Method for confirming gnss integer ambiguity and device thereof

Abstract

The application relates to the positioning field and discloses a GNSS integer ambiguity confirmation method and device. The method comprises the following steps: constructing a double-difference observation equation based on original observation and solving the double-difference observation equation to obtain a floating-point solution; searching for an ambiguity based on the floating-point solution to obtain optimal ambiguity and suboptimal ambiguity; respectively solving the optimal ambiguity and the suboptimal ambiguity to obtain corresponding optimal fixed solutions and suboptimal fixed solutions; respectively inversely calculating double-difference ambiguities of carriers by using the optimal fixed solutions and the suboptimal fixed solutions; and confirming whether the optimal fixed solution is a correct fixed solution according to comparison of decimal parts of the double-difference ambiguities. The optimal fixed solution and the suboptimal fixed solution are obtained by searching for the ambiguity, then the decimal parts of the double-difference ambiguities of all carriers inversely calculated by the optimal fixed solution and the suboptimal fixed solution are compared to confirm whether the optimal fixed solution searched at present is correct, a fixed threshold does not need to be set, and therefore the reliability of ambiguity confirmation is improved.

Description

Technical Field

[0001] This invention relates to the field of positioning, and in particular to a technology for confirming GNSS integer ambiguity. Background Technology

[0002] In GNSS relative positioning, the accuracy of the integer ambiguity confirmation method directly affects the final positioning accuracy. Incorrect integer ambiguity can lead to positioning results with deviations at the decimeter or even meter level. Whether the integer ambiguity can be accurately determined depends directly on the reliability of the ambiguity confirmation method.

[0003] Traditional ambiguity verification methods typically involve searching for ambiguities and then obtaining verification parameters based on ambiguity fixation data, such as the error ratio, ambiguity attenuation factor (adop), posterior variance, and the number of fixed satellites. These parameters are then used to determine the correctness of the ambiguity fixation. For example, the accuracy of the ambiguity fixation can be judged by comparing the verification parameters with a set threshold. However, these methods are highly dependent on the quality of the threshold setting, and in practical applications, ambiguity verification errors can still occur, where incorrect ambiguity fixation occurs but verification passes, ultimately resulting in an incorrect fixed solution.

[0004] Therefore, there is an urgent need for a new method for ambiguity verification to improve the reliability of ambiguity verification. Summary of the Invention

[0005] The purpose of this invention is to provide a method and apparatus for confirming GNSS integer ambiguity. The method involves performing an ambiguity search to obtain an optimal fixed solution and a suboptimal fixed solution. Then, by comparing the fractional parts of the double-difference ambiguities of all carriers calculated from the optimal and suboptimal fixed solutions, the method confirms whether the currently searched optimal fixed solution is correct. This eliminates the need to set a fixed threshold, thereby improving the reliability of ambiguity confirmation.

[0006] To address the aforementioned technical problems, embodiments of the present invention disclose a method for confirming GNSS integer ambiguity, comprising the following steps:

[0007] A double-difference observation equation is constructed based on the original observations, and the double-difference observation equation is solved to obtain a floating-point solution;

[0008] Based on the floating-point solution, a fuzziness search is performed to obtain the optimal fuzziness and the second-best fuzziness.

[0009] The corresponding optimal fixed solution and suboptimal fixed solution are obtained by using the optimal fuzziness and suboptimal fuzziness respectively;

[0010] The double-difference ambiguity of the carrier wave is calculated using the optimal fixed solution and the suboptimal fixed solution respectively.

[0011] Based on the comparison of the fractional parts of the double-difference ambiguity, it is confirmed whether the optimal fixed solution is a correct fixed solution.

[0012] Embodiments of the present invention also disclose a GNSS integer ambiguity verification device, comprising:

[0013] The double-difference observation equation construction module is used to construct double-difference observation equations based on the original observations and to solve the double-difference observation equations to obtain floating-point solutions.

[0014] The fuzziness search module is used to perform fuzziness search based on the floating-point solution to obtain the optimal fuzziness and the second-best fuzziness.

[0015] The fixed solution calculation module is used to calculate the corresponding optimal fixed solution and suboptimal fixed solution using the optimal fuzziness and suboptimal fuzziness, respectively.

