A method for ambiguity resolution of RTK combined with INS positioning

Abstract

The application provides a RTK and INS combined positioning ambiguity solution method, comprising: obtaining a baseline vector float solution and its variance-covariance matrix, a float ambiguity and its variance-covariance matrix, and a mutual covariance matrix between the float ambiguity and the baseline vector float solution of a RTK and INS combined positioning system; determining integer ambiguity and its weight by using the float ambiguity and its variance-covariance matrix, and determining a predetermined number of integer ambiguity candidate solutions according to the weight; solving the residual error of the receiver position coordinate and the receiver position coordinate predicted by the INS by using the screened ambiguity candidate value, and updating the weight of the integer ambiguity candidate solution by using the residual error; weighting and fusing the predetermined number of integer candidate solutions obtained by using the updated ambiguity candidate value weight to obtain an ambiguity estimation value and a baseline vector estimation value; and determining a final solution according to the ambiguity estimation value and the baseline vector estimation value.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of RTK (Real Time Kinematic) and INS (Inertial Navigation System) combined positioning technology, and particularly relates to ambiguity resolution of RTK and INS combined positioning. BACKGROUND

[0002] To improve the robustness of RTK and INS combined positioning algorithm and realize high-precision positioning in complex observation environment, accurate estimation of integer ambiguity is a core link. RTK technology can effectively eliminate the influence of ionospheric delay, atmospheric delay and other errors by means of double difference observation model, but the float solution of ambiguity is easily disturbed by measurement noise and there is strong correlation between parameters, which increases the difficulty of integer fixing, affected by observation environment (such as shielding, multipath effect, etc.); INS can provide continuous navigation information, but the error accumulates with time and needs to be fused with GNSS observation data to suppress drift.

[0003] The present application relates to the field of RTK (Real Time Kinematic) and INS (Inertial Navigation System) combined positioning technology, and particularly relates to ambiguity resolution of RTK and INS combined positioning.

[0004] Therefore, there is a need for an ambiguity estimation method for RTK and INS combined positioning to improve the precision and reliability of ambiguity resolution in RTK and INS combined positioning.

[0005] The technology described in the background section is only a brief description of the background of the present application by the inventor, for the convenience of understanding the present application, and should not be considered as the technology and shortcomings known to those skilled in the art. The present application does not aim to solve all the above technical problems. SUMMARY

[0006] The present application is made in view of the above problems to solve one or more defects in the prior art and at least provide a beneficial alternative.

[0007] According to one aspect of the present application, there is provided a method for ambiguity resolution of RTK and INS integrated positioning, comprising obtaining a baseline vector float solution and its variance-covariance matrix, a float ambiguity and its variance-covariance matrix, and a cross-covariance matrix between the float ambiguity and the baseline vector float solution of an RTK and INS integrated positioning system; determining integer ambiguities and their weights using the float ambiguity and its variance-covariance matrix, and determining a predetermined number of integer ambiguity candidate solutions according to the weights; calculating a residual between a receiver position coordinate and a predicted receiver position coordinate of the INS using the screened ambiguity candidate values, and updating the weights of the integer ambiguity candidate solutions using the residual; performing weighted fusion on the predetermined number of integer candidate solutions obtained using the updated ambiguity candidate value weights to obtain ambiguity estimates and baseline vector estimates; and determining a final solution according to the ambiguity estimates and the baseline vector estimates.

[0008] According to one embodiment of the present application, the influence of outliers on ambiguity estimation can be reduced, thereby significantly improving the accuracy of ambiguity estimation and further improving the positioning accuracy of the integrated navigation system.

[0009] According to one embodiment of the present application, false ambiguity candidate solutions can be effectively excluded, thereby providing effective protection for the improvement of positioning accuracy and the enhancement of scheme robustness.

[0010] According to one embodiment of the present application, the calculation efficiency can be optimized.

[0011] Some embodiments of the present application can effectively weaken the influences of RTK-related and INS-related errors such as ionospheric residual, tropospheric residual, receiver clock error, receiver clock drift, etc., improve the accuracy of float solution estimation, overcome or alleviate the limitations of traditional ILS algorithms that fix ambiguities to integer values and cannot separate part of the residuals, improve the positioning accuracy in complex observation environments, and well improve the adaptability of RTK and INS integrated positioning.

[0012] Embodiments of the present application are applicable to complex observation environments such as urban canyons, can meet the demand for high-precision and high-reliability navigation and positioning in such environments, and can be applied to high-precision navigation and positioning scenarios such as surveying and mapping engineering, aerospace, and intelligent driving.

