A method for resolving integer ambiguity based on time correlation and adaptive sliding window

Abstract

A method for integer ambiguity resolution based on time correlation and adaptive sliding window belongs to the field of global satellite navigation system positioning. The method solves the problems of distortion of mean standard deviation evaluation, inaccuracy of fixed criterion and difficulty of adaptive adjustment of window length in existing methods. The method is as follows: collecting observation data and setting sampling interval, constructing double difference observation equation, solving ambiguity float solution, analyzing initial ambiguity float sequence window by autocorrelation function, calculating autocorrelation coefficient and correcting effective independent sample number in current window; calculating mean standard deviation of ambiguity float sequence window; comparing mean standard deviation of window with threshold value: less than or equal to threshold value, rounding mean value of float sequence and taking it as output, greater than threshold value, including ambiguity float solution of next epoch into current window and expanding window length until meeting fixed condition of integer ambiguity; outputting fixed value of integer ambiguity for subsequent positioning calculation. The method is used in the field of high-precision satellite navigation positioning.

Description

Technical Field

[0001] This invention belongs to the field of global satellite navigation system positioning technology, and in particular relates to a method for integer ambiguity resolution based on time correlation and adaptive sliding window. Background Technology

[0002] High-precision positioning technology using Global Navigation Satellite Systems (GNSS) is crucial for achieving high-precision positioning in fields such as surveying and mapping, autonomous driving, and precision agriculture. Among the key factors determining final positioning accuracy and convergence time is the ability to quickly and reliably fix the integer ambiguity in carrier phase observations. While the integer ambiguity is essentially an integer, in actual calculations, factors such as multipath effects, ionospheric delay residuals, and observation noise result in a floating-point solution containing a decimal part obtained directly from the observation equations. Therefore, correctly determining and fixing the integer value from the floating-point solution is a critical challenge that high-precision positioning must overcome.

[0003] In existing technologies, the most direct method is to round the floating-point ambiguity solution obtained independently for each epoch. This method is computationally simple and has high real-time performance. However, in dynamic and complex environments such as urban canyons and forests, observation noise increases significantly and becomes unstable. In such cases, the floating-point solution for a single epoch may deviate severely from its theoretical integer value. Directly rounding such heavily noise-contaminated values ​​can easily lead to incorrect fixing, i.e., fixing to an incorrect integer. This will directly result in positioning results with deviations at the decimeter or even meter level, resulting in very low reliability.

[0004] To improve the success rate of fixed ambiguity calculations, the industry commonly employs a sliding window averaging method based on multi-epoch smoothing. This method sets a fixed-time window, averages the floating-point ambiguity values ​​of all epochs within the window, and then rounds the average. Its core idea is to use statistical averaging to suppress the influence of random noise. However, this method has two main approaches in practical engineering implementation, both with inherent flaws. The first approach is high-frequency sampling (e.g., once per second) and treating all epoch observations as statistically independent samples for a sliding average. While this method seems to make full use of the data, its premise—that the observation errors between epochs are uncorrelated white noise—often does not hold true in practice. Due to the time correlation of errors such as receiver clock bias and multipath errors, there is also significant time correlation between the floating-point ambiguity solutions of consecutive epochs. Ignoring this correlation and directly calculating the average and standard deviation severely underestimates the uncertainty of the mean result, i.e., the standard deviation, thus statistically overestimating the confidence of the fixed ambiguity result. This can lead to the system outputting a fixed ambiguity value based on an incorrect assessment, even with seemingly accurate misjudgments, resulting in hidden misfixing with significant harm.

[0005] To circumvent the problems caused by temporal correlation, a second engineering approach emerged: artificially reducing the sampling frequency, for example, using data from one epoch only every 30 seconds or 1 minute, attempting to make the data more independent by increasing the epoch interval. While this method can reduce correlation to some extent, its drawback is that the window length and sampling interval cannot be changed once set. This results in a lack of flexibility in dealing with different environments. In scenarios with poor observation conditions and high noise levels, a fixed window may not contain enough effective information to reach the required confidence level for fixing, leading to fixing failure or slow convergence; conversely, in scenarios with good observation conditions, an excessively long window and sparse sampling will unnecessarily delay the initial fixing time, reducing the system's convergence efficiency and response speed. In short, the fixed-parameter sliding window method cannot adapt to changing environmental noise and data correlation, making it difficult to balance reliability and efficiency. Summary of the Invention

