being less than a preset limit, taking the tide level inversion value corresponding to the minimum difference as the output optimal tide level inversion value;

[0041] In response to the minimum difference being not less than the preset limit, the process returns to S71, sets the tide level inversion value of the next priority as the initial inversion value, and re-executes S71-S75.

[0042] In some embodiments, in S72, constructing a p-order polynomial to fit the 2M+1 data points in the window to obtain a fitting curve q(n) in the window includes:

[0043]

[0044] Where a m is the fitting coefficient, the fitting order p is not greater than 2M+1, n m are the terms of the polynomial;

[0045] In S73, based on the initial inversion value sequence x(n) and q(n), the fitting residual δ is calculated, including:

[0046]

[0047] In a second aspect, the present invention provides a device for obtaining an optimal tide level inversion value from interferometric reflection measurement of a global navigation satellite system, comprising a processor and a storage medium;

[0048] The storage medium is used to store instructions;

[0049] The processor is configured to operate according to the instructions to execute the steps of the method according to the first aspect.

[0050] In a third aspect, the present invention provides a storage medium having a computer program stored thereon, which implements the steps of the method described in the first aspect when executed by a processor.

[0051] Beneficial Effects: Compared with existing technologies, this method offers the following advantages: It addresses the problem of low temporal resolution in tide inversion results by focusing on the output frequency of spectrum analysis. Using the LSP method to perform spectrum analysis on the signal-to-noise ratio residual sequence, the first four peaks of the analysis results are obtained, and the optimal GNSS-IR tide inversion result is extracted. This method achieves higher temporal resolution in tide inversion results and greater GNSS data utilization, thus possessing high scientific research and application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 This is a flow chart of a method for obtaining the best GNSS-IR tide inversion value according to an embodiment of the present invention;

[0053] Figure 2 A geometric relationship diagram of a satellite reflection model according to an embodiment of the present invention;

[0054] Figure 3 A schematic diagram of estimating the signal-to-noise ratio frequency of a multipath signal using the Lomb-Scargle spectrum analysis method according to an embodiment of the present invention;

[0055] Figure 4 This is a result diagram of obtaining the optimal tide level by sliding the window in an embodiment of the present invention. DETAILED DESCRIPTION

[0056] In order to make the technical means, creative features, objectives and effects achieved by the present invention easier to understand, the present invention is further described below in conjunction with specific embodiments.

[0057] In the description of the present invention, "several" means more than one, "plurality" means more than two, "greater than," "less than," and "exceed" are understood to exclude the number itself, while "above," "below," and "within" are understood to include the number itself. The use of "first" and "second" in the description is solely for the purpose of distinguishing technical features and should not be construed as indicating or implying relative importance, implicitly specifying the number of the indicated technical features, or implicitly specifying the order of the indicated technical features.

[0058] In the description of the present invention, reference to terms such as "one embodiment," "some embodiments," "illustrative embodiments," "examples," "specific examples," or "some examples" means that the specific features, structures, materials, or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the exemplary expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0059] Example 1

[0060] A method for obtaining an optimal tide level inversion value from a global navigation satellite system interferometric reflection measurement, comprising:

[0061] Step S1: Obtain observation data collected by a GNSS receiver, extract signal-to-noise ratio data from the observation data, and calculate satellite position information and user receiver position information;

[0062] Step S2: Calculating satellite elevation and azimuth using the satellite position information and user receiver position information;

[0063] Step S3: Preliminarily screening signal-to-noise ratio data suitable for tide inversion from the signal-to-noise ratio data based on the calculated satellite elevation angle and azimuth angle and the preset threshold range of the satellite elevation angle and azimuth angle;

[0064] Step S4: fitting the trend term of the signal-to-noise ratio time series of each satellite using a low-order polynomial based on the selected signal-to-noise ratio data, and extracting the residual time series of the reflected signal signal-to-noise ratio caused by the satellite reflected signal through the residual between the selected signal-to-noise ratio data and the fitted trend term;

[0065] Step S5: estimating the first four frequencies of the multipath signal-to-noise ratio based on the reflected signal signal-to-noise ratio residual time series;

[0066] Step S6: Calculating and obtaining a multi-frequency inversion tide value based on the first four frequencies;

[0067] Step S7: Taking satellites as units, the best inverted tide level value is selected from the multi-frequency inverted tide level values using a sliding window fitting method.

