A partial ambiguity resolution method suitable for complex dynamic environments

Abstract

A partial ambiguity resolution method suitable for complex dynamic environment belongs to the field of high-precision dynamic satellite navigation relative positioning, and the implementation method comprises the following steps: constructing a single epoch carrier phase double difference differential observation equation; calculating the weight factor corresponding to each satellite through standardized residual and IGG3 method; bringing the weight factor into the double difference factor matrix to obtain the modified observation value; solving the updated observation equation through weighted iterative least square method; fixing the partial ambiguity of the modified ambiguity float solution to obtain the integer ambiguity fixed solution; performing Ratio-based posterior consistency traversal detection; monitoring the number of satellites participating in the solution in the traversal search process; if the number of satellites is less than the threshold, the float solution is output; if the number of satellites is greater than or equal to the threshold after traversal search, the integer ambiguity fixed solution is brought into the updated observation equation to obtain the fixed solution of the three-dimensional relative position vector and its covariance matrix.

Description

Technical Field

[0001] This invention belongs to the field of high-precision dynamic satellite navigation relative positioning technology, and relates to a carrier phase differential partial ambiguity resolution method suitable for complex dynamic environments. Background Technology

[0002] To address the navigation challenges during autonomous landing of rockets and the high-precision measurement of trajectory and velocity of test vehicles in the aerospace telemetry and control field, both domestically and internationally, a combination of multiple navigation devices is currently employed. Whether it's official institutions or private commercial rocket recovery, both primarily utilize a combination of satellite navigation and other navigation modes such as inertial navigation. Satellite navigation generally employs high-precision carrier phase differential positioning to improve navigation and positioning accuracy. Among these, satellite navigation dominates due to its global coverage, applicability to various weather conditions, and high-precision positioning effects. Therefore, research on carrier phase differential positioning has significant practical implications for the landing navigation phase.

[0003] With the increasing demand for high-precision navigation and positioning, real-time kinematic positioning has gradually become a research hotspot in recent years. While RTK positioning can achieve millimeter-level static positioning accuracy, the success rate of fuzzy positioning has always been a major challenge. In fuzzy positioning solutions for RTK, partial fuzzy resolution (PAR) has been widely applied.

[0004] In the highly dynamic and complex environment of rocket recovery, especially sea-based rocket recovery, the satellite observations received by the receiver are not as ideal as the static data in an open environment. There are certain gross differences in the observations from different satellites, which may affect the resolution of ambiguities to some extent. Summary of the Invention

[0005] To address the challenges of fixing ambiguities and the impact of gross errors in observations under complex dynamic environments, this invention aims to provide a partial ambiguity resolution method suitable for such environments. The improved partial ambiguity algorithm employs triple constraints—ambiguity fixation success rate, FFRT detection, and Ratio detection—and eliminates ambiguities in descending order of the conditional variance matrix to obtain a subset of ambiguities that are easier to fix under these constraints. This invention achieves partial ambiguity-fixed carrier phase relative positioning based on residual correction, reducing the impact of gross errors in carrier phase observations and achieving higher positioning accuracy and positioning ratio than real-time differential RTD and full ambiguity resolution FAR.

[0006] The objective of this invention is achieved through the following technical solution:

[0007] This invention discloses a method for resolving partial ambiguities applicable to complex dynamic environments, comprising the following steps:

[0008] Step S11: Using a satellite antenna and a satellite navigation receiver, receive satellite signals from the mobile station and the base station respectively. Calculate the satellite position and the receiver's line-of-sight vector using the observation data and ephemeris data of the two satellite signals, and construct a single-epoch carrier phase double-difference observation equation.

[0009] Step S12: Correct the a priori residuals of the observations from S11: Calculate the weight factor for each satellite using standardized residuals and the IGG3 method, and substitute the weight factor into the double-difference factor matrix to obtain the corrected observations. Update the observation equations.

[0010] Step S13: Solve the observation equation updated in S12 using the weighted iterative least squares method to obtain the corrected floating-point solution and variance matrix.

[0011] Step S14: Fix part of the ambiguity of the corrected floating-point ambiguity solution in step S13 to obtain the integer ambiguity fixed solution.

[0012] Step S15: Perform a ratio-based posterior consistency traversal test. During the traversal search, monitor the number of satellites participating in the solution. If the number of satellites is less than a threshold, output a floating-point solution. If the number of satellites after the traversal search is greater than or equal to the threshold, proceed to the next step.

