CN112083464B

CN112083464B - Partial ambiguity fixing method and device

Info

Publication number: CN112083464B
Application number: CN201910516786.5A
Authority: CN
Inventors: 曾琪
Original assignee: Beijing Unistrong Science & Technology Co ltd
Current assignee: Beijing Unistrong Science & Technology Co ltd
Priority date: 2019-06-14
Filing date: 2019-06-14
Publication date: 2023-12-26
Anticipated expiration: 2039-06-14
Also published as: CN112083464A

Abstract

The application discloses a method and a device for fixing partial ambiguity, wherein after an ambiguity eliminating sequence is determined according to a target strategy, if a first condition is met, the ambiguity is fixed, and when the Ratio of the fixed ambiguity does not exceed a fourth threshold value, the ambiguity is indicated to be failed to be fixed, the ambiguity is needed to be eliminated according to the ambiguity eliminating sequence, and whether the first condition is met is continuously judged; repeating the steps until the ambiguity fixing is successful or the first condition is not met; if the first condition is not satisfied, other strategies except the target strategy can be selected from the strategy set to serve as the target strategy, and the process of determining the ambiguity eliminating sequence according to the target strategy is carried out. Therefore, in order to fully exert the advantages of partial ambiguity fixation, proper strategies can be adaptively selected from at least three strategies to carry out ambiguity screening, the limitations of each strategy set can be overcome, the complementary advantages can be realized, and the success rate of ambiguity fixation can be improved.

Description

Partial ambiguity fixing method and device

Technical Field

The present disclosure relates to the field of satellite navigation positioning technologies, and in particular, to a method and an apparatus for fixing a partial ambiguity.

Background

With the development of satellite navigation positioning, the rapid and reliable implementation of ambiguity fixing becomes the most critical process in high-precision positioning. However, because of the complex and varying observation environments, the spatial orientations of the satellites relative to the receiver and the errors in the signal propagation paths are different, making it difficult to fix ambiguities quickly and reliably.

In addition, since it is difficult and unnecessary to fix all the ambiguities at one time, it is necessary to fix part of the ambiguities at one time, and thus, the position parameters can be updated only by fixing part of the ambiguities correctly, and a positioning result with high accuracy can be obtained. However, how to achieve a fast fixation of the partial ambiguity is a problem to be solved.

Disclosure of Invention

In order to solve the technical problems in the prior art, the application provides a method and a device for fixing partial ambiguity, which can acquire an accurate and reliable ambiguity eliminating sequence, thereby improving the success rate of partial ambiguity fixing and further improving the accuracy of a positioning result.

In order to achieve the above purpose, the technical scheme provided by the application is as follows:

the application provides a method for fixing partial ambiguity, which comprises the following steps:

Acquiring an ambiguity eliminating sequence of an ambiguity set according to a target strategy, and judging whether a first condition is met or not; wherein the first condition is: the number of the ambiguities in the ambiguity set is larger than a first threshold, the number of the ambiguities which are removed is smaller than a second threshold, and the position precision factor PDOP is smaller than a third threshold;

when the first condition is met, according to the floating ambiguity and the covariance matrix thereof, acquiring the fixed ambiguity and the Ratio value Ratio thereof; if the Ratio exceeds a fourth threshold, determining that partial ambiguity is successfully fixed, and outputting the fixed ambiguity; if the Ratio does not exceed the fourth threshold, removing the ambiguity to be removed from the ambiguity set according to the ambiguity removing sequence, and continuing to execute the step of judging whether the first condition is met;

and when the first condition is not met, selecting other strategies except the target strategy from a strategy set comprising at least three strategies as the target strategy, and initializing the ambiguity set so as to continue to execute the step of acquiring the ambiguity eliminating sequence of the ambiguity set according to the target strategy.

Optionally, the policy set includes: at least three of a satellite altitude angle screening strategy, a ambiguity variance screening strategy, an integer least squares Bootstrapping success rate screening strategy and a ambiguity precision attenuation factor ADOP screening strategy.