[0068] In some specific embodiments, the GPS L1 band data of the 157th to 172nd day (15d) of 2021 at the French BRST (48.4°N, 4.5°W) station is taken as an example. Figure 1 This is an implementation flow chart of an embodiment of the present invention, and the specific steps are as follows:

[0069] Step 1: GNSS data preprocessing

[0070] GNSS receivers are used to collect data and obtain observation data. Signal-to-noise ratio data is extracted from it to calculate the satellite position coordinates and the user receiver position coordinates.

[0071] Step 2: Calculate satellite elevation and azimuth

[0072] The satellite altitude and azimuth angles are calculated using the satellite position and receiver position from step 1. The calculation formulas are shown in equations (1) and (2):

[0073]

[0074]

[0075] In formula (1) and formula (2), ELE is the elevation angle, AZI is the azimuth angle, is the receiver longitude; is the satellite orbit longitude, and β is the receiver latitude.

[0076] Step 3: Set the satellite elevation and azimuth thresholds to select satellite data suitable for tide inversion

[0077] Using the data extracted in steps one and two, combined with the Fresnel reflection principle and the spatial distribution of the target reflecting surface relative to the GNSS receiver, the threshold ranges of the satellite elevation angle and azimuth angle are reasonably set to preliminarily screen out satellite data suitable for tide inversion.

[0078] The filtering conditions are:

[0079] 1. The mirror reflection points of the signal are distributed within the target observation reflection surface area;

[0080] 2. The satellites are located in an area where the GNSS antenna is not blocked.

[0081] 3. The data collected by the GNSS receiver includes ephemeris orbit information and raw observation information.

[0082] Step 4: Extract satellite reflection signals

[0083] Figure 2 This is the geometric model diagram of the multipath effect of the satellite direct signal and the reflected signal in the receiving process. The received interference signal can be expressed by formula (3).

[0084]

[0085] In formula (3), A c Represents the amplitude of the composite signal, A d Indicates the amplitude of the direct signal, A r represents the amplitude of the multipath reflection signal, is the phase difference of the reflected signal relative to the direct signal.

[0086] According to the GNSS interference signal model of formula (3), the first term is the trend term dominated by the direct signal, and the second term is the signal-to-noise ratio oscillation term caused by the interference of the direct and reflected signals. It can be seen that in the signal-to-noise ratio time series, the direct signal determines the overall change trend of the interference signal, that is, A d Much larger than A r The impact of the reflected signal is mainly manifested in the high-frequency oscillation of the SNR, while the multipath phase delay is related to the height of the receiver antenna, as shown in Equation (4). Therefore, detrending the interference signal can better analyze the tide level information in the reflected signal.

[0087]

[0088] In formula (4), h is the height from the phase center of the receiver antenna to the reflecting surface, θ is the satellite elevation angle, and λ is the carrier signal wavelength.

[0089] A low-order polynomial is used to fit the trend term of each satellite's signal-to-noise ratio time series, and the reflected signal time series is extracted by the residual between the original signal and the fitted trend term. A quadratic function is fitted to the signal-to-noise ratio time series according to the least squares principle, as shown in Equation (5).

[0090] D SNR =ax 2 +bx+c (5)

[0091] In formula (5), D SNR is the fitted direct signal SNR value, a, b, c are the fitting coefficients, and x is the epoch number.

[0092] The original SNR sequence is subtracted from the fitting result to obtain the SNR time series of the reflected signal caused by the satellite reflected signal, as shown in formula (6).

[0093]

[0094] In formula (6), R SNR is the signal-to-noise ratio of the reflected signal after removing the trend term, A is the signal amplitude, and φ is the phase.

[0095] Step 5: Estimate the first 4 frequencies of the multipath signal-to-noise ratio

[0096] The first step is to resample the signal-to-noise ratio data

[0097] If we assume t = sinθ, Then Equation (6) can be transformed into a standard cosine function, as shown in Equation (7).