[0013] The above description of the advantages of the present application does not mean that the technical solutions of the independent claims have all the above advantages at the same time. BRIEF DESCRIPTION OF DRAWINGS

[0014] The present application can be better understood in conjunction with the accompanying drawings. The drawings are only illustrative and not limiting to the scope of protection of the present application.

[0015] Figure 1is a schematic flow chart of the ambiguity resolution method for RTK and INS integrated positioning according to an embodiment of the present application.

[0016] Figure 2 is a schematic diagram showing the method of obtaining baseline vector float solution ( ) and its variance-covariance matrix ( ), float ambiguity ( ) and its variance-covariance matrix ( ), and the cross-covariance matrix between float ambiguity and baseline vector float solution ( and ) according to an embodiment of the present application.

[0017] Figure 3 is a schematic diagram showing the method of obtaining ambiguity candidate solutions according to an embodiment of the present application. DETAILED DESCRIPTION

[0018] The specific embodiments of the present application will be described hereinafter with reference to the accompanying drawings, which are given by way of example only and thus are not restrictive of the present application. The description is not intended to limit the scope of the application, as such can include any variations falling within the scope of the application. The description also does not describe all possible combinations of features that can be claimed in the claims.

[0019] The specific embodiments of the present application will be described hereinafter with reference to the accompanying drawings, which are given by way of example only and thus are not restrictive of the present application. The description is not intended to limit the scope of the application, as such can include any variations falling within the scope of the application. The description also does not describe all possible combinations of features that can be claimed in the claims.

[0020] Figure 1 is a schematic flow chart of the ambiguity resolution method for RTK and INS integrated positioning according to an embodiment of the present application.

[0021] As shown in Figure 1 , first, in step S101, baseline vector float solution ( ) and its variance-covariance matrix ( ), float ambiguity ( ) and its variance-covariance matrix ( ), and the cross-covariance matrix between float ambiguity and baseline vector float solution ( and ) of the RTK and INS integrated positioning system are obtained.

[0022] The baseline vector float solution, float ambiguity, and its variance-covariance matrix, cross-covariance matrix of the RTK and INS integrated positioning system can be obtained by various methods of the prior art, which will not be described here.

[0023] Figure 2 A diagram showing the baseline vector float solution , its variance-covariance matrix , float ambiguity , its variance-covariance matrix , and the cross-covariance matrix between the float ambiguity and the baseline vector float solution and obtained in step S101 according to an embodiment of the present application is shown.

[0024] As shown in Figure 2 , in step S201, the observation values of the RTK and INS integrated positioning system are obtained. For example, in a city canyon environment, the observation values of the RTK and INS integrated positioning system are first obtained by using a receiver. According to an embodiment, the observation values include GNSS pseudorange observation values, carrier phase observation values, ephemeris files, and state quantity measurement values of the INS.

[0025] Then, in step S202, the measurement innovation values at the current time (set as the k time) are calculated according to the obtained GNSS pseudorange observation values and the state quantity measurement values of the INS, and the state prediction values at the current time are calculated at the same time.

[0026] According to an embodiment, the state vector at the current time is defined as follows:

[0027] (1)

[0028] wherein:

[0029] is the INS error state, including attitude error , velocity error , position error , accelerometer bias , and gyroscope bias , wherein n represents the value in the n system (i.e., the navigation coordinate system), and b represents the value in the b system (i.e., the carrier coordinate system).

[0030] is the single-difference ambiguity vector, represents the distance from the reference station to the rover station , and the superscript denotes the satellite system (G: GPS, R: GLONASS, E: GALILEO, C: BDS, J: QZSS, I: IRNSS), denotes the frequency (e.g. GPS: L1, L2, L5, GLONASS: L1, L2, GALILEO: E1, E5a, E5b, E6, BDS: B1, B2, B3, QZSS: L1, L2, L5, IRNSS: L5, S).

[0031] According to an embodiment, the measurement innovation value is calculated as follows:

[0032] (2)

[0033] wherein:

[0034] denotes the double-difference operator;

[0035] denotes the first satellite and the second satellite, respectively;

[0036] is the double-difference carrier phase;

[0037] is the double-difference pseudorange;

[0038] is the double-difference geometric range predicted by the INS;

[0039] and denote the single-difference ambiguities of the first satellite and the second satellite, respectively.