[0013] Step S16: Substitute the fixed solution of integer ambiguity into the updated observation equation to obtain the fixed solution of the three-dimensional relative position vector and its covariance matrix, thus realizing the resolution of ambiguity in the carrier phase differential part.

[0014] It also includes step S17, which involves calculating the ambiguity of the carrier phase differential part based on the result obtained in step S16, and then eliminating it in descending order of the conditional variance matrix to obtain a subset of ambiguities that are easier to fix under constraints. This significantly improves the efficiency of fixing integer ambiguities and is more suitable for navigation scenarios during the autonomous landing phase of rocket recovery.

[0015] The observation equation obtained in step S11 is composed of pseudorange observations and carrier phase observations within one epoch, with a weighting of 1:10000. The single-epoch carrier phase double-difference observation equation is specifically expressed as follows:

[0016]

[0017] D g It is a double difference operation matrix. For single-difference carrier phase observations, λ g Let g represent the carrier wavelength of constellation g, and b be the relative position vector. For single-difference integer ambiguity, This represents the tropospheric error in the double-difference carrier phase observations. Measurement noise for double-difference code observations, This is for current layer error.

[0018] The specific implementation method of step S13 is as follows:

[0019] The updated observation equations are simplified to a matrix expression solved using the least squares method:

[0020] y=A(Δb ur )+BN+ε

[0021] Where y is the double-difference carrier phase and pseudo-range measurement vector given by the receiver, A and B are constant coefficient matrices, and N is the double-difference full-period ambiguity vector. Δb ur For unknown baseline vectors or baseline vector correction values.

[0022] Based on the least squares principle, the least squares solution is obtained:

[0023]

[0024] Q x =([AB]) T C[AB]) -1

[0025] Where C = Q -1 Q is defined as the weight matrix. The weight matrix Q is determined using measurement noise. Since the original observations of the satellite carrier phase are independent, uniformly distributed random variables, the variance of the carrier phase observations is: The variance of the pseudo-distance observations is:

[0026]

[0027] Where f 1n =f1 + fn, where f is the carrier phase weight and p is the pseudorange weight. A joint weighting model of elevation angle and carrier-to-noise ratio is adopted, taking into account both the carrier-to-noise ratio and the satellite elevation angle. The specific weighting model is as follows:

[0028]

[0029] Where E i Let C / N be the elevation angle of the satellite at the i-th epoch. 0,Measured The measured carrier-to-noise ratio.

[0030] Step S14 includes the following steps:

[0031] Step S142: Sort the subset of floating-point solutions for ambiguity according to the conditional variance matrix, and exclude satellites with poor signal quality by using the elevation angle and signal-to-noise ratio thresholds. The minimum number of satellites participating in the solution is n=8, and the ambiguity fixation success rate threshold is P=0.995.

[0032] Step S143: Calculate the lower limit of the ambiguity fixed success rate of the ambiguity floating-point solution subset using the sequential rounding method, and compare it with the preset threshold. If the success rate is greater than the threshold, proceed to the next step; otherwise, remove the last element in the ambiguity floating-point solution subset. If the number of satellites does not meet the requirements after removing the element, output the floating-point solution; otherwise, repeat this step.

[0033] Step S144: Perform FFRT lookup using the number of satellites and the ambiguity fixation success rate obtained in the previous step, and compare it with the preset threshold to perform FFRT detection. If the FFRT is greater than the threshold, proceed to the next step; otherwise, remove the last element in the ambiguity floating-point solution subset. If the number of satellites does not meet the requirements after removing the element, output the floating-point solution; otherwise, repeat this step.

[0034] Step S145: The obtained improved partial ambiguity floating-point solution subset is used to search for a fixed solution using the MLAMBDA algorithm.

[0035] Step S146: Perform a ratio decision on the optimal and suboptimal solutions obtained by the MLAMBDA algorithm. This achieves partial ambiguity fixation.

[0036] Step S15 includes the following steps:

[0037] Step S151: The elevation angle signal-to-noise ratio integrated weighting model is used to sort the data, obtaining the four optimal groups of double-difference satellites, which serve as the base subset for posterior consistency testing. The base ratio value of the base subset is then calculated.

[0038] Step S152: After obtaining the basic subset, add the remaining ambiguities in the partial ambiguity subset to the basic subset in sequence. If the Ratio value increases compared to the basic Ratio value, add this ambiguity to the basic subset to obtain a new posterior subset.