[0098] R SNR=A·cos(2π·f·t+φ) (7)

[0099] Equation (10) shows that the oscillation frequency f of the signal-to-noise ratio depends on the vertical distance h from the antenna to the reflecting surface and the sine value of the satellite elevation angle θ. Therefore, to obtain the reflection height, the time series of the signal-to-noise ratio residuals needs to be reordered according to the sine value of the satellite elevation angle at the corresponding epoch.

[0100] The second step is to estimate the multipath signal-to-noise ratio frequency

[0101] The signal-to-noise ratio data recorded by the receiver is usually sampled uniformly in time, but the sine values of the elevation angle are unevenly distributed, making it impossible to use traditional Fast Fourier Transform (FFT) for spectral analysis. However, Lomb-Scargle spectral analysis (LSP) can process samples with uneven intervals. Therefore, LSP spectral analysis is used to perform spectral analysis on the non-uniformly sampled signal-to-noise ratio residual sequence in step 4. Figure 3 This is the LSP spectrum analysis result of the signal-to-noise ratio data of the G1 satellite on the 160th day.

[0102] The third step is to set the peak-to-noise ratio threshold to control the frequency output:

[0103] When f1 / f2 is greater than or equal to 2, set f2, f3, and f4 to 0;

[0104] When f1 / f2 is less than 2 and f2 / f3 is greater than or equal to 2, set f3 and f4 to 0;

[0105] When f1 / f2 and f2 / f3 are less than 2 and f3 / f4 is greater than or equal to 2, f4 is assigned a value of 0;

[0106] In addition, f1 to f4 are all output as original values.

[0107] Step 6: Calculate multi-frequency inversion tide values

[0108] In the first step, the tide level is calculated using the frequency value in step 5 according to formula (8).

[0109]

[0110] In formula (8), i is the frequency rank.

[0111] The second step is to eliminate the gross errors in the multi-frequency tide level inversion values.

[0112] 1. Eliminate the tide level values calculated from the frequency values assigned 0 in step 5;

[0113] 2. Setting a first threshold range for output according to the density distribution of the inverted tidal values, and eliminating tidal values outside the first threshold range;

[0114] 3. If there is historical tide level data near the observation station, set the output second threshold range according to the historical tide level data, intersect the first threshold range and the second threshold range to obtain the third threshold range, and eliminate the tide level values outside the third threshold range.

[0115] Step 7: Obtain the best inverted tide value from h1 to h4 through the sliding window

[0116] After removing the gross errors from the multi-frequency tide inversion results in step six, some signal-to-noise ratio data correspond to only one tide inversion value, while some signal-to-noise ratio data segments correspond to multiple tide inversion values. The best tide inversion value is selected from the satellites using the sliding window fitting method.

[0117] The first step is to set the priority of tide inversion values to h1>h2>h3>h4 to determine the initial inversion value sequence x(n). There are two cases when determining the initial inversion value sequence:

[0118] Case 1: When there is only one tide inversion value corresponding to a certain section of signal-to-noise ratio data, it is directly used as the initial inversion value.

[0119] Case 2: When there are multiple tide inversion values corresponding to a certain section of signal-to-noise ratio data, they are selected according to the set priority, and the inversion value with the highest priority is selected as the initial inversion value.

[0120] The second step is to set the window radius M and the sliding step size. Select a window centered at x(i) containing 2M+1 data points and construct a p-order polynomial q(n) to fit the array, as shown in Formula (9).

[0121]

[0122] In formula (9), a m is the fitting coefficient, and the fitting order p is not greater than 2M+1.

[0123] In the third step, the fitting residual δ is obtained according to formula (10), the polynomial fitting coefficient when δ is the smallest is obtained, the fitting curve in the window is obtained, and the fitting value at the data center point is taken as the filtered value.

[0124]

[0125] The fourth step is to obtain all the fitting points of the initial inversion value by sliding the window according to the sliding step size, and output the optimal tide level inversion value according to the preset conditions. The obtained optimal tide level is as follows: Figure 4 shown.

[0126] The preconditions are as follows:

[0127] 1. For case 1 in the first step, directly use it as the output result;

[0128] 2. For the second case in step 1, calculate the difference between the inverted and fitted values. If the minimum difference is less than the tolerance, use that inverted value as the output. Otherwise, repeat step 1 and set the corresponding initial inverted value to the tidal level of the next priority (for example, if you set it to h1 the first time, you need to set it to h2 the next time you execute the algorithm).