[0040] According to an embodiment, the state prediction value at the current time instant is determined as follows:

[0041] (3)

[0042] wherein: is the state transition matrix, is the process noise, is the state estimate at the previous time instant (i.e. at time instant k-1).

[0043] As the skilled person will readily understand, when the time instant k = 1, the state estimate at the first time instant has to be obtained by setting the initial a priori estimate . The state estimate at the first time instant can be obtained by means of a Kalman filter As the filtering process iterates, the state estimates at times 2, 3, 4, ..., k-2, k-1 are obtained sequentially. This is the state estimate at time k-1, and it is used in the calculation of the state estimate at time k.

[0044] Next, in step S203, the prior covariance is calculated. According to one implementation, the prior covariance is calculated as follows. :

[0045] (4)

[0046] in: It is the posterior covariance estimate of the previous time step, i.e., time step k-1. for The transpose of a matrix. For process noise The variance matrix; Similar to the state estimate, it is calculated through Kalman filtering iterations from the previous time step (time step k-2) and participates in the posterior covariance matrix of the next time step (time step k). The calculation.

[0047] Next, in step S204, the measurement prediction value is obtained based on the state prediction value. According to one embodiment, this can be performed as follows:

[0048] (5)

[0049] in: For the observation matrix, To observe noise.

[0050] Then, calculate the Kalman gain. :

[0051] (6)

[0052] in: To measure noise The variance matrix.

[0053] Then, in step S205, based on the residual between the measured predicted value and the measured innovative value (i.e. ), to obtain state estimates And based on the prior covariance at the current moment Calculate the posterior covariance at the current time. .

[0054] According to one implementation, the following is based on the Kalman gain. and the residual of the innovation value to calculate the state estimation value at the current time :

[0055] (7)

[0056] According to an embodiment, the posterior covariance at the current time can be calculated as follows according to the prior covariance at the current time : :

[0057] (8)

[0058] By statistical correlation and error propagation characteristics between the state estimation values at the current time can be obtained as the basis for subsequent calculation of the variance-covariance matrix of the baseline vector float solution at the current time , the variance-covariance matrix of the float ambiguity and the cross-covariance matrix between them and .

[0059] Finally, in step S206, the baseline vector at the current time is corrected using the state estimation value and the corresponding variance-covariance matrix, and the baseline vector float solution at the current time, its variance-covariance matrix , the float ambiguity and its variance-covariance matrix , the cross-covariance matrix between the float ambiguity and the baseline vector float solution and (their summary is referred to as the required parameters) are obtained.

[0060] This step can be performed in various ways known to those skilled in the art.

[0061] This method can model the state quantity as a random walk model by directly estimating the INS-related and GNSS-related errors, thereby reducing the calculation difficulty of the state prediction value.

[0062] Returning to Figure 1 , in step S102, the ambiguity and its variance-covariance matrix are subjected to ambiguity decorrelation processing to obtain a set of decorrelated float ambiguities.

[0063] According to an embodiment, the LAMBDA algorithm can be used to perform integer Gaussian transformation (also referred to as Z transformation) on the ambiguity parameters and their variance-covariance matrix. This transformation is implemented through an integer transformation matrix Z, which aims to reduce the correlation between the ambiguity parameters. After transformation, the variance-covariance matrix ( the absolute values of the non-diagonal elements in the matrix are significantly reduced, i.e. the correlation between ambiguities is significantly reduced, so that the matrix is closer to diagonalization. This decorrelation processing can obtain a set of mapped floating-point ambiguities with lower correlation , and further improve the efficiency of ambiguity search.

[0064] The LAMBDA algorithm is used for ambiguity decorrelation and candidate solution search, which effectively reduces the search space, reduces the computational complexity, improves the running efficiency of the algorithm, and is more suitable for navigation application scenarios with high real-time requirements.

[0065] Then, in step S103, a predetermined number (in this paper, set to t) of ambiguity candidate solutions are obtained from the decorrelated floating-point ambiguity set and the variance-covariance matrix.

[0066] Figure 3 A schematic diagram of a method for obtaining ambiguity candidate solutions according to an embodiment of the application is shown.

[0067] According to an embodiment, the ambiguity search space after decorrelation processing is first obtained in step S301.

[0068] According to an embodiment, first, the floating-point ambiguity vector after decorrelation processing and the corresponding variance-covariance matrix The region containing the true solution of the integer ambiguity vector after decorrelation processing is defined by the chi-square distribution: where a is the preset confidence level (such as 99.7%), and n is the dimension of the ambiguity.