[0039] Step S153: If the dimension of the posterior subset obtained after the first traversal search is <6, output the floating-point solution. If the posterior subset is ≥6, perform the second traversal search. The second traversal search removes each ambiguity in the posterior subset in turn to form a new posterior subset. If the Ratio value of the new posterior subset is greater than the Ratio value of the previous posterior subset, remove this satellite. Continue until the dimension of the posterior subset is =6 or the traversal search is completed.

[0040] Step S154: Output the subset of ambiguities after posterior ratio consistency and fix it using the Mlambda algorithm.

[0041] In step S13, the residual correction factor correction method is used, specifically the IGG3 algorithm. Using the floating-point solution and its variance-covariance matrix, the residuals of the carrier phase double difference and pseudorange double difference for each satellite are calculated. Then, using the Danish method, the residual correction factor for each satellite is calculated using the standardized residuals. The residual correction factors are then superimposed onto the least-squares weighted matrix, and the corrected floating-point solution and variance matrix are recalculated.

[0042]

[0043] Sv i These are the standardized residuals of the pseudorange and carrier phase; k0 and k1 are two thresholds for the standardized residuals, with k0 taking a value of 1 and k1 taking a value of 3, λ i It is the weight factor of the IGG3 weight function.

[0044] The Ratio detection is a ratio test, defined as the ratio of the quadratic residual of the suboptimal solution to the optimal solution when ambiguity is fixed. Essentially, it's a discrimination test to determine the distinguishability between the optimal and suboptimal solutions when ambiguity is fixed. The closer the optimal solution is to the true value, the greater the distinguishability between the suboptimal and optimal solutions. Therefore, the accuracy of the optimal solution can be estimated using the Ratio value. Typically, a threshold is manually set for the Ratio detection. When the obtained Ratio value is greater than the threshold, ambiguity is considered successfully fixed, and the fixed value is used as the final result; otherwise, the ambiguity fixation accuracy is considered poor, and a floating-point solution is used as the final result. The Ratio detection method is data-driven, and the threshold is usually set to 2-3. Specific discrimination conditions are as follows:

[0045]

[0046] in, For floating-point solutions, This is the optimal solution. This is a suboptimal solution. This is the floating-point covariance matrix.

[0047] Beneficial effects:

[0048] 1. This invention discloses a partial ambiguity resolution method applicable to complex dynamic environments. Specifically designed for the complex dynamic environment of autonomous landing of a recoverable rocket, it employs residual correction-based partial ambiguity fixed carrier phase relative positioning. The differential positioning observation equation of this invention is solved in a single epoch, offering greater dynamic adaptability. Compared to traditional full ambiguity resolution methods, this partial ambiguity resolution method exhibits better positioning accuracy and robustness even in poor signal conditions. Compared to traditional carrier phase differential positioning algorithms, this invention offers wider applicability and higher positioning accuracy in complex dynamic environments during autonomous landing, demonstrating greater versatility.

[0049] 2. This invention discloses a partial ambiguity resolution method applicable to complex dynamic environments. During the landing phase of a recoverable rocket, a single-epoch carrier phase double-difference differential observation equation is constructed for long-baseline differential positioning. The Mlambda algorithm is used to fix the carrier phase differential ambiguity. For the complex dynamic application scenario of recoverable rocket landing, the method integrates a priori residual correction and a posteriori consistency detection through ambiguity fixation success rate, FFRT detection, and Ratio detection. Based on the triple-constraint partial ambiguity resolution method, the conditional variance matrix is ​​reduced by descending order to obtain a subset of ambiguities that are easier to fix under constraints, reducing the impact of coarse errors in carrier phase observation. Compared with the traditional LAMBDA algorithm, the probability of incorrect fixation is significantly reduced. Attached Figure Description

[0050] Figure 1 The flowchart illustrates a partial ambiguity resolution method applicable to complex dynamic environments according to the present invention.

[0051] Figure 2 This is a flowchart of the MLAMBDA algorithm of the present invention.

[0052] Figure 3 This represents the geometric relationship between the satellite and the receiver. Detailed Implementation

[0053] The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0054] like Figure 1 As shown in the figure, this embodiment discloses a partial ambiguity resolution method applicable to complex dynamic environments. The specific implementation steps are as follows:

[0055] Step S11: Using a satellite antenna and a satellite navigation receiver, receive satellite signals from the mobile station and the base station respectively, and perform acquisition and tracking processing. Calculate the satellite position and receiver observation line-of-sight vector using the satellite signal observation data and ephemeris data obtained from the satellite navigation receiver, and construct a carrier phase double-difference differential model.