[0006] In view of this, the present invention aims to propose an integer ambiguity resolution method based on time correlation and adaptive sliding window, so as to solve the problems of existing fixed integer ambiguity methods in strong time correlation and complex noise environment, which lead to distorted mean and standard deviation evaluation, inaccurate fixed criteria, and difficulty in adaptively adjusting window length due to ignoring interepoch correlation or using fixed window parameters.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: a method for resolving integer ambiguity based on temporal correlation and adaptive sliding window, the method comprising: Step S1: Collect raw observation data from the satellite navigation system and set the sampling interval; Step S2: Process the original observation data to obtain the double-difference observation equation, which includes an integer ambiguity term; Step S3: Solve the ambiguity floating-point solution epoch by epoch according to the double-difference observation equation, and accumulate them in time order to form an initial ambiguity floating-point sequence window; Step S4: Perform autocorrelation function analysis on the ambiguity floating-point sequence in the current window, calculate the autocorrelation coefficient under each time delay, and correct the number of effective independent samples in the current window based on the autocorrelation coefficient; Step S5: Based on the number of valid independent samples, calculate the window mean standard deviation of the ambiguity floating-point sequence within the current window; Step S6: Compare the window mean standard deviation with a preset threshold: If the window mean standard deviation is less than or equal to the threshold, the mean is rounded and the rounded result is output as the fixed value of integer ambiguity; If the window mean standard deviation is greater than the threshold, the floating-point solution of the ambiguity of the next epoch is included in the current window to expand the window length, and the process returns to step S4. Step S7: Output the integer ambiguity fixed value and use it for subsequent positioning calculation.

[0008] Furthermore, a preferred method is proposed, wherein the double-difference observation equation in step S2 is:

[0009] Where y represents the residual between the double-difference pseudorange observation and the carrier phase observation. b The geometric baseline vector is represented by the design matrix. H ; a Represents integer ambiguity, and the corresponding design matrix is: F ; This is the noise term.

[0010] Furthermore, a preferred method is proposed, wherein the initial size of the ambiguity floating-point sequence window in step S3 is... N min =10.

[0011] Furthermore, a preferred method is proposed, wherein the number of effective independent samples in step S4 is... The calculation formula is:

[0012] in, N The number of data items in the window; k The time lag is the time delay. ρ k For time delay k The autocorrelation coefficient of the time data ranges from [-1, 1].

[0013] Furthermore, a preferred method is proposed, wherein the time delay is k The formula for calculating the autocorrelation coefficient of time data is:

[0014] Where t is the epoch index in the ambiguity floating-point sequence. For the floating-point solution of the fuzzyness corresponding to the t-th epoch, This represents the mean of the floating-point sequence of ambiguities within the window. This is the floating-point solution for the fuzzyness corresponding to the (t+k)th epoch.

[0015] Furthermore, a preferred method is proposed, wherein the formula for calculating the standard deviation of the mean in step S5 is:

[0016] in, The standard deviation of the window mean after adjustment for the number of effective independent samples. This represents the standard deviation of the mean of the floating-point sequence of ambiguities within the window.

[0017] Furthermore, a preferred method is proposed, wherein the preset threshold in step S5 is obtained through an ideal fixed success rate. Confirmed, the calculation formula is:

[0018] in, x This refers to the standard normal distribution quantile in the statistical success rate calculation formula; When the corrected standard deviation is greater than the preset threshold, the current window does not meet the integer ambiguity fixing condition; when the corrected standard deviation is less than or equal to the preset threshold, the mean of the data in the window is rounded to obtain the integer ambiguity fixing value corresponding to the current window.

[0019] Furthermore, a preferred method is proposed, wherein step S1 further includes: before collecting the original observation data, calculating the elevation angle of visible satellites based on the satellite ephemeris and the user's approximate location, and removing satellites with elevation angles lower than a set threshold.

[0020] Based on the same inventive concept, the present invention also proposes a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor runs the computer program stored in the memory, the processor executes an integer ambiguity resolution method based on time correlation and adaptive sliding window as described in any of the preceding claims.

[0021] Based on the same inventive concept, the present invention also proposes a computer-readable storage medium storing a computer program, which, when executed by a processor, performs the steps of a time-dependent and adaptive sliding window-based integer ambiguity resolution method as described in any of the above-mentioned embodiments.