[0069] Then, the true solution of the integer ambiguity vector after decorrelation processing is determined according to the single-dimensional variance: where k is the coefficient corresponding to the confidence level (such as 3 for 99.7%), and and are the floor and ceiling functions, respectively, so as to obtain the ambiguity search space after decorrelation processing.

[0070] It should be noted that obtaining the ambiguity search space after decorrelation processing is optional, not mandatory, but obtaining the ambiguity search space after decorrelation processing can improve the efficiency of the search.

[0071] Then, in step S302, the integer ambiguity that meets the predetermined condition is determined in the ambiguity search space. According to an embodiment, the integer value in the ambiguity search space after decorrelation processing is obtained in sequence by sequential conditional least squares search, and the inequality The set of integer ambiguity vectors after decorrelation processing. Through inverse transformation... For each integer ambiguity vector obtained after downcorrelation processing ( The transformation is performed to obtain the integer fuzzyness vector that participates in the screening. ).

[0072] Then in step S303, according to the quadratic residual form of each integer ambiguity vector and the floating-point ambiguity vector ( This means that the weighted distance between the integer ambiguity vector and the floating-point ambiguity vector is used to arrange each integer ambiguity vector from smallest to largest.

[0073] Then, in step S304, the weights of each integer ambiguity vector participating in OIA screening are calculated sequentially using the weight calculation function.

[0074] The weight calculation function is:

[0075] (9)

[0076] in:

[0077] For the first The weights corresponding to the integer ambiguity vectors;

[0078] Let be the i-th integer ambiguity vector in the original solution space;

[0079] is the Laplace distribution scaling factor.

[0080] It is a weighted norm. It is the quadratic residual of the floating-point ambiguity and the i-th integer ambiguity vector.

[0081] Finally, in step S305, a predetermined number of integer ambiguities that meet the conditions are selected as ambiguity candidate solutions based on the weights.

[0082] According to one implementation, based on the weights, the weight percentage function value corresponding to each integer ambiguity is determined until the weight percentage function value of the (t+1)th integer ambiguity vector is found to be less than a threshold. Then, the first t integer ambiguity vectors that meet the requirements are retained to form a candidate ambiguity solution. Here, t is a positive integer.

[0083] The weighting percentage function value is:

[0084] (10)

[0085] The screening stop condition is:

[0086] (11)

[0087] wherein: is the weight accumulation threshold of OIA test.

[0088] When the latest candidate solution weight proportion is stopped, and the first t candidate solutions are obtained.

[0089] According to the embodiments of the present application, the OIA test and the reasonable candidate solution screening strategy are adopted, which can effectively exclude the false ambiguity candidate solutions, thereby controlling the precision of the ambiguity candidate values participating in the calculation process, and providing effective guarantee for the improvement of positioning precision and the enhancement of scheme robustness.

[0090] Then, in step S104, the receiver position coordinates are calculated by using the ambiguity candidate solutions, and the residual between the receiver position coordinates and the INS predicted receiver position coordinates is calculated , and the residual is used to update the weight of the integer ambiguity candidate solutions in step S103.

[0091] According to an embodiment, the residual is calculated as follows:

[0092] (12)

[0093] wherein:

[0094] is the fixed solution of the receiver coordinates calculated by substituting the i th candidate combination into the constructed double-difference observation equation;

[0095] is the INS predicted receiver position coordinate result;

[0096] According to an embodiment, the weight of the ambiguity candidate solution is updated as follows:

[0097] First, the value of the INS influence factor is obtained according to the residual , and then the value of the INS influence factor is used to update the weight of the ambiguity candidate value.

[0098] (13)

[0099] (14)

[0100] wherein, These parameters are selected empirically and are used to adjust the range of influence of the INS pre-integration results on the updated weights. According to one implementation, they can be obtained by fitting historical data. According to another implementation, they can be simply set to a predetermined number, which can be a number between 0.95 and 1.05. It is a weighted norm. It is the quadratic residual of the floating-point ambiguity and the i-th integer ambiguity vector.

[0101] According to an embodiment of the present invention, by adding the INS influence factor to the weight factor calculation formula based on the Laplace distribution and re-deriving its partial derivative with respect to the floating-point solution of ambiguity, it is possible to better handle noisy data with heavy-tailed characteristics, reduce the influence of outliers on ambiguity estimation, thereby significantly improving the accuracy of ambiguity estimation and thus enhancing the positioning accuracy of the integrated navigation system.