[0129] To verify the accuracy of the present invention, the data from the Brest tide gauge station near the BRST station are considered as true values. A data extraction method using a sliding window to obtain the best GNSS-IR tide level inversion value is denoted as SW-LSP. The tide level results calculated by the traditional LSP method are compared with those calculated by the SW-LSP method. The final calculation results are shown in Table 1.

[0130] Table 1 Comparison of accuracy indicators

[0131]

[0132] It can be seen that the SW-LSP method achieves comparable inversion accuracy to the LSP method, reaching 0.3211m; the correlation coefficients are both better than 0.98; however, the average daily number of inversion points increases by 37.5% compared to the LSP method, reaching 19.77; and the maximum time interval decreases by 43.2% to 6.63h. Therefore, the proposed method can improve data utilization and enhance tide inversion results while maintaining accuracy.

[0133] Example 2

[0134] In a second aspect, this embodiment provides a device for obtaining an optimal tide level inversion value using interferometric reflection measurement of a global navigation satellite system, including a processor and a storage medium;

[0135] The storage medium is used to store instructions;

[0136] The processor is configured to operate according to the instructions to execute the steps of the method according to embodiment 1.

[0137] Example 3

[0138] In a third aspect, this embodiment provides a storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the method described in Example 1 are implemented.

[0139] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0140] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0141] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0142] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The steps for the function specified in one or more boxes.

[0143] It is understood from common technical knowledge that the present invention may be implemented by other embodiments that do not depart from its spirit or essential features. Therefore, the embodiments disclosed above are, in all respects, merely illustrative and not exclusive. All modifications within the scope of the present invention or equivalent to the scope of the present invention are intended to be encompassed by the present invention.

Claims

1. A method for obtaining the best tide inversion value from global navigation satellite system interferometric reflection measurement, characterized in that: include: Step S1: Obtain observation data collected by a GNSS receiver, extract signal-to-noise ratio data from the observation data, and calculate satellite position information and user receiver position information; Step S2: Calculating satellite elevation and azimuth using the satellite position information and user receiver position information; Step S3: Preliminarily screening signal-to-noise ratio data suitable for tide inversion from the signal-to-noise ratio data based on the calculated satellite elevation angle and azimuth angle and the preset threshold range of the satellite elevation angle and azimuth angle; Step S4: fitting the trend term of the signal-to-noise ratio time series of each satellite using a low-order polynomial based on the selected signal-to-noise ratio data, and extracting the residual time series of the reflected signal signal-to-noise ratio caused by the satellite reflected signal through the residual between the selected signal-to-noise ratio data and the fitted trend term; Step S5: estimating the first four frequencies of the multipath signal-to-noise ratio based on the reflected signal signal-to-noise ratio residual time series; Step S6: Calculating and obtaining a multi-frequency inversion tide value based on the first four frequencies; Step S7: Taking satellites as units, the best inverted tide level value is selected from the multi-frequency inverted tide level values using a sliding window fitting method.

2. The method for obtaining the optimal tide level inversion value of the global navigation satellite system interferometric reflection measurement according to claim 1, characterized in that: The step S5 comprises: S51. reordering the reflected signal signal-to-noise ratio residual time series according to the sine value of the satellite elevation angle of the corresponding epoch; S52, performing spectrum analysis on the reordered reflected signal signal-to-noise ratio residual sequence using an LSP spectrum analysis method to obtain a spectrum analysis result, and determining the first four frequencies of the spectrum analysis result; S53 . Based on the first four frequencies of the spectrum analysis result and a preset peak-to-noise ratio threshold, output the values of the frequencies f1 , f2 , f3 , and f4 according to preset conditions.