Optionally, the selecting, as the target policy, a policy other than the target policy from a policy set including at least three policies specifically includes:

selecting other strategies except the target strategy from the strategy set according to a preset sequence to serve as the target strategy;

the preset sequence is used for recording the selected sequence of at least three strategies included in the strategy set.

Optionally, before the selecting, in a preset order, the policies other than the target policy from the policy set, the method further includes:

judging whether all strategies in the strategy set have been traversed;

if all strategies in the strategy set are traversed, determining that the ambiguity fixing fails, and outputting floating ambiguity;

and if the existence strategies in the strategy set are not traversed, continuing to execute the step of selecting other strategies except the target strategy from the strategy set according to a preset sequence.

Optionally, the obtaining the order of removing the ambiguity of the ambiguity set according to the target policy specifically includes:

according to a target strategy, sequencing the ambiguities in the ambiguity set to obtain an ambiguity eliminating sequence;

And if the ambiguity which participates in the ambiguity fixing process for the first time exists in the ambiguity set, adjusting the ambiguity eliminating sequence, so that the ambiguity which participates in the ambiguity fixing process for the first time is in the sequence which is eliminated first in the adjusted ambiguity eliminating sequence.

Optionally, before the obtaining the order of removing the ambiguity of the ambiguity set according to the target policy, the method further includes:

removing the ambiguity included in the satellite meeting the second condition from the original ambiguity set to obtain a target ambiguity set, and taking the target ambiguity set as the ambiguity set; wherein the original ambiguity set includes all ambiguities to be fixed; the second condition is: the satellite altitude is lower than a preset altitude threshold, the signal to noise ratio of the satellite is lower than a preset signal to noise ratio threshold, and the number of continuous tracking epochs of the satellite is lower than a preset number threshold;

the initialization of the ambiguity set is specifically as follows: and taking the target ambiguity set as the ambiguity set.

Optionally, the obtaining the fixed ambiguity and the Ratio value Ratio thereof according to the floating ambiguity and the covariance matrix thereof specifically includes:

and according to the floating ambiguity and the covariance matrix thereof, carrying out ambiguity fixing on the ambiguity set by utilizing a least square ambiguity-reduction correlation method LAMBDA to obtain a fixed ambiguity and a Ratio value Ratio thereof.

Optionally, the second threshold is determined according to 8 and a minimum value of one half of the number of ambiguities comprised by the initialized set of ambiguities.

Optionally, the first threshold is 4; the third threshold is 3; the fourth threshold is 2.5.

The application also provides a fixing device for partial ambiguity, comprising:

the acquisition unit is used for acquiring the ambiguity eliminating sequence of the ambiguity set according to the target strategy;

a judging unit configured to judge whether a first condition is satisfied; wherein the first condition is: the number of the ambiguities in the ambiguity set is larger than a first threshold, the number of the ambiguities which are removed is smaller than a second threshold, and the position precision factor PDOP is smaller than a third threshold;

the eliminating unit is used for acquiring fixed ambiguity and a Ratio value Ratio thereof according to the floating ambiguity and a covariance matrix thereof when the first condition is met; if the Ratio exceeds a fourth threshold, determining that partial ambiguity is successfully fixed, and outputting the fixed ambiguity; if the Ratio does not exceed the fourth threshold, removing the ambiguity to be removed from the ambiguity set according to the ambiguity removing sequence, and continuing to execute the step of judging whether the first condition is met;

And the updating unit is used for selecting other strategies except the target strategy from a strategy set comprising at least three strategies to serve as the target strategy when the first condition is not met, and initializing the ambiguity set so as to continuously execute the step of acquiring the ambiguity eliminating sequence of the ambiguity set according to the target strategy.