[0016] The inverse calculation module is used to inversely calculate the double-difference ambiguity of the carrier using the optimal fixed solution and the suboptimal fixed solution respectively;

[0017] The confirmation module is used to confirm whether the optimal fixed solution is a correct fixed solution based on the comparison of the decimal parts of the double-difference ambiguity.

[0018] The main differences and effects of the embodiments of the present invention compared with the prior art are as follows:

[0019] By performing ambiguity search to obtain the optimal fixed solution and the second-best fixed solution, and then comparing the fractional parts of the double-difference ambiguities of all carriers calculated by the optimal and second-best fixed solutions respectively, the correctness of the currently searched optimal fixed solution can be confirmed. There is no need to set a fixed threshold, thereby improving the reliability of ambiguity confirmation.

[0020] Furthermore, after performing ambiguity search using the LAMBDA algorithm, the optimal fixed solution and the second-best fixed solution are used to back-calculate the double-difference ambiguity of all carriers. By comparing the integer characteristics of the double-difference ambiguities of all carriers back-calculated by the optimal fixed solution and the second-best fixed solution, it is confirmed whether the currently searched optimal solution is the correct fixed solution, thereby improving the reliability of ambiguity confirmation.

[0021] The specification of this application contains numerous technical features distributed across various technical solutions. Listing all possible combinations of these technical features (i.e., technical solutions) would make the specification excessively lengthy. To avoid this problem, the various technical features disclosed in the above-described invention, the various technical features disclosed in the following embodiments and examples, and the various technical features disclosed in the accompanying drawings can be freely combined to form various new technical solutions (all of which are considered to have been described in this specification), unless such a combination of technical features is technically infeasible. For example, one example discloses feature A+B+C, and another example discloses feature A+B+D+E. Features C and D are equivalent technical means that serve the same function, and technically only one needs to be used; they cannot be used simultaneously. Feature E can technically be combined with feature C. Therefore, the solution A+B+C+D should not be considered as described because it is technically infeasible, while the solution A+B+C+E should be considered as described. Attached Figure Description

[0022] Figure 1 This is a flowchart illustrating a method for confirming GNSS integer ambiguity according to the first embodiment of the present invention.

[0023] Figure 2 This is a flowchart illustrating a preferred embodiment of the first embodiment of the present invention;

[0024] Figure 3 This is a schematic diagram of the structure of a GNSS integer ambiguity verification device according to the second embodiment of the present invention. Detailed Implementation

[0025] In the following description, numerous technical details are presented to facilitate the reader's understanding of this application. However, those skilled in the art will understand that the technical solutions claimed in the claims of this application can be implemented even without these technical details and with various variations and modifications based on the following embodiments.

[0026] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.

[0027] Explanation of terms:

[0028] Integer ambiguity: Integer ambiguity, also known as integer unknown, is the integer unknown corresponding to the first observation of the phase difference between the carrier phase and the reference phase when measuring the carrier phase in GPS technology.

[0029] In GNSS relative positioning, using carrier phase observations for double-difference ambiguity calculations can eliminate or significantly reduce some errors between the base station and the rover, such as satellite ephemeris errors, satellite clock errors, receiver clock errors, ionospheric delay, tropospheric delay, etc., ultimately giving the double-difference ambiguity integer characteristics, meaning the decimal part of the double-difference ambiguity is close to 0. When using the correct rover position and the known base station position for inverse calculation of the double-difference ambiguity, the decimal part of the calculated double-difference ambiguity is almost always close to 0. However, if an incorrect rover position and the known base station position are used for inverse calculation, the decimal part of the calculated double-difference ambiguity will show many cases where it is not close to 0. Therefore, the inverse calculation result of double-difference ambiguity using the optimal fixed solution obtained after ambiguity search using the LAMBDA algorithm should be better than the inverse calculation result of double-difference ambiguity using the suboptimal fixed solution. If the inverse calculation result of double-difference ambiguity using the optimal fixed solution obtained after ambiguity search using the LAMBDA algorithm is worse than the suboptimal fixed solution, it indicates that there is a problem with the current optimal solution obtained by ambiguity search, and the ambiguity confirmation fails.