[0102] Then, in step S105, the first t integer candidate solutions are weighted and fused using the updated weights of the integer ambiguity candidate solutions to obtain the ambiguity estimate. ) and baseline vector estimates ( ).

[0103] According to one implementation, weighted fusion is performed as follows to obtain the ambiguity estimate ( ) and baseline vector estimates ( ):

[0104] (1) Ambiguity estimate

[0105] (15)

[0106] (2) Baseline vector estimate

[0107] (16)

[0108] Finally, in step S106, the final solution is determined based on the ambiguity estimate and the baseline vector estimate.

[0109] According to one implementation, the trace of the variance-covariance matrix of the ambiguity estimate and the baseline vector estimate is used... ) and the trace of the variance-covariance matrix of the baseline vector floating-point solution ( Compare them, in Greater than When, the baseline vector estimate ( If the baseline vector is not found, the floating-point solution will be used as the final solution; otherwise, the baseline vector will be used as the floating-point solution. () as the final solution.

[0110] According to one implementation, the variance-covariance matrix of the baseline vector estimate is first calculated. ). According to an embodiment, the variance-covariance matrix of the baseline vector estimate is determined as follows:

[0111] (17)

[0112] wherein:

[0113] (18)

[0114] wherein, is a unit diagonal matrix;

[0115] (19)

[0116] is a weighted norm.

[0117] Then, the trace of the variance-covariance matrix of the baseline vector estimate (V) and the variance-covariance matrix of the baseline vector float solution (Vf) are compared:

[0118] the baseline vector estimate is output when the baseline vector float solution is output when

[0119] With the embodiments of the present application, the partial derivatives of the ambiguity float solution are ingeniously utilized, the noise data with heavy-tailed characteristics can be better processed, the influence of outliers on ambiguity estimation is reduced, the precision of ambiguity estimation is significantly improved, and the positioning precision of the integrated navigation system is further improved.

[0120] The method of the present application can be applied to the Beidou satellite navigation system.

[0121] The method of the present application improves the precision and reliability of ambiguity estimation in the integrated navigation system by optimizing the decorrelation, candidate solution screening and fusion process of ambiguity, and can be applied to high-precision navigation and positioning scenarios such as surveying and mapping engineering, aerospace, intelligent driving and other fields.

[0122] The numbering of the method steps of the present application is only for the convenience of description, and is not a provision or description of the execution order. Those skilled in the art should understand that the order of these steps or the parallel execution of some steps can be adjusted according to the actual situation.

[0123] ​​The various units of the present application can be implemented by hardware, or by software cooperating with hardware, or by a processor executing software stored in a memory. The foregoing detailed description of the application has been presented for purposes of clarity and description. It is not intended to limit the scope of the application to the precise form described. It will be apparent to persons skilled in the art that many modification, variation, and alternatives to the exact device or method described herein can be practiced that are within the scope of the application. The described embodiments were chosen and described in order to best explain the principles of the application and its best mode of practice. The foregoing detailed description has set forth various embodiments of the application via the use of specific terminology. However, embodiments of the application are not necessarily limited to those described, but can be practiced with the entire scope of equivalents known to those of skill in the art.

Claims

1. A method for ambiguity resolution for RTK and INS integrated positioning, characterized in that, The method comprises the following steps: obtaining a baseline vector float solution of an RTK and INS combined positioning system and its variance-covariance matrix, a float ambiguity and its variance-covariance matrix, and a mutual covariance matrix between the float ambiguity and the baseline vector float solution; determining integer ambiguity and its weight by using the float ambiguity and its variance-covariance matrix, and determining a predetermined number of integer ambiguity candidate solutions according to the weight; calculating the residual of the receiver position coordinate and the predicted receiver position coordinate of the INS by using the screened ambiguity candidate value, and updating the weight of the integer ambiguity candidate solution by using the residual; performing weighted fusion on the obtained predetermined number of integer candidate solutions by using the updated ambiguity candidate value weight to obtain ambiguity estimation value and baseline vector estimation value; and determining the final solution according to the ambiguity estimation value and the baseline vector estimation value.

2. The method of claim 1, wherein, The determination of the integer ambiguity and its weight by using the float ambiguity and its variance-covariance matrix comprises ambiguity decorrelation processing on the float ambiguity and its variance-covariance matrix to obtain a set of decorrelated float ambiguities, and the determination of the integer ambiguity and its weight by using the decorrelated float ambiguities, wherein the LAMBDA algorithm is used to perform integer Gaussian transformation on the ambiguity parameters and their variance-covariance matrix to reduce the correlation between the ambiguity parameters and obtain the set of decorrelated float ambiguities.