Compared with the prior art, the application has at least the following advantages:

according to the partial ambiguity fixing method, after the ambiguity eliminating sequence corresponding to the ambiguity set is determined according to the target strategy, if the first condition is met, the ambiguity fixing is carried out, when the Ratio does not exceed the fourth threshold, the partial ambiguity fixing is failed, at the moment, the ambiguity eliminating is needed according to the ambiguity eliminating sequence, and the step of determining whether the first condition is met is continued; repeating the steps until the ambiguity fixing is successful or the first condition is not met; if the first condition is not met, the current target strategy is not used in the current application scene, at this time, other strategies except the target strategy can be selected from the strategy set to serve as target strategies, and the step of determining the ambiguity eliminating sequence according to the target strategies is carried out in a returning mode. In the method, the bad influence of the ambiguity with lower precision on the ambiguity fixing process can be reduced after the ambiguity with lower precision is removed from the ambiguity set, so that the fixing success rate of the partial ambiguity can be improved, the quick fixing of the partial ambiguity can be realized, and the precision of a positioning result is improved.

In addition, in order to fully exert the advantage of partial ambiguity fixation, the method can adaptively select proper strategies from the strategy set comprising at least three strategies to carry out ambiguity screening, so that the limitation of each strategy set is overcome to the maximum extent, the advantages are complementary, and the success rate of ambiguity fixation is improved.

In addition, in the application, as the ambiguity eliminating sequence is not invariable, the ambiguity eliminating sequence is updated along with the updating of the target strategy, so that the partial ambiguity fixing method can be more suitable for various application scenes, and is particularly suitable for dynamic positioning scenes.

Further, a reasonable dynamic ambiguity eliminating sequence is constructed according to the actual characteristics of the dynamic positioning scene and the data, and compared with the prior art, the method is more suitable for the actual situation of dynamic positioning.

Drawings

In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments described in the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

FIG. 1 is a flow chart of a method for fixing partial ambiguity provided in an embodiment of the method of the present application;

FIG. 2 is a timing chart of Ratio and satellite change obtained by adopting LAMBDA algorithm to fix ambiguity for real-time dynamic data provided by the embodiment of the application;

fig. 3 is a schematic structural diagram of a partial ambiguity fixing apparatus according to an embodiment of the present application.

Detailed Description

In view of the technical problems provided in the background art, the inventor finds that in the process of fixing a part of ambiguities, the core problem is how to select an ambiguity subset, that is, how to select an ambiguity subset capable of being successfully fixed from an ambiguity set including all ambiguities.

In addition, the selection process of the ambiguity subset may specifically be: and ordering all the ambiguities in the ambiguity set according to an ambiguity screening strategy or a preset criterion so as to remove the ambiguities according to the order, and obtaining an ambiguity subset comprising the ambiguities with higher precision, so that the ambiguity subset can be fixed subsequently. Therefore, the ambiguity screening strategy can influence the success rate of partial ambiguity fixing because the ambiguity screening strategy can influence the selection of the ambiguity subset and the selection of the ambiguity subset can influence the success rate of partial ambiguity fixing.

However, in the present application, four kinds of ambiguity filtering strategies may be used to filter the ambiguities in the ambiguity set, where the four kinds of ambiguity filtering strategies may be: satellite altitude angle screening policies, ambiguity variance screening policies, integer least squares Bootstrapping success rate screening policies (hereinafter, bootstrapping success rate) and fuzzy precision attenuation factor (Ambiguity Dilution of Precision, ADOP) screening policies.

For ease of explanation and understanding, the four ambiguity screening strategies described above will be described in turn.

First, the relevant content of the satellite altitude-angle screening strategy will be described.

The satellite altitude angle can reflect the accuracy of the ambiguity, so that a satellite altitude angle screening strategy is provided. The satellite altitude angle screening strategy specifically comprises the following steps: and sequencing according to the altitude angle of each satellite, and then sequentially and iteratively removing the ambiguities included in the satellite with the lowest altitude angle from the ambiguity set until the ambiguities are successfully fixed.