[0030] The main technical problem solved by this invention is to use the double-difference ambiguity of all carriers calculated by back-calculating the optimal fixed solution and the suboptimal fixed solution obtained after ambiguity search using the LAMBDA algorithm, so as to confirm whether the currently searched optimal solution is correct, thereby improving the reliability of ambiguity confirmation.

[0031] The first embodiment of the present invention relates to a method for confirming GNSS integer ambiguity. Figure 1 This is a flowchart illustrating the method for confirming the integer ambiguity of this GNSS.

[0032] Specifically, such as Figure 1 As shown, the method for confirming GNSS integer ambiguity includes the following steps:

[0033] In step 101, a double-difference observation equation is constructed based on the original observations, and the double-difference observation equation is solved to obtain a floating-point solution.

[0034] Then proceed to step 102, where a fuzziness search is performed based on the floating-point solution to obtain the optimal fuzziness and the second-best fuzziness.

[0035] Furthermore, preferably, in step 102, the fuzzy search based on the floating-point solution is performed using the LAMBDA algorithm.

[0036] Then proceed to step 103, where the optimal fuzziness and the suboptimal fuzziness are used to calculate the corresponding optimal fixed solution and suboptimal fixed solution, respectively.

[0037] Then proceed to step 104, where the double-difference ambiguity of the carrier is calculated using the optimal fixed solution and the suboptimal fixed solution respectively.

[0038] In this embodiment, preferably, the original observations come from multiple satellite systems.

[0039] Accordingly, preferably,

[0040] In step 101, the above step of constructing the double-difference observation equation based on the original observations includes the following sub-steps:

[0041] Reference satellites of the various satellite systems are selected respectively, and corresponding double-difference observation equations are constructed.

[0042] Step 104 includes the following sub-steps:

[0043] The double-difference ambiguity of the carrier wave for all satellite systems is calculated by using the optimal and suboptimal fixed solutions for all satellite systems.

[0044] Then proceed to step 105, where the optimal fixed solution is confirmed to be the correct fixed solution based on the comparison of the fractional parts of the double-difference ambiguity.

[0045] Further, preferably, step 105 includes the following sub-steps:

[0046] Extract the fractional part of the carrier double-difference ambiguity calculated from the optimal fixed solution and the suboptimal fixed solution;

[0047] Calculate the average and variance of the fractional parts of the double-difference ambiguities calculated from the optimal fixed solution and the suboptimal fixed solution, respectively;

[0048] By comparing the average and variance of the decimal parts of the double-difference ambiguities calculated from the optimal fixed solution and the suboptimal fixed solution, it can be determined whether the ambiguity fixation is correct.

[0049] Furthermore, preferably, the step of comparing the average and variance of the decimal parts of the double-difference ambiguities calculated from the optimal fixed solution and the suboptimal fixed solution to determine whether the ambiguity fixation is correct may further include the following sub-steps:

[0050] Determine whether the average value and variance of the fractional part of the double-difference ambiguity calculated by the optimal fixed solution are both greater than the average value and variance of the fractional part of the double-difference ambiguity calculated by the suboptimal fixed solution.

[0051] If yes, then the ambiguity is fixed incorrectly; if no, then the ambiguity is fixed correctly.

[0052] This process will then end.

[0053] In summary, the embodiments of the present invention perform ambiguity search to obtain the optimal fixed solution and the second-best fixed solution, and then confirm whether the currently searched optimal fixed solution is correct by comparing the fractional parts of the double-difference ambiguities of all carriers calculated by the optimal fixed solution and the second-best fixed solution respectively. It is not necessary to set a fixed threshold, thereby improving the reliability of ambiguity confirmation.

[0054] This application utilizes the LAMBDA algorithm to perform ambiguity search, and then uses the optimal fixed solution and the second-best fixed solution to inversely calculate the double-difference ambiguity of all carriers. By comparing the integer characteristics of the double-difference ambiguities of all carriers inversely calculated by the optimal fixed solution and the second-best fixed solution, it confirms whether the currently searched optimal solution is a correct fixed solution, thereby improving the reliability of ambiguity confirmation.