[0056]

[0057]

[0058] Where: λ s This represents the carrier wavelength of constellation g; The fractional part of the single-difference carrier phase observation value of the s-th satellite in constellation g; Represents the integer part of the single-difference carrier phase observation of the s-th satellite in constellation g; This represents the single-difference code observation value of the s-th satellite in constellation g; The measurement noise represents the single-difference carrier phase observation of the s-th satellite in constellation g; The measurement noise represents the single-difference code observation of the s-th satellite in constellation g; dt represents the single difference between the station distances of antennas u and r with respect to the s-th satellite in constellation g; u dt represents the clock bias of the receiver to which antenna u belongs; r represents the clock bias of the receiver to which antenna r belongs; c represents the speed of light.

[0059] Step S12: Solve the observation equation using the weighted iterative least squares method: The observation equation for a single-epoch carrier phase double difference can be expressed as:

[0060]

[0061] D g It is a double difference operation matrix. For single-difference carrier phase observations, λ g Let g represent the carrier wavelength of constellation g, and b be the relative position vector. For single-difference integer ambiguity, This represents the tropospheric error in the double-difference carrier phase observations. Measurement noise for double-difference code observations, This is for current layer error.

[0062] For ease of understanding, the two-difference observation equation can be simplified into a matrix expression solved using the least squares method:

[0063] y=A(Δb ur )+BN+ε

[0064] Where y is the double-difference carrier phase and pseudo-range measurement vector given by the receiver, A and B are constant coefficient matrices, N is the double-difference full-period ambiguity vector, and Δb ur This represents the unknown baseline vector or baseline vector correction. Based on the least squares principle, the least squares solution can be obtained:

[0065]

[0066] Q x =([AB]) T C[AB]) -1

[0067] Where C = Q -1 Q is defined as the weight matrix. The weight matrix Q is determined using measurement noise. Since the original observations of the satellite carrier phase are independent, uniformly distributed random variables, the variance of the carrier phase observations is: The variance of the pseudo-distance observations is:

[0068]

[0069] Where f 1n =f1 + fn, where f is the carrier phase weight and p is the pseudorange weight. A joint weighting model of elevation angle and carrier-to-noise ratio is adopted, taking into account both the carrier-to-noise ratio and the satellite elevation angle. The specific weighting model is as follows:

[0070]

[0071] Where E i Let C / N be the elevation angle of the satellite at the i-th epoch. 0,Measured The measured carrier-to-noise ratio.

[0072] In this step, floating-point solutions for the relative positioning vector and double-difference ambiguity are obtained.

[0073] Step S13: Correct the a priori residuals of satellite signal observations. Calculate the weight factor corresponding to each satellite using the standardized residuals and the IGG3 method, and substitute the weight factor into the double difference factor matrix to obtain the corrected observations. Solve the matrix to obtain the corrected floating-point solution and variance matrix.

[0074] In this invention, the ambiguity correction model based on prior residuals calculates the residuals of carrier phase double differences and pseudorange double differences for each satellite in the least squares iteration using the floating-point solution and its variance-covariance matrix. Then, using the Danish method, the residual correction factor for each satellite is calculated using the standardized residuals. These residual correction factors are then superimposed into the least squares weighted matrix, and the corrected floating-point solution and variance matrix are recalculated. The IGG3 method is a measurement robustness estimation theory, and its model is as follows:

[0075]

[0076] Sv i λ is the standardized residual of pseudorange and carrier phase; k0 and k1 are two thresholds of the standardized residual. In this paper, k0 is set to 1, k1 is set to 3, and λ is... i It is the weight factor of the IGG3 weight function.

[0077] Step S14: Fix part of the ambiguity of the modified ambiguity subset in step S13;

[0078] In this invention, a high-precision, high-reliability baseline vector can only be obtained when the integer ambiguity values ​​are fixed. If the integer ambiguity values ​​are incorrectly fixed, the baseline vector result will produce a larger error. The integer ambiguity is fixed using the least squares ambiguity downcorrelation adjustment method.