It should be noted that, since each satellite may include at least one ambiguity, when using the satellite altitude screening strategy for ambiguity rejection, at least one ambiguity may be rejected at a time.

The above is relevant to the satellite altitude angle screening strategy.

The relevant content of the ambiguity variance filtering strategy is described below.

Because the ambiguity variance is an important indicator for measuring the accuracy of the floating ambiguity, and the smaller the variance, the higher the accuracy of the floating ambiguity is, the more likely it is to be fixed, thus providing an ambiguity variance screening strategy. The ambiguity variance screening strategy specifically comprises the following steps: firstly, before ambiguity fixing is carried out, covariance matrixes of floating ambiguity corresponding to all satellites are needed to be obtained, and variances of all ambiguities are obtained according to diagonal elements of the covariance matrixes; then sorting according to the variance of each ambiguity, and then removing the ambiguity from the ambiguity set in sequence and iteratively until the ambiguity is successfully fixed.

The above is relevant content of the ambiguity variance filtering strategy.

The following describes the relevant content of the Bootstrapping success rate screening strategy.

The success rate of ambiguity fixing refers to the probability magnitude that the floating ambiguity is fixed to the correct integer ambiguity.

The success rate of the ambiguity fixation can represent the strength of the GNSS mathematical model, so that the success rate of the ambiguity fixation becomes a quantization index for measuring the correct fixation probability of the ambiguity. However, when the least squares ambiguity-reducing correlation method (Least Square Ambiguity Decorrelation Adjustment, LAMBDA) is used for ambiguity fixing, since the LAMBDA algorithm is based on the integer least squares principle, but the integer least squares success rate is calculated by integrating the probability density function of the ambiguity over its regular domain, the integration result cannot be directly calculated by the numerical integration method. While Bootstrapping success rate is the lower bound of the integer least squares success rate, and is considered to be the approximate solution that most approximates the integer least squares success rate, so Bootstrapping success rate approximation can be used as the integer least squares success rate. Furthermore, a screening strategy of Bootstrapping success rate is provided. And the Bootstrapping success rate can be calculated by using formulas (1) to (2).

Wherein P is _s Representing Bootstrapping success rate;representing a continuous multiplication operation; n represents the dimension of the ambiguity;representing the conditional variance of the ith ambiguity fixed to integer ambiguities with the previous (i-1) floating ambiguity.

Based on the calculation formula of the Bootstrapping success rate provided above, the Bootstrapping success rate screening strategy specifically includes: when partial ambiguity fixing is performed, a preliminary value is presetInitial success rate P ₀ Then, performing ambiguity elimination according to standard iteration of ambiguity variance reduction until the fixed success rate exceeds P ₀ Then carrying out ambiguity searching and fixing, if the fixing fails, improving P ₀ Repeating the steps until the fixation is successful.

The above is related content of the Bootstrapping success rate screening strategy.

The following describes the relevant content of the ADOP screening strategy.

ADOP (Ambiguity Dilution of Precision) refers to a modulo dextrin degree attenuation factor that describes the precision level of floating ambiguity, and ADOP can be calculated using equation (3).

In the method, in the process of the invention,a covariance matrix representing floating ambiguity; det [. Cndot.]Representing matrix determinant calculations; n represents the dimension of the floating ambiguity.

The ADOP screening strategy differs from the several strategies described above in that: the ADOP value considers the variance and covariance information in the covariance matrix of the ambiguity, reflects the average accuracy level of the ambiguity, and the smaller the ADOP value is, the higher the average accuracy of the ambiguity is, and the easier the overall fixation is.

Therefore, when the partial ambiguity fixing is performed by using the ADOP filtering strategy, if the fixing fails, the LAMBDA fixing needs to be performed for the subset with the minimum ADOP after the elimination in the case of eliminating one ambiguity from the ambiguity set including all ambiguities, and if the fixing fails again, the LAMBDA fixing needs to be performed for the subset with the minimum ADOP after the elimination in the case of eliminating two ambiguities from the ambiguity set including all ambiguities until the ambiguity fixing is successful.