[0055] To better understand the technical solution of this specification, a preferred embodiment will be described below. The details listed in this preferred embodiment are mainly for ease of understanding and are not intended to limit the scope of protection of this application.

[0056] The technical solution of this preferred embodiment is as follows:

[0057] In this embodiment, a method for confirming GNSS integer ambiguity is disclosed. The optimal fixed solution and the suboptimal fixed solution obtained by performing ambiguity search using the LAMBDA algorithm are used to back-calculate the double-difference ambiguity of all carriers. The correctness of the current fix is ​​determined by comparing the integer characteristics of the two solutions. It is not necessary to set a fixed threshold, which can effectively improve the reliability of ambiguity confirmation.

[0058] Figure 2 This is a flowchart illustrating the process of this preferred embodiment. Specifically, as shown below... Figure 2 As shown, the method for confirming GNSS integer ambiguity mainly includes the following steps:

[0059] 1. Construct a double-difference observation equation based on the original observations, solve the double-difference observation equation, and obtain a floating-point solution.

[0060] 2. The LAMBDA algorithm is used to perform fuzziness search, and two sets of candidate fuzziness results are obtained, namely the optimal fuzziness and the second-best fuzziness.

[0061] 3. The optimal and suboptimal fixed solutions are obtained by solving the optimal and suboptimal fuzzy solutions respectively.

[0062] 4. Calculate the double-difference ambiguity of all carriers using the optimal fixed solution and the suboptimal fixed solution respectively.

[0063] a) Construct the non-differenced observation equation. The carrier observation equation for the rover station for the i-th satellite is:

[0064]

[0065] In the formula, φ is the carrier observation, (X p Y p Z p (X) represents satellite coordinates. i Y i Z i V represents the coordinates of the optimal or suboptimal fixed solution. tR V is the receiver clock bias. ts V represents satellite clock bias, N represents integer ambiguity, and V represents... ion For ionospheric residuals, V trop This is the tropospheric residual.

[0066] b) Construct a single-difference observation equation. If at time t1, rover i and base station j simultaneously perform carrier phase measurements on satellite p, taking into account... Then, we can obtain:

[0067]

[0068] In the formula, φ represents the carrier wave observation. (X p Y p Z p (X) represents satellite coordinates. For the rover, (X) i Y i Z i (X) represents the coordinates of the optimal or suboptimal fixed solution. For the base station, (X) i Y i Z i ( ) represents the known coordinates of the base station. and For satellite clock bias, V tp Where is the receiver clock bias, N is the integer ambiguity, and V is the receiver clock bias. ion For ionospheric residuals, V trop For tropospheric residuals;

[0069] Subtracting the two equations above, we obtain the single-difference observation equation for satellite p:

[0070]

[0071]

[0072] make

[0073]

[0074]

[0075] The single-difference observation equation for satellite p can then be simplified to:

[0076]

[0077] c) Construct the double-difference observation equation. If receivers i and j simultaneously perform carrier phase measurements on satellite q at time t1, then the single-difference observation equation for satellite q is:

[0078]

[0079] Subtracting the single-difference observation equation for satellite p from the single-difference observation equation for satellite q yields the double-difference observation equation:

[0080]

[0081] make

[0082]

[0083]

[0084] Therefore, the double difference equation can be simplified to:

[0085]

[0086] For short baselines, the last two terms on the right-hand side of the simplified double-difference observation equation are close to 0, resulting in:

[0087]

[0088] in, This is the obtained double-difference ambiguity.

[0089] d) Select reference satellites respectively, and then sequentially calculate the double-difference ambiguities ΔN1, ΔN2, ... ΔN of the carrier wave calculated from all optimal and suboptimal fixed solutions. n .

[0090] 5. Extract the fractional part ΔN of the carrier double-difference ambiguity calculated from all optimal and suboptimal fixed solutions. dot1 ΔN dot2 、…ΔN dotn Calculate the average and variance of the decimal parts of the ambiguity calculated from the optimal and suboptimal fixed solutions, respectively.