[0022] Compared with the prior art, the beneficial effects of the present invention are: This invention addresses the temporal correlation characteristic of ambiguity floating-point sequences by introducing autocorrelation function analysis to correct the effective independent sample number within the sliding window. Based on this, the corrected standard deviation of the window mean is calculated, thereby establishing the ambiguity fixation criterion on statistics that match the actual temporal correlation.

[0023] Traditional methods either completely ignore the temporal correlation between epochs, mistakenly treating all data as independent samples and leading to an underestimated standard deviation; or they passively attempt to weaken correlation by using fixed long-interval sampling, but cannot accurately measure it. This invention, however, actively and quantitatively analyzes the strength of temporal dependencies between data by calculating the autocorrelation function of the ambiguity floating-point sequence within a window. Based on this, it introduces the effective independent sample count, using the calculated autocorrelation coefficient to discount and correct the nominal sample size. This corrected effective independent sample count truly reflects the statistically effective information content of the current data, and is then used to accurately calculate the true standard deviation of the window mean, ensuring that the final statistic used to determine the success rate of fixing is accurate and reliable, fundamentally avoiding overestimation of confidence and incorrect fixing caused by ignoring correlation.

[0024] Traditional methods use a fixed, pre-set window length, which cannot adapt to dynamically changing observation environments. The decision-making logic of this invention is entirely driven by the data itself. It uses whether the corrected true standard deviation reaches a preset reliability threshold as the sole criterion. If the mean accuracy of the data in the current window, after correlation correction, still does not meet the requirements, it will not blindly output data but will automatically include the data from the next history into the window, expanding the sample size, and then repeating the above iteration until the threshold requirement is met. In harsh environments with high noise and strong correlation, the window will automatically grow to accumulate sufficient effective information; in environments with good signal quality and weak data correlation, the window can converge quickly, requiring only a small N value to meet the fixed conditions, thus achieving fully adaptive dynamic adjustment of the window length.

[0025] By employing autocorrelation analysis and effective sample size correction, this invention accurately assesses the uncertainty of window mean results, thereby enabling fixed-point decisions based on true and stringent standard deviations. This fundamentally overcomes the problem of erroneous fixed-point decisions caused by underestimating the standard deviation in traditional sliding window averaging methods. This makes ambiguity fixed-point results more reliable in complex scenarios with high noise and strong temporal correlation, significantly improving the overall fixed-point success rate.

[0026] Traditional fixed-interval sampling methods often require long waiting times to ensure data independence, leading to slow initial convergence. This invention, through high-frequency sampling and an adaptive mechanism, can quickly reach a fixed threshold with fewer epochs when signal quality is good, significantly shortening the initial fixation time and accelerating localization convergence. In harsh environments, it can ensure reliability through automatic window expansion, avoiding the failure problem of fixing small windows under noise. Therefore, this invention can adaptively find the optimal balance between convergence speed and fixation success rate in different environments.

[0027] This invention eliminates the need for manually pre-setting the window length or sampling interval. Whether and by how much the window expands is entirely determined by the intrinsic quality of the real-time data and the pre-set reliability target. This gives the method a strong environmental adaptability, automatically adjusting to the optimal working state in static, dynamic, or varying noise environments such as urban areas and open fields, greatly improving the practicality and robustness of high-precision positioning systems in complex real-world applications. Attached Figure Description

[0028] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings: Figure 1 This is a flowchart of an integer ambiguity resolution method based on time correlation and adaptive sliding window as described in this invention. Detailed Implementation

[0029] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. It should be noted that, unless otherwise specified, the embodiments and features in the embodiments of the present invention can be combined with each other, and the described embodiments are only some embodiments of the present invention, not all embodiments.