The above is relevant content of the ADOP screening strategy.

However, the inventors have further studied and found that the four screening strategies provided above all have the following technical problems:

the screening conditions according to the four screening strategies are single, and the actual characteristics of the dynamic positioning scene and the data are not combined, so that the application effect of dynamic positioning in a complex environment is not good; in addition, in dynamic positioning, the precision and the fixing success rate of the ambiguity are related to multiple factors, and the ambiguity is screened according to single or few information, so that the advantage of partial ambiguity fixing is difficult to be exerted to the greatest extent.

In order to solve the technical problems described in the background art and the problems further found by the inventor, the embodiment of the application provides a method for fixing partial ambiguities, in which, after removing ambiguities with lower precision from ambiguity set, adverse effects of the ambiguities with lower precision on the process of fixing the ambiguities can be reduced, so that the success rate of fixing the partial ambiguities can be improved, and thus the fixation of the partial ambiguities can be realized rapidly, and the accuracy of a positioning result is improved. In addition, in order to fully exert the advantage of partial ambiguity fixation, the method can adaptively select proper strategies from the strategy set comprising at least three strategies to carry out ambiguity screening, so that the limitation of each strategy set is overcome to the maximum extent, the advantages are complementary, and the success rate of ambiguity fixation is improved. In addition, in the application, as the ambiguity eliminating sequence is not invariable, the ambiguity eliminating sequence is updated along with the updating of the target strategy, so that the partial ambiguity fixing method can be more suitable for various different application scenes, especially for dynamic positioning scenes, and further, a reasonable dynamic ambiguity eliminating sequence is constructed according to the actual characteristics of the dynamic positioning scenes and data, and is more suitable for the actual dynamic positioning situations compared with the prior art method.

In order to make the present invention better understood by those skilled in the art, the following description will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.

Method embodiment

Referring to fig. 1, a flowchart of a method for fixing a partial ambiguity is provided in an embodiment of the method.

The method for fixing the partial ambiguity provided by the embodiment of the application comprises the following steps:

s101: a set of ambiguities is determined.

S102: and determining a target strategy.

S103: and obtaining the ambiguity eliminating sequence of the ambiguity set according to the target strategy.

S104: judging whether the first condition is satisfied, if so, executing S105; if not, S109 is performed.

S105: and obtaining the fixed ambiguity and the Ratio value Ratio thereof according to the floating ambiguity and the covariance matrix thereof.

S106: judging whether the Ratio exceeds a fourth threshold, if so, executing S107; if not, S108 is performed.

S107: and determining that partial ambiguity is successfully fixed, and outputting the fixed ambiguity.

S108: and according to the ambiguity eliminating sequence, eliminating the ambiguity to be eliminated from the ambiguity set, and returning to the step S104.

S109: and selecting other strategies except the target strategy from a strategy set comprising at least three strategies as the target strategy.

S110: initializing the ambiguity set, and returning to execute step S103 according to the updated target policy and the initialized ambiguity set.

The S101 and S102 are not in a fixed execution order, and S101 and S102 may be executed sequentially, S102 and S101 may be executed sequentially, or S101 and S102 may be executed simultaneously.

The above is the execution steps of the partial ambiguity fixing method provided in the first embodiment of the present application, and in order to facilitate explanation and understanding of the specific implementation of the partial ambiguity fixing method provided in the first embodiment of the present application, steps S101 to S110 will be described in sequence.

First, a specific embodiment of S101 will be described.

In S101, the ambiguity set includes a plurality of ambiguities to be fixed. In addition, it should be noted that, in the present application, the ambiguity to be fixed is floating ambiguity.