[0091] 6. Compare the mean and variance of the fractional part of the ambiguity calculated from the optimal fixed solution and the suboptimal fixed solution. If the mean and variance of the fractional part of the ambiguity of the optimal fixed solution are both greater than the mean and variance of the fractional part of the ambiguity of the suboptimal fixed solution, then the current fixed solution is considered to be incorrect.

[0092] The double-difference ambiguities of all carriers are calculated by back-calculating the optimal fixed solution and the second-best fixed solution after ambiguity search using the LAMBDA algorithm. The integer characteristics of the double-difference ambiguities of all carriers calculated by the optimal fixed solution and the second-best fixed solution are compared to confirm whether the currently searched optimal solution is the correct fixed solution, thereby improving the reliability of ambiguity confirmation.

[0093] In summary, this preferred embodiment performs ambiguity search to obtain the optimal fixed solution and the second-best fixed solution. Then, by comparing the fractional parts of the double-difference ambiguities of all carriers calculated by the optimal fixed solution and the second-best fixed solution respectively, it is confirmed whether the currently searched optimal fixed solution is correct. It is not necessary to set a fixed threshold, thereby improving the reliability of ambiguity confirmation.

[0094] All embodiments of the present invention can be implemented in software, hardware, firmware, etc. Regardless of whether the present invention is implemented in software, hardware, or firmware, the instruction code can be stored in any type of computer-accessible memory (e.g., permanent or modifiable, volatile or non-volatile, solid-state or non-solid-state, fixed or replaceable media, etc.). Similarly, the memory can be, for example, Programmable Array Logic (PAL), Random Access Memory (RAM), Programmable Read Only Memory (PROM), Read-Only Memory (ROM), Electrically Erasable Programmable ROM (EEPROM), magnetic disk, optical disk, Digital Versatile Disc (DVD), etc.

[0095] The second embodiment of the present invention relates to a device for confirming GNSS integer ambiguity. Figure 3 This is a schematic diagram of the GNSS integer ambiguity verification device.

[0096] Specifically, such as Figure 3 As shown, the GNSS integer ambiguity verification device includes:

[0097] The double-difference observation equation construction module is used to construct double-difference observation equations based on the original observations and to solve the double-difference observation equations to obtain floating-point solutions.

[0098] The fuzziness search module is used to perform fuzziness search based on the floating-point solution to obtain the optimal fuzziness and the second-best fuzziness.

[0099] The fixed solution calculation module is used to calculate the corresponding optimal fixed solution and suboptimal fixed solution using the optimal fuzziness and suboptimal fuzziness, respectively.

[0100] The inverse calculation module is used to inversely calculate the double-difference ambiguity of the carrier using the optimal fixed solution and the suboptimal fixed solution respectively;

[0101] The confirmation module is used to confirm whether the optimal fixed solution is a correct fixed solution based on the comparison of the decimal parts of the double-difference ambiguity.

[0102] In this embodiment, preferably, the LAMBDA algorithm is used for fuzziness search in the fuzziness search module.

[0103] Further, preferably, the original observations come from multiple satellite systems.

[0104] The double-difference observation equation construction module is used to select reference satellites of the multiple satellite systems respectively and construct corresponding double-difference observation equations.

[0105] The inverse calculation module is used to inversely calculate the double-difference ambiguity of the carrier wave for all satellite systems using the optimal and suboptimal fixed solutions for all satellite systems respectively.

[0106] Further, preferably, the confirmation module may include the following sub-modules:

[0107] The truncation submodule is used to truncate the fractional part of the double-difference ambiguity of the carrier calculated from the optimal fixed solution and the suboptimal fixed solution.

[0108] The calculation submodule is used to calculate the average and variance of the fractional parts of the double-difference ambiguities calculated from the optimal fixed solution and the suboptimal fixed solution, respectively.

[0109] The comparison submodule is used to compare the average and variance of the fractional parts of the double-difference ambiguities calculated by the optimal fixed solution and the suboptimal fixed solution, thereby determining whether the ambiguity fixation is correct.

[0110] Furthermore, preferably, the comparison submodule may further include the following submodules:

[0111] The judgment submodule is used to determine whether the average value and variance of the fractional part of the double-difference ambiguity calculated by the optimal fixed solution are both greater than the average value and variance of the fractional part of the double-difference ambiguity calculated by the suboptimal fixed solution.