[0030] Implementation Method 1: This implementation method addresses the problem that existing integer ambiguity fixing methods, whether simple direct rounding or fixed-parameter sliding window averaging, struggle to accurately evaluate the mean and standard deviation of the ambiguity floating-point sequence and adaptively determine the fixing timing in environments with strong time correlation and complex, variable noise. It proposes an integer ambiguity resolution method based on time correlation and an adaptive sliding window, the method comprising: Step S1: Collect raw observation data from the satellite navigation system and set the sampling interval; Step S2: Process the original observation data to obtain the double-difference observation equation, which includes an integer ambiguity term; Step S3: Solve the ambiguity floating-point solution epoch by epoch according to the double-difference observation equation, and accumulate them in time order to form an initial ambiguity floating-point sequence window; Step S4: Perform autocorrelation function analysis on the ambiguity floating-point sequence in the current window, calculate the autocorrelation coefficient under each time delay, and correct the number of effective independent samples in the current window based on the autocorrelation coefficient; Step S5: Based on the number of valid independent samples, calculate the window mean standard deviation of the ambiguity floating-point sequence within the current window; Step S6: Compare the window mean standard deviation with a preset threshold: If the window mean standard deviation is less than or equal to the threshold, then the mean of the fuzzy floating-point sequence within the current window is rounded, and the rounded result is output as the fixed value of the integer fuzziness; if the window mean standard deviation is greater than the threshold, then the fuzzy floating-point solution of the next epoch is included in the current window to extend the window length, and the process returns to step S4. Step S7: Output the integer ambiguity fixed value and use it for subsequent positioning calculation.

[0031] In this embodiment, the double-difference observation equation in step S2 is:

[0032] Where y represents the residual between the double-difference pseudorange observation and the carrier phase observation. b The geometric baseline vector is represented by the design matrix. H ; a Representing integer ambiguity, the corresponding design matrix is: F ; This represents the noise term. It should be noted that due to the temporal correlation between the observed data across different epochs, the variance-covariance matrix Q corresponding to the observed data... yy It typically contains significantly non-zero cross-correlated terms, rather than the ideal diagonal matrix. It is precisely the existence of this correlation that necessitates adjusting the number of independent samples in subsequent statistical analysis and ambiguity fixing processes to ensure the accuracy of confidence assessment.

[0033] The corresponding ambiguity floating-point solutions are obtained by solving the observation equation epoch by epoch. All the ambiguity floating-point numbers obtained by epoch recursion are accumulated in time series to form the ambiguity sequence {z1, z2, ..., z...} N}, where N is the window size. At this time... N It needs to be larger than the initial sliding window size. N min Since subsequent steps require autocorrelation function analysis, in this embodiment, the initial ambiguity floating-point sequence window size is... N min =10, which can be adjusted according to different noise levels.

[0034] In this embodiment, the number of valid independent samples in step S4 The calculation formula is:

[0035] in, N The number of data items in the window; k The time lag is the time delay. ρ k For time delayk The autocorrelation coefficient of the time data ranges from [-1, 1].

[0036] The effective number of independent samples can reflect the true independence of the window data. When there is no temporal correlation or the error is white noise, Neff is equivalent to N.

[0037] Furthermore, the time delay is k The formula for calculating the autocorrelation coefficient of time data is:

[0038] Where t is the epoch index in the ambiguity floating-point sequence. For the floating-point solution of the fuzzyness corresponding to the t-th epoch, This represents the mean of the floating-point sequence of ambiguities within the window. This is the floating-point solution for the fuzzyness corresponding to the (t+k)th epoch.

[0039] In this embodiment, the formula for calculating the corrected standard deviation of the mean in step S5 is:

[0040] in, The standard deviation of the window mean after adjustment for the number of effective independent samples. This represents the true standard deviation of the mean of the floating-point sequence of ambiguity within the window.

[0041] In this embodiment, the preset standard deviation threshold in step S5 achieves an ideal fixed success rate. Confirmed, the calculation formula is:

[0042] in, x This refers to the standard normal distribution quantile in the statistical success rate calculation formula. When the corrected standard deviation is greater than the preset threshold, the current window does not meet the integer ambiguity fixing condition; when the corrected standard deviation is less than or equal to the preset threshold, the mean of the data within the window is rounded to obtain the integer ambiguity fixing value corresponding to the current window.

[0043] The corrected standard deviation is compared with a preset standard deviation threshold. If the threshold requirement is met, the floating-point sequence of ambiguity within the current window is determined to satisfy the integer ambiguity fixation condition. If the above criterion is not met, the window length is automatically expanded, more epoch floating-point numbers are collected, statistical analysis and criterion judgment are re-performed, and the process is iterated until the window mean standard deviation reaches the threshold requirement.

[0044] Implementation Method 2, see below Figure 1This embodiment describes a complete implementation process for the integer ambiguity resolution method based on time correlation and adaptive sliding window described in Embodiment 1, including: Step S1: Using satellite ephemeris files and the user's approximate location, calculate the elevation and azimuth angles of all visible satellites, and remove all satellites with elevation angles below 20°. Set the sampling interval to 1 second and continuously collect raw observation data from the receiver to ensure the continuity and integrity of all raw observation information data streams.