The ambiguity set may include all the ambiguities to be fixed, or may include part of the ambiguities to be fixed; further, the ambiguity set may be set in advance, for example, the ambiguity set may be set in advance according to the application scenario.

As a first embodiment, S101 may specifically be: and taking all the ambiguity sets to be fixed as ambiguity sets.

In addition, in order to improve the precision of the ambiguities included in the ambiguity set and improve the success rate and efficiency of partial ambiguity fixing, the initial screening can be performed on all the ambiguity sets so as to reject ambiguities with lower precision. Thus, the present application provides a second embodiment, in which S101 may specifically include S1011-S1013:

s1011: and forming an original ambiguity set according to all ambiguity sets to be fixed.

S1012: and removing the ambiguity included in the satellite meeting the second condition from the original ambiguity set to obtain a target ambiguity set.

Wherein the second condition is: the altitude of the satellite is lower than a preset altitude threshold, the signal to noise ratio of the satellite is lower than a preset signal to noise ratio threshold, and the number of continuous tracking epochs of the satellite is lower than a preset number threshold.

The preset altitude angle threshold may be preset, for example, the preset altitude angle threshold may be preset according to an application scenario. As an example, the preset elevation angle threshold value may be preset to any value between 10 ° and 15 °.

The preset snr threshold may be preset, for example, the preset snr threshold may be preset according to an application scenario. As an example, the preset signal-to-noise threshold may be preset to any value between 35dBHz and 45 dBHz.

The preset number threshold may be preset, for example, the preset number threshold may be preset according to an application scenario. As an example, the preset number threshold may be preset to any integer value between 5 and 10.

It should be noted that each satellite may include at least one ambiguity.

S1013: the target ambiguity set is taken as an ambiguity set.

In the second embodiment of S101, in this embodiment, the target ambiguity set for eliminating the ambiguities included in the satellite that satisfies the second condition may be used as the ambiguity set, thereby improving the accuracy of the ambiguities included in the ambiguity set, and being beneficial to improving the success rate of fixing the partial ambiguities.

The above is a specific embodiment of S101, in which the set of all ambiguities may be regarded as an ambiguity set; and the set of all the ambiguities can be used as an original ambiguity set, and the ambiguities included in the satellite meeting the second condition are removed from the original ambiguity set to obtain the ambiguity set. Therefore, the ambiguity included in the satellite with poor ambiguity precision can be filtered, the screening speed of the ambiguity is accelerated, and the success rate of partial ambiguity fixation is improved.

A specific embodiment of S102 is described below.

In S102, the target policy refers to a policy for ordering the ambiguities according to factors affecting the accuracy of the ambiguities, so that the ordered ambiguities can be changed from high to low (or from low to high) according to the accuracy of the ambiguities.

In the method for fixing the partial ambiguity provided by the application, the target strategy used for the first time may be preset, for example, may be preset according to an application scenario. At this time, the present application provides an embodiment of S102, in this embodiment, S102 may specifically be: determining a preset strategy as a target strategy; the preset strategy is preset, and is any strategy included in the strategy set.

It should be noted that the policy set may include: at least three of a satellite altitude angle screening strategy, an ambiguity variance screening strategy, a Bootstrapping success rate screening strategy and an ADOP screening strategy.

In addition, because different epoch data have different correlations, the target policy used for the first time may also be a policy used when the ambiguity fixing of the previous epoch data is successful. At this time, the present application provides an embodiment of S102, in this embodiment, S102 may specifically be: and taking a strategy used when the ambiguity of the previous epoch data is successfully fixed as a target strategy.

As an example, if the first epoch data and the second epoch data have a correlation, and the first epoch data successfully fixes the ambiguity with the second policy in the policy set, when fixing the ambiguity corresponding to the second epoch data, S102 may specifically be: and taking the second strategy in the strategy set as a target strategy.