[0112] A determination submodule is used to determine that the ambiguity fixation is incorrect when the determination submodule determines it to be true, and to determine that the ambiguity fixation is correct when the determination submodule determines it to be false.

[0113] In summary, the embodiments of the present invention perform ambiguity search to obtain the optimal fixed solution and the second-best fixed solution, and then confirm whether the currently searched optimal fixed solution is correct by comparing the fractional parts of the double-difference ambiguities of all carriers calculated by the optimal fixed solution and the second-best fixed solution respectively. It is not necessary to set a fixed threshold, thereby improving the reliability of ambiguity confirmation.

[0114] This application utilizes the LAMBDA algorithm to perform ambiguity search, and then uses the optimal fixed solution and the second-best fixed solution to inversely calculate the double-difference ambiguity of all carriers. By comparing the integer characteristics of the double-difference ambiguities of all carriers inversely calculated by the optimal fixed solution and the second-best fixed solution, it confirms whether the currently searched optimal solution is a correct fixed solution, thereby improving the reliability of ambiguity confirmation.

[0115] This embodiment is a device embodiment corresponding to the first embodiment, and this embodiment can be implemented in conjunction with the first embodiment. The relevant technical details mentioned in the first embodiment remain valid in this embodiment, and will not be repeated here to avoid repetition. Correspondingly, the relevant technical details mentioned in this embodiment can also be applied to the first embodiment.

[0116] It should be noted that the modules mentioned in the various device embodiments of the present invention are all logical modules. Physically, a logical module can be a physical module, a part of a physical module, or a combination of multiple physical modules. The physical implementation of these logical modules themselves is not the most important factor; rather, the combination of functions implemented by these logical modules is the key to solving the technical problem proposed by the present invention. Furthermore, to highlight the innovative aspects of the present invention, the above-described device embodiments have not introduced modules that are not closely related to solving the technical problem proposed by the present invention. This does not mean that the above-described device embodiments do not contain other modules.

[0117] It should be noted that those skilled in the art should understand that the functions of each module shown in the embodiments of the above-described devices can be understood with reference to the relevant descriptions of the corresponding methods. The functions of each module shown in the embodiments of the above-described devices can be implemented by a program (executable instructions) running on a processor, or by specific logic circuits. If the above-described devices in the embodiments of this specification are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this specification, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods described in the various embodiments of this specification. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), magnetic disks, or optical disks. Thus, the embodiments of this specification are not limited to any specific hardware and software combination.

[0118] It should be noted that in this patent application, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one" does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element. In this patent application, if it refers to performing an action according to an element, it means performing the action at least according to that element, including two cases: performing the action only according to that element, and performing the action according to that element and other elements. Expressions such as "multiple," "repeatedly," and "various" include two, two times, two kinds, and more than two, more than two times, and more than two kinds.

[0119] All documents mentioned in this application are considered to be incorporated in their entirety into the disclosure of this application so that they can serve as a basis for modifications if necessary. Furthermore, it should be understood that after reading the foregoing disclosure of this application, those skilled in the art can make various alterations or modifications to this application, and these equivalent forms also fall within the scope of protection claimed in this application.

Claims

1. A method for confirming GNSS integer ambiguity, characterized in that, Includes the following steps: A double-difference observation equation is constructed based on the original observations, and the double-difference observation equation is solved to obtain a floating-point solution; Based on the floating-point solution, a fuzziness search is performed to obtain the optimal fuzziness and the second-best fuzziness. The corresponding optimal fixed solution and suboptimal fixed solution are obtained by using the optimal fuzziness and suboptimal fuzziness respectively; The double-difference ambiguity of the carrier wave is calculated using the optimal fixed solution and the suboptimal fixed solution respectively. Based on the comparison of the fractional parts of the double-difference ambiguity, it is confirmed whether the optimal fixed solution is a correct fixed solution.

2. The method according to claim 1, characterized in that, In the step of performing fuzzy search based on the floating-point solution, the LAMBDA algorithm is used for fuzzy search.