[0045] Step S2: Receive B1, B2, and B3 frequency observation data from the BeiDou system. After processing the signals using ephemeris files, perform double-difference processing to obtain the pseudorange and carrier phase residuals, and construct the observation equations:

[0046] Where y represents the double-difference pseudorange observation. p Phase observations with carrier residual b The geometric baseline vector is represented by the design matrix. H ; a Representing integer ambiguity, the corresponding design matrix is: F ; This is the noise term.

[0047] Step S3: Solve the observation equation epoch by epoch to obtain the corresponding ambiguity floating-point solution. Accumulate all the ambiguity floating-point numbers obtained by epoch recursion to form an ambiguity sequence {z1, z2, ..., z...}. N}.at this time N It needs to be larger than the initial sliding window size. N min Since subsequent steps require autocorrelation function analysis, N min It needs to be at least greater than 10, and can be adjusted according to different noise levels. If the initial window size reaches N min Proceed to the next step; if the condition is not met, return to the previous step to accumulate new fuzzy floating-point values.

[0048] Step S4: Perform autocorrelation function analysis on the floating-point ambiguity values ​​within the initial window, calculate the autocorrelation coefficients at each time lag, and then correct the number of effective independent samples within the window. :

[0049]

[0050] in, N The number of data items in the window; k The time lag is the time delay. ρ k For time delay k The autocorrelation coefficient of the time data ranges from [-1, 1], and t is the epoch index in the ambiguity floating-point sequence. For the floating-point solution of the fuzzyness corresponding to the t-th epoch, This represents the mean of the floating-point sequence of ambiguities within the window. This is the floating-point solution for the fuzzyness corresponding to the (t+k)th epoch.

[0051] Step S5: Based on the number of valid samples N eff Calculate the true standard deviation of the mean of the floating-point sequence of ambiguity within the window.

[0052]

[0053] in, This is the standard deviation of the mean of the floating-point sequence of ambiguity within the window after time correlation correction, to avoid underestimation of the mean standard deviation due to the influence of time correlation. and Calculate according to the following formulas:

[0054]

[0055] in, For the i-th data in the window, i Index the data within the window.

[0056] Step S6: Compare the standard deviation of the mean with a preset threshold. If the standard deviation is less than the threshold, round the mean of all data within the window to the nearest integer, and use this as the fixed output for fuzziness. Based on the statistical success rate calculation formula, when the preset ideal fixed success rate is 99.9%, the threshold corresponding to the standard deviation of the mean of the data within the window is determined as follows: .

[0057]

[0058] When the ideal fixed success rate is set to 99.9%, the corresponding standard deviation threshold is; when If the current window does not meet the preset fixed success rate requirement, then the current window does not meet the requirement. At that time, the mean of the data in the window is rounded to the nearest integer to obtain the fixed integer ambiguity value corresponding to the current window.

[0059] If the standard deviation is less than the threshold, the mean of all data within the window is rounded to the nearest integer and used as the fixed fuzziness output.

[0060] If the threshold requirement is not met, the fuzzy floating-point number calculated in the next epoch is included in the window, the window length is expanded, and the autocorrelation analysis, effective sample number correction, and standard deviation calculation are repeated until the criterion is met.

[0061] Step S7: Once the ambiguity is fixed, the result is used for subsequent positioning or coordinate calculation. The entire process is iterative, continuously processing new data to achieve dynamic fixing and real-time updating of the ambiguity.

[0062] Implementation Method 3: This implementation method proposes a computer device, including a memory and a processor. The memory stores a computer program. When the processor runs the computer program stored in the memory, the processor executes an integer ambiguity resolution method based on time correlation and adaptive sliding window as described in any one of Implementation Methods 1 to 2.

[0063] Implementation Method 4: This implementation method proposes a computer-readable storage medium storing a computer program, which, when executed by a processor, performs the steps of a time-dependent and adaptive sliding window-based integer ambiguity resolution method as described in any one of Implementation Methods 1 to 2.

[0064] Those skilled in the art will understand that embodiments of this disclosure can be provided as methods, systems, or computer program products. Therefore, this disclosure can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this disclosure 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.

[0065] This disclosure is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It will be understood that each block of the flowchart illustrations and / or block diagrams, and 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, create a machine for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1The 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 operate 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.

[0066] 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.