The above is a specific embodiment of S102, in this embodiment, the target policy may be determined according to a preset policy, or may be determined according to a policy used when the ambiguity fixing of the previous epoch data is successful. Therefore, the method is beneficial to reducing the number of times of fuzzy degree iterative elimination, shortening the fuzzy degree fixing time and improving the fuzzy degree fixing efficiency.

A specific embodiment of S103 is described below.

In S103, the ambiguity set includes a plurality of ambiguities to be fixed. The ambiguity rejection order corresponds to the ambiguity set, and is used to describe different rejected orders corresponding to different ambiguities in the ambiguity fixing process.

Because different target strategies will obtain different ambiguity rejection sequences, specific embodiments of determining the ambiguity rejection sequences by four different target strategies will be described in turn.

First, the first embodiment of S103 will be described.

As a first embodiment, when the target strategy is a satellite altitude screening strategy, S103 may specifically be: and according to a satellite altitude angle screening strategy, sequencing all the ambiguities in the ambiguity set according to the altitude angle, and obtaining an ambiguity eliminating sequence.

In this embodiment, since the greater the satellite altitude angle is, the higher the accuracy of the ambiguity representing the satellite is, two examples corresponding to the first embodiment of S103 are provided in the present application.

As an example, S103 may specifically be: according to a satellite altitude angle screening strategy, all the ambiguities in the ambiguity set are ordered from big to small according to the altitude angle, and an ambiguity eliminating sequence is obtained, so that in the ambiguity eliminating process, the ambiguity is eliminated from the last ambiguity in the ambiguity eliminating sequence.

As another example, S103 may specifically be: according to a satellite altitude angle screening strategy, all the ambiguities in the ambiguity set are ordered from small to large according to the altitude angle, and an ambiguity eliminating sequence is obtained, so that in the ambiguity eliminating process, the ambiguity is eliminated from the most previous ambiguity in the ambiguity eliminating sequence.

The above is the first embodiment of S103.

A second embodiment of S103 is described below.

As a second embodiment, when the target policy is an ambiguity variance filtering policy, S103 may specifically be: and according to the ambiguity variance screening strategy, sequencing all the ambiguities in the ambiguity set according to the ambiguity variance, and obtaining an ambiguity eliminating sequence.

In this embodiment, since the smaller the ambiguity variance of the floating ambiguity is, the higher the accuracy representing the floating ambiguity is, the greater the possibility of being fixed, and thus, the second embodiment of S103 is provided in the present application as two examples.

As an example, S103 may specifically be: according to a ambiguity variance screening strategy, all the ambiguities in the ambiguity set are ordered according to the ambiguity variances from small to large, and an ambiguity eliminating sequence is obtained, so that in the ambiguity eliminating process, the ambiguities are eliminated from the last ambiguity in the ambiguity eliminating sequence.

As another example, S103 may specifically be: according to a ambiguity variance screening strategy, all the ambiguities in the ambiguity set are ordered from big to small according to the ambiguity variances, and an ambiguity eliminating sequence is obtained, so that in the ambiguity eliminating process, the ambiguities are eliminated from the most previous ambiguity in the ambiguity eliminating sequence.

The above is the second embodiment of S103.

A third embodiment of S103 is described below.

As a third embodiment, when the target policy is a Bootstrapping success rate screening policy, S103 may specifically be: and ordering all the ambiguities in the ambiguity set according to the Bootstrapping success rate screening strategy to obtain an ambiguity eliminating sequence.

In this embodiment, since the greater the Bootstrapping success rate of the floating ambiguity, the greater the probability that the floating ambiguity of the satellite is fixed to the correct integer ambiguity, two examples are provided for the third embodiment of S103 in this application.

As an example, S103 may specifically be: according to a Bootstrapping success rate screening strategy, sequencing all the ambiguities in the ambiguity set according to Bootstrapping success rates from large to small to obtain an ambiguity eliminating sequence, so that in the ambiguity eliminating process, the ambiguities are eliminated from the last ambiguity in the ambiguity eliminating sequence.