3. The method according to claim 1, characterized in that, The original observations come from multiple satellite systems. The step of constructing the double-difference observation equation based on the original observations includes the following sub-steps: Reference satellites of the various satellite systems are selected respectively, and corresponding double-difference observation equations are constructed. The step of inversely calculating the double-difference ambiguity of the carrier wave using the optimal fixed solution and the suboptimal fixed solution respectively includes the following sub-steps: The double-difference ambiguity of the carrier wave for all satellite systems is calculated by using the optimal and suboptimal fixed solutions for all satellite systems.

4. The method according to claim 1 or 3, characterized in that, The step of confirming whether the optimal fixed solution is a correct fixed solution by comparing the fractional parts of the double-difference ambiguity includes the following sub-steps: Extract the fractional part of the carrier double-difference ambiguity calculated from the optimal fixed solution and the suboptimal fixed solution; Calculate the average and variance of the fractional parts of the double-difference ambiguities calculated from the optimal fixed solution and the suboptimal fixed solution, respectively; By comparing the average and variance of the decimal parts of the double-difference ambiguities calculated from the optimal fixed solution and the suboptimal fixed solution, it can be determined whether the ambiguity fixation is correct.

5. The method according to claim 4, characterized in that, The step of comparing the average and variance of the fractional parts of the double-difference ambiguities calculated from the optimal fixed solution and the suboptimal fixed solution to determine whether the ambiguity fixation is correct includes the following sub-steps: Determine whether the average value and variance of the fractional part of the double-difference ambiguity calculated by the optimal fixed solution are both greater than the average value and variance of the fractional part of the double-difference ambiguity calculated by the suboptimal fixed solution. If yes, then the ambiguity is fixed incorrectly; if no, then the ambiguity is fixed correctly.

6. A GNSS integer ambiguity verification device, characterized in that, include: The double-difference observation equation construction module is used to construct double-difference observation equations based on the original observations and to solve the double-difference observation equations to obtain floating-point solutions. The fuzziness search module is used to perform fuzziness search based on the floating-point solution to obtain the optimal fuzziness and the second-best fuzziness. The fixed solution calculation module is used to calculate the corresponding optimal fixed solution and suboptimal fixed solution using the optimal fuzziness and suboptimal fuzziness, respectively. The inverse calculation module is used to inversely calculate the double-difference ambiguity of the carrier using the optimal fixed solution and the suboptimal fixed solution respectively; The confirmation module is used to confirm whether the optimal fixed solution is a correct fixed solution based on the comparison of the decimal parts of the double-difference ambiguity.

7. The apparatus according to claim 6, characterized in that, The fuzziness search module uses the LAMBDA algorithm for fuzziness search.

8. The apparatus according to claim 6, characterized in that, The original observations came from multiple satellite systems. The double-difference observation equation construction module is used to select reference satellites of the multiple satellite systems respectively and construct corresponding double-difference observation equations. The inverse calculation module is used to inversely calculate the double-difference ambiguity of the carrier wave for all satellite systems using the optimal and suboptimal fixed solutions for all satellite systems respectively.

9. The apparatus according to claim 6 or 8, characterized in that, The confirmation module includes the following sub-modules: The truncation submodule is used to truncate the fractional part of the double-difference ambiguity of the carrier calculated from the optimal fixed solution and the suboptimal fixed solution. The calculation submodule is used to calculate the average and variance of the fractional parts of the double-difference ambiguities calculated from the optimal fixed solution and the suboptimal fixed solution, respectively. The comparison submodule is used to compare the average and variance of the fractional parts of the double-difference ambiguities calculated by the optimal fixed solution and the suboptimal fixed solution, thereby determining whether the ambiguity fixation is correct.

10. The apparatus according to claim 9, characterized in that, The comparison submodule includes the following submodules: The judgment submodule is used to determine whether the average value and variance of the fractional part of the double-difference ambiguity calculated by the optimal fixed solution are both greater than the average value and variance of the fractional part of the double-difference ambiguity calculated by the suboptimal fixed solution. A determination submodule is used to determine that the ambiguity fixation is incorrect when the determination submodule determines it to be correct, and to determine that the ambiguity fixation is correct when the determination submodule determines it to be incorrect.

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