3. The method for obtaining the optimal tide level inversion value of the global navigation satellite system interferometric reflection measurement according to claim 2, characterized in that: The step S53 includes: When f1 / f2 is greater than or equal to the peak-to-noise ratio threshold, f2, f3, and f4 are assigned to 0; When f1 / f2 is less than the peak-to-noise ratio threshold and f2 / f3 is greater than or equal to the peak-to-noise ratio threshold, f3 and f4 are assigned to 0; When f1 / f2 and f2 / f3 are less than the peak-to-noise ratio threshold and f3 / f4 is greater than or equal to the peak-to-noise ratio threshold, f4 is assigned a value of 0; In addition, f1 to f4 are all output as original values.

4. The method for obtaining the optimal tide level inversion value of a global navigation satellite system interferometric reflection measurement according to claim 2 or 3, characterized in that: The peak-to-noise ratio threshold is greater than 1, preferably 1.5-3, and more preferably 2.

5. The method for obtaining the best tide inversion value of global navigation satellite system interferometric reflection measurement according to claim 1, characterized in that: The step S6 comprises: S61, using the first four frequencies to calculate the corresponding inversion tide value h i : Where i is the frequency order, λ is the wavelength of the carrier signal, and f i is the frequency value.

6. A method for obtaining the best tide inversion value of a global navigation satellite system interferometric reflection measurement according to claim 1 or 5, characterized in that: The step S6 further includes: S62. Eliminate gross errors in the multi-frequency tide level inversion value based on the first four frequencies obtained, and obtain the multi-frequency tide level inversion value after eliminating gross errors: S621. Eliminate the inverted tide level value calculated by assigning a frequency value of 0; S622. Outputting a first threshold range according to the density distribution of the inverted tide values, and removing inverted tide values outside the first threshold range; S623. If there is historical tide level data near the observation station, according to the second threshold range preset by the historical tide level data, the first threshold range and the second threshold range are intersected to obtain a third threshold range, and the inverted tide level values outside the third threshold range are eliminated.

7. The method for obtaining the best tide inversion value by interferometric reflection measurement of a global navigation satellite system according to claim 1, characterized in that: The step S7 comprises: S71. Set the priority of tide level inversion values to h1>h2>h3>h4 to determine the initial inversion value sequence x(n). There are two cases when determining the initial inversion value sequence: Case 1: When there is only one tide inversion value corresponding to a certain section of signal-to-noise ratio data, it is directly used as the initial inversion value, and the initial inversion value is taken as the optimal tide inversion value; Case 2: When there are multiple tide inversion values corresponding to a certain section of signal-to-noise ratio data, the inversion value with the highest priority is selected as the initial inversion value according to the set priority. S72. Based on a preset window radius M and sliding step size, select, in the order of the initial inversion value sequence, a window containing 2M+1 data points centered at one of the initial inversion values x(i) in x(n), and construct a p-order polynomial to fit the 2M+1 data points in the window to obtain a fitting curve q(n) in the window: S73, based on the initial inversion value sequence x(n) and q(n), calculate the fitting residual δ; determine the polynomial fitting coefficient a when δ is minimized m , obtain the fitting curve q(n) in the window with the minimum δ, and determine the fitting tide level inversion value at the center point of the fitting curve in the window with the minimum δ; S74, obtaining all fitted tide level inversion values of the initial inversion value by sliding the window according to a preset sliding step size; S75, calculating the difference between a plurality of tide level inversion values and the corresponding fitted tide level inversion values; in response to the minimum difference being less than a preset limit, taking the tide level inversion value corresponding to the minimum difference as the output optimal tide level inversion value; In response to the minimum difference being not less than the preset limit, the process returns to S71, sets the tide level inversion value of the next priority as the initial inversion value, and re-executes S71-S75.

8. The method for obtaining the best tide inversion value from GNSS interferometric reflection measurement according to claim 7, characterized in that: In S72, a p-order polynomial is constructed to fit the 2M+1 data points in the window to obtain a fitting curve q(n) in the window, including: Where a m is the fitting coefficient, the fitting order p is not greater than 2M+1, n m are the terms of the polynomial; And / or, in S73, based on the initial inversion value sequence x(n) and q(n), the fitting residual δ is calculated, including:

9. A device for obtaining the best tide inversion value from interferometric reflection measurement of a global navigation satellite system, characterized in that: including processors and storage media; The storage medium is used to store instructions; The processor is configured to operate according to the instructions to execute the steps of the method according to any one of claims 1 to 8.

10. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

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