[0079] like Figure 2As shown, in some embodiments, step S14 includes: step S141, transforming the obtained ambiguity subset into LDL decomposition in Z space, performing ambiguity decorrelation, and obtaining the conditional variance matrix. The tasks in the decorrelation stage mainly involve two aspects:

[0080] (1) Make the absolute value of the off-diagonal elements in matrix L less than 0.5.

[0081] (2) Sort the diagonal elements of matrix D in descending order.

[0082] In some embodiments, step S14 includes: step S142, sorting the ambiguity subset according to the conditional variance matrix, and excluding satellites with poor signal quality by using elevation angle and signal-to-noise ratio thresholds.

[0083] In some embodiments, step S14 includes: step S143, calculating the lower limit of the success rate of fixing the ambiguity of the ambiguity subset by sequential rounding method, and comparing it with the set threshold. If the success rate is greater than the threshold, proceed to the next step; otherwise, remove the last element in the ambiguity subset. If the number of satellites does not meet the requirements after removing the element, output the floating-point solution; otherwise, repeat this step.

[0084] In some embodiments, step S14 includes: step S144, looking up a table using FFRT and comparing it with a set threshold to perform FFRT detection. If the FFRT is greater than the threshold, proceed to the next step; otherwise, remove the last element in the ambiguity subset. If the number of satellites does not meet the requirements after removing the element, output the floating-point solution; otherwise, repeat this step.

[0085] In some embodiments, step S14 includes: step S145, using the obtained improved partial ambiguity subset to search for a fixed solution using the MLAMBDA algorithm. The search conditions for integer ambiguity based on integer least squares can be expressed as:

[0086]

[0087] The objective function can be simplified to:

[0088]

[0089] Let the maximum value of the objective function be r. 2

[0090]

[0091] To fix the ambiguity, we need to find a set of integer ambiguity parameters within an n-dimensional sphere of radius r that minimizes the objective function f. The search space is shaped like an ellipsoid in a multidimensional space. A larger radius results in a larger search space, leading to a higher confidence level for the optimal integer ambiguity, but also a greater computational burden. Conversely, a smaller radius results in a smaller search space, and the optimal integer ambiguity may not even be within the search area, leading to a lower confidence level. Because... It is an upper triangular matrix, which can be solved using a sequential solution method, starting from the last integer ambiguity and working backwards to finally obtain the solution. According to the objective function, we can obtain

[0092]

[0093] Solving All integers within the interval constitute the integer ambiguity. The candidate group is then substituted into the following formula to obtain the candidate group for the previous ambiguity: The general formula for iteration is:

[0094]

[0095] If the calculation results in a range exceeding the search radius, the current result is discarded. After obtaining the fixed group and alternative group of ambiguity parameters, they are substituted into the objective function. The combination that minimizes the objective function is the optimal ambiguity combination.

[0096] In some embodiments, step S14 includes: step S146, performing a ratio decision on the optimal and suboptimal solutions obtained by the MLAMBDA algorithm.

[0097] Step S15: Perform a ratio-based posterior consistency traversal test. During the traversal search, monitor the number of satellites participating in the solution. If the number of satellites is less than the threshold, output the floating-point solution. If the number of satellites after the traversal search is greater than or equal to the threshold, proceed to the next step.

[0098] In this invention, the posterior consistency detection based on ratio is designed for the extreme case where the residuals of some satellite observations are large after satellite lock-on is lost. Using a subset of ambiguities and a covariance matrix obtained by fixing some ambiguities based on prior residual correction, a set of ambiguity-fixed solutions with the optimal ratio value is obtained through a traversal search.

[0099] In some embodiments, step S15 includes: step S151, using an elevation angle signal-to-noise ratio integrated weighting model to sort the data and obtain the four optimal sets of double-difference satellites as the base subset for posterior consistency detection. The base ratio value of the base subset is then calculated.

[0100] In some embodiments, step S15 includes: step S152, after obtaining the basic subset, adding the remaining ambiguities in the partial ambiguity subset to the basic subset in sequence; if the Ratio value increases compared to the basic Ratio value, then adding this ambiguity to the basic subset to obtain a new posterior subset.

[0101] In some embodiments, step S15 includes: step S153, if the dimension of the posterior subset obtained after the first traversal search is <6, then output the floating-point solution; if the posterior subset is ≥6, then perform a second traversal search, in which each ambiguity in the posterior subset is removed in turn to form a new posterior subset; if the Ratio value of the new posterior subset is greater than the Ratio value of the previous posterior subset, then remove this satellite, until the dimension of the posterior subset is =6 or the traversal search is completed.