[0067] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this disclosure and not to limit its protection scope. Although this disclosure has been described in detail with reference to the above embodiments, those skilled in the art should understand that after reading this disclosure, they can still make various changes, modifications or equivalent substitutions to the specific implementation of the invention, but these changes, modifications or equivalent substitutions are all within the protection scope of the pending claims.

Claims

1. A method for resolving integer ambiguity based on temporal correlation and adaptive sliding window, characterized in that, The method includes: Step S1: Collect raw observation data from the satellite navigation system and set the sampling interval; Step S2: Process the original observation data to obtain the double-difference observation equation, which includes an integer ambiguity term; Step S3: Solve the ambiguity floating-point solution epoch by epoch according to the double-difference observation equation, and accumulate them in time order to form an initial ambiguity floating-point sequence window; Step S4: Perform autocorrelation function analysis on the ambiguity floating-point sequence in the current window, calculate the autocorrelation coefficient under each time delay, and correct the number of effective independent samples in the current window based on the autocorrelation coefficient; Step S5: Based on the number of valid independent samples, calculate the window mean standard deviation of the ambiguity floating-point sequence within the current window; Step S6: Compare the window mean standard deviation with a preset threshold: If the window mean standard deviation is less than or equal to the threshold, then the mean of the fuzzy floating-point sequence within the current window is rounded, and the rounded result is output as the fixed value of the integer fuzziness; if the window mean standard deviation is greater than the threshold, then the fuzzy floating-point solution of the next epoch is included in the current window to extend the window length, and the process returns to step S4. Step S7: Output the integer ambiguity fixed value and use it for subsequent positioning calculation.

2. The integer ambiguity resolution method based on time correlation and adaptive sliding window according to claim 1, characterized in that, The double-difference observation equation in step S2 is: Where y represents the residual between the double-difference pseudorange observation and the carrier phase observation. b The geometric baseline vector is represented by the design matrix. H ; a Represents integer ambiguity, and the corresponding design matrix is: F ; This is the noise term.

3. The integer ambiguity resolution method based on time correlation and adaptive sliding window according to claim 1, characterized in that, The initial ambiguity floating-point sequence window size in step S3 N min =10.

4. The integer ambiguity resolution method based on time correlation and adaptive sliding window according to claim 1, characterized in that, The number of valid independent samples in step S4 The calculation formula is: in, N The number of data items in the window; k The time lag is the time delay. ρ k For time delay k The autocorrelation coefficient of the time data ranges from [-1, 1].

5. The integer ambiguity resolution method based on time correlation and adaptive sliding window according to claim 4, characterized in that, The time delay is k The formula for calculating the autocorrelation coefficient of time data is: Where t is the epoch index in the ambiguity floating-point sequence. For the floating-point solution of the fuzzyness corresponding to the t-th epoch, This represents the mean of the floating-point sequence of ambiguities within the window. This is the floating-point solution for the fuzzyness corresponding to the (t+k)th epoch.

6. The integer ambiguity resolution method based on time correlation and adaptive sliding window according to claim 5, characterized in that, The formula for calculating the window mean and standard deviation in step S5 is as follows: in, The standard deviation of the window mean after adjustment for the number of effective independent samples. This represents the standard deviation of the mean of the floating-point sequence of ambiguities within the window.

7. The integer ambiguity resolution method based on time correlation and adaptive sliding window according to claim 6, characterized in that, The preset threshold in step S5 achieves an ideal fixed success rate. Confirmed, the calculation formula is: in, x This refers to the standard normal distribution quantile in the statistical success rate calculation formula; When the corrected standard deviation is greater than the preset threshold, the current window does not meet the integer ambiguity fixing condition; when the corrected standard deviation is less than or equal to the preset threshold, the mean of the data in the window is rounded to obtain the integer ambiguity fixing value corresponding to the current window.

8. The integer ambiguity resolution method based on time correlation and adaptive sliding window according to claim 1, characterized in that, Step S1 further includes: before collecting raw observation data, calculating the elevation angle of visible satellites based on satellite ephemeris and the user's approximate location, and removing satellites with elevation angles lower than a set threshold.

9. A computer device, characterized in that: The device includes a memory and a processor. The memory stores a computer program. When the processor runs the computer program stored in the memory, the processor executes a method for integer ambiguity resolution based on time correlation and adaptive sliding window as described in any one of claims 1-8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, performs the steps of a time-dependent and adaptive sliding window-based integer ambiguity resolution method as described in any one of claims 1-8.

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