3. The method of claim 1, wherein, The baseline vector float solution of the RTK and INS combined positioning system and its variance-covariance matrix, the float ambiguity and its variance-covariance matrix, and the mutual covariance matrix between the float ambiguity and the baseline vector float solution are obtained as follows: obtaining the observation values of the RTK and INS combined positioning system; obtaining the measurement innovation value at the current time and the state prediction value by using the observation values; calculating the prior covariance of the state prediction value at the current time; obtaining the measurement prediction value according to the state prediction value; obtaining the state estimation value according to the residual of the measurement prediction value and the measurement innovation value, and calculating the posterior covariance of the state estimation value at the current time according to the prior covariance at the current time; correcting the state quantity estimation value at the current time and the corresponding variance-covariance matrix to obtain the baseline vector float solution of the RTK and INS combined positioning system and its variance-covariance matrix, the float ambiguity and its variance-covariance matrix, and the mutual covariance matrix between the float ambiguity and the baseline vector float solution.

4. The method of claim 2, wherein, The determination of the integer ambiguity and its weight by using the decorrelated float ambiguity and the determination of a predetermined number of integer ambiguity candidate solutions according to the weight are performed as follows: obtaining the ambiguity search space after the decorrelation processing; determining the integer ambiguities participating in the screening in the ambiguity search space according to the predetermined condition; calculating the weight corresponding to each integer ambiguity; and determining the weight proportion function value corresponding to each integer ambiguity according to the weight, and screening out a predetermined number of integer ambiguities meeting the condition as the ambiguity candidate solutions.

5. The method of claim 4, wherein, The weight is determined as follows: （9） wherein: For the first integer ambiguity vector corresponds to the weight; is a floating ambiguity; is a th integer ambiguity vector in the original space; i is a th integer ambiguity vector in the original space; is the scale factor for the Laplace distribution; For n integer ambiguity vector set; represents a weighted norm, is a residual quadratic form of the floating-point ambiguities with the i-th integer ambiguity vector, represents a residual quadratic form of the floating-point ambiguities with the z-th integer ambiguity vector.

6. The method of claim 5, wherein, The screening out of the predetermined number of integer ambiguities meeting the condition as the ambiguity candidate solutions according to the weight is performed as follows: sorting the integer ambiguities participating in the screening from small to large; According to the weight, a weight proportion function value corresponding to each integer ambiguity participating in screening is determined; The searching is sequentially performed until a weight proportion function value of a (t+1)th integer ambiguity vector is less than a threshold value, and the first t integer ambiguity vectors are combined to form an ambiguity candidate solution, where t is a positive integer.

7. The method of claim 6, wherein, The weight proportion function value is determined as follows: （10） where j is used to identify the jth integer ambiguity vector, is the weight corresponding to the jth integer ambiguity vector. i is an index variable in summation operation, used to traverse integer ambiguity vectors from 1 to j to calculate the sum of weights corresponding to the vectors; wherein the residual is calculated as follows : （12） Wherein: Substitute the fixed solution of receiver coordinates calculated for the ith candidate combination into the constructed double-difference observation equations; The receiver position coordinates result predicted for the INS.

8. The method of claim 7, wherein, The weight of the ambiguity candidate solution is updated as follows: According to the residual The INS impact factor is obtained as follows value of the INS impact factor, （13） The INS influence factor is then used to update the ambiguity candidate weight values as follows ​ （14） wherein is a predetermined parameter; is a vector space of integer ambiguity vectors, is a weighted norm, is a residual quadratic form of the floating-point ambiguity and the i-th integer ambiguity vector.

9. The method according to claim 8, characterized in that, The weighted fusion is performed as follows to obtain the ambiguity estimate and the baseline vector estimate : (1) Ambiguity estimation value （15） (2) Baseline vector estimation value （16） wherein, is the baseline vector float solution, denotes the baseline vector float solution the cross-covariance matrix of the float ambiguities , and denotes the variance-covariance matrix of the float ambiguities .

10. The method of claim 1, wherein, The final solution is determined according to the ambiguity estimation value and the baseline vector estimation value as follows: computing the trace of the variance-covariance matrix, i.e. ; Compute the trace of the baseline vector float solution variance-covariance matrix, i.e. ; In greater than then the baseline vector estimate is taken as the final solution, otherwise the baseline vector floating point solution is taken as the final solution.

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