[0102] In some embodiments, step S15 includes: step S154, outputting a subset of ambiguities after posterior ratio consistency, and fixing it using the Mlambda algorithm.

[0103] Step S16: Obtain the fixed solution of integer ambiguity, the fixed solution of the three-dimensional relative position vector and its covariance matrix.

[0104]

[0105] It also includes step S17, which involves calculating the ambiguity of the carrier phase differential part based on the result obtained in step S16, and then eliminating it in descending order of the conditional variance matrix to obtain a subset of ambiguities that are easier to fix under constraints. This significantly improves the efficiency of fixing integer ambiguities and is more suitable for navigation scenarios during the autonomous landing phase of rocket recovery.

[0106] The above detailed description further illustrates the purpose, technical solution, and beneficial effects of the invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A partial ambiguity resolution method suitable for use in complex dynamic environments, characterized by: The method comprises the following steps: Step S11, satellite signals of a mobile station and a reference station are received by a satellite antenna and a satellite navigation receiver respectively, and satellite positions and receiver observation direction vectors are calculated through observation data and ephemeris data of the two kinds of satellite signals to construct a single-epoch carrier phase double-difference differential observation equation; Step S12, a priori residual correction is performed on the observation value of S11: a weight factor corresponding to each satellite is calculated through standardized residual and IGG3 method, and the weight factor is brought into a double-difference factor matrix to obtain a corrected observation value; and the observation equation is updated; Step S13, the updated observation equation of S12 is solved by a weighted iterative least square method, and a corrected float solution and a variance matrix are obtained by the least square method; Step S14, the float solution of the ambiguity in step S13 is fixed in part to obtain a fixed solution of the integer ambiguity; Step S15, post-iterative consistency detection based on Ratio is performed, and the number of satellites participating in the solution is monitored in the process of iterative search, if the number of satellites is less than a threshold value, the float solution is output; if the number of satellites is greater than or equal to the threshold value after the iterative search, the next step is entered; Step S16, the fixed solution of the integer ambiguity is brought into the updated observation equation to obtain a fixed solution of the three-dimensional relative position vector and its covariance matrix, that is, the carrier phase difference partial ambiguity solution is realized.

2. The method of claim 1, wherein: The observation equation obtained in step S11 is composed of pseudo-range observation values and carrier phase observation values in an epoch, wherein the weight value is set to 1:10000; the single-epoch carrier phase double-difference differential observation equation is specifically expressed as: D g is a double-difference operation matrix, is a single-difference carrier phase observation, λ g denotes the carrier wavelength of constellation g, b is a relative position vector, is a single-difference integer ambiguity, is a tropospheric error of the double-difference carrier phase observation, is a measurement noise of the double-difference code observation, is a tropospheric error.

3. The partial ambiguity resolution method for complex dynamic environments of claim 2, wherein: The specific implementation method of step S13 is: The updated observation equation is simplified as a matrix expression for solving by the least square method: y = A(Δb ur ) + BN + ε where y is a double-difference carrier phase and pseudo-range measurement vector given by the receiver, A and B are constant coefficient matrices, and N is a double-difference full-cycle ambiguity vector; Δb ur is an unknown baseline vector or baseline vector correction; Based on the least square principle, the least square solution is obtained: Q x = ([A B] T C[A B]) -1 where C = Q -1 Q is defined as a weight matrix; the weight matrix Q is determined using the measurement noise, and the original observation of the satellite carrier phase is an independent equally distributed random variable, then the variance of the carrier phase observation is: The variance of the pseudo-range observation is: where f 1n = f1+ fn, f is the carrier phase weight, and p is the pseudorange weight; a joint weighting model of elevation angle and carrier-to-noise ratio is adopted, which considers both the carrier-to-noise ratio factor and the satellite elevation angle factor, and the specific weighting model is as follows: where E i is the elevation angle of the ith satellite at the epoch, C / N 0,Measured is the measured carrier-to-noise ratio.

4. The partial ambiguity resolution method applicable to complex dynamic environments as described in claim 3, characterized in that: The step S14 comprises the following steps: Step S142, the ambiguity float solution subset is sorted according to the conditional variance matrix, and satellites with poor signal quality are excluded through an elevation angle and a signal-to-noise ratio threshold, the minimum number of satellites participating in the solution is n=8, and the ambiguity fixing success rate threshold P is 0.995; Step S143, the ambiguity fixing success rate lower limit of the ambiguity float solution subset is calculated by the sequential integer method, and compared with a preset threshold value, if the success rate is greater than the threshold value, the next step is entered, otherwise the last element in the ambiguity float solution subset is removed, if the number of satellites after the removal does not meet the requirement, the float solution is output, otherwise the step is repeated; Step S144, FFRT table lookup is performed through the number of satellites and the ambiguity fixing success rate obtained in the previous step, and compared with a preset threshold value for FFRT detection, if the FFRT is greater than the threshold value, the next step is entered, otherwise the last element in the ambiguity float solution subset is removed, if the number of satellites after the removal does not meet the requirement, the float solution is output, otherwise the step is repeated; Step S145, the improved ambiguity float solution subset is obtained, and an MLAMBDA algorithm is searched to fix the solution; Step S146, the optimal solution and the suboptimal solution obtained by the MLAMBDA algorithm are judged by Ratio; and the fixing of the partial ambiguity is realized.

5. A partial ambiguity resolution method suitable for use in a complex dynamic environment as claimed in claim 4, characterized in that: The step S15 comprises the following steps: Step S151, using the height angle signal-to-noise ratio comprehensive weighting model to sort to obtain the optimal four groups of double-difference satellites as the basis subset for the posterior consistency detection; and calculating the basis Ratio value of the basis subset; Step S152, after obtaining the basis subset, adding the remaining ambiguities in the partial ambiguity subset to the basis subset in turn, if the Ratio value increases compared with the basis Ratio value, then adding the ambiguity to the basis subset to obtain a new posterior subset; Step S153, if the dimension of the posterior subset obtained after the first traversal search is < 6, then output the floating-point solution, if the posterior subset is ≥ 6, then perform the second traversal search, the second traversal search sequentially eliminates each ambiguity in the posterior subset to form a new posterior subset, if the Ratio value of the new posterior subset is greater than the Ratio value of the previous posterior subset, then the satellite is eliminated, until the dimension of the posterior subset is = 6 or the traversal search is completed; Step S154, output the ambiguity subset after the posterior Ratio consistency, and perform the Mlambda algorithm fixing.

6. A partial ambiguity resolution method suitable for use in a complex dynamic environment as claimed in claim 5, characterized in that: In the step S13, the residual correction factor correction method is used for the IGG3 algorithm; the carrier phase double difference and the pseudo-range double difference of each satellite are calculated respectively through the floating-point solution and the variance covariance matrix, and then the residual correction factor of each satellite is calculated by using the Danish method and the standardized residual, and the residual correction factor is added to the weighted matrix of the least square to recalculate the modified floating-point solution and the variance matrix; where Sv i is the normalized residual of pseudo-range and carrier phase; k0and k1are two threshold values of the normalized residual, in which k0is 1 and k1is 3 in this paper, and λ i is the weight factor of the IGG3 method function.

7. A partial ambiguity resolution method suitable for use in a complex dynamic environment as claimed in claim 6, characterized in that: The Ratio value detection is a ratio detection, which is defined as the ratio of the residual quadratic form of the suboptimal solution to the optimal solution in the ambiguity fixing; when the optimal solution is infinitely close to the true value, the difference between the suboptimal solution and the optimal solution is greater, so the accuracy of the optimal solution can be estimated by the Ratio value judgment; usually, the threshold of the Ratio detection is manually set, when the obtained Ratio value is greater than the threshold, it is considered that the ambiguity fixing is successful, and the fixed value is used as the final result, otherwise, it is considered that the ambiguity fixing accuracy is poor, and the floating-point solution is used for the final result; the Ratio detection method is a data-driven type, and the threshold is usually set to 2-3, and the specific discrimination condition is as follows: wherein, is a float solution, is an optimal solution, is a suboptimal solution, is a float solution covariance matrix.

8. A partial ambiguity resolution method suitable for use in a complex dynamic environment as claimed in claim 1, 2, 3, 4, 5, 6 or 7, characterized in that: It also comprises step S17, obtaining the carrier phase difference partial ambiguity solution result according to the result obtained in step S16, and eliminating through the conditional variance matrix in descending order to obtain the ambiguity subset which is more easily fixed under the constraint, thereby significantly improving the integer ambiguity fixing efficiency, and being more suitable for the navigation scene of the rocket recovery autonomous landing stage.

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