As another example, S103 may specifically be: according to a Bootstrapping success rate screening strategy, all the ambiguities in the ambiguity set are sequenced from small to large according to Bootstrapping success rates, and an ambiguity eliminating sequence is obtained, so that in the ambiguity eliminating process, the ambiguity is eliminated from the most previous ambiguity in the ambiguity eliminating sequence.

The third embodiment of S103 is described above.

A fourth embodiment of S103 is described below.

As a fourth embodiment, when the target policy is an ADOP screening policy, S103 may specifically be: when the preset number of ambiguities need to be removed from the ambiguity set, the sizes of the ADOP values of the ambiguity subsets with the preset number of ambiguities removed can be ordered according to an ADOP screening strategy, and an ambiguity removing sequence is obtained.

Each ambiguity subset is obtained by removing a preset number of ambiguities from the ambiguity set. As an example, when the ambiguity set includes N ambiguities and 1 ambiguity needs to be removed from the ambiguity set, then the 1 st ambiguity subset may be obtained by removing the 1 st ambiguity from the ambiguity set, the 2 nd ambiguity subset may be obtained by removing the 2 nd ambiguity from the ambiguity set, … …, and the nth ambiguity subset may be obtained by removing the nth ambiguity from the ambiguity set.

The above example is described with respect to the case where 1 ambiguity needs to be removed from the ambiguity set, as an example, the ambiguity subset is described. However, in the present application, not only the ambiguity subset from which 1 ambiguity is removed, but also the ambiguity subset from which at least two ambiguities are removed may be obtained according to the ambiguity set, and the obtaining manner of the ambiguity subset from which at least two ambiguities are removed is the same as the above example, which is not described here again for brevity.

In this embodiment, since the smaller the ADOP value of the floating ambiguity is, the higher the average accuracy of the ambiguity is, the easier the overall fixation is, and thus, the fourth embodiment of S103 is provided in this application to correspond to two examples.

As an example, S103 may specifically be: according to an ADOP screening strategy, calculating ADOP values corresponding to the ambiguity subsets with the preset number of ambiguities removed; sequencing all the ambiguity subsets with the preset number of ambiguities removed according to the ADOP value from large to small to obtain an ambiguity removing sequence corresponding to the ambiguity subsets with the preset number of ambiguities removed, so that the last preset number of ambiguities in the ambiguity removing sequence are removed in the process of removing the preset number of ambiguities.

As another example, S103 may specifically be: according to an ADOP screening strategy, calculating ADOP values corresponding to the ambiguity subsets with the preset number of ambiguities removed; and sequencing the ambiguity subsets with the preset number of ambiguities removed according to the ADOP value from small to large to obtain an ambiguity removing sequence corresponding to the ambiguity subsets with the preset number of ambiguities removed, so that the most expensive preset number of ambiguities in the ambiguity removing sequence are removed in the process of removing the preset number of ambiguities.

It should be noted that, in the present application, the process of performing the ambiguity screening by using the ADOP screening policy is different from the process of performing the ambiguity screening by using the above three screening policies, in that: when the three screening strategies are utilized for carrying out the ambiguity screening, the ambiguity set based on the current round of ambiguity eliminating process is the ambiguity set obtained after the ambiguity eliminating action is executed in the previous round of ambiguity eliminating process; however, when the ADOP filtering strategy is used for filtering the ambiguity, the ambiguity set according to each ambiguity rejection process is the ambiguity set (or the ambiguity set after initialization) obtained in S101.

The specific process of carrying out ambiguity screening by utilizing an ADOP screening strategy comprises the following steps: if the ADOP screening strategy is used for carrying out the ambiguity elimination for the first time, 1 ambiguity to be eliminated can be determined according to the corresponding ambiguity elimination sequence of each ambiguity subset after 1 ambiguity is eliminated, and the 1 ambiguity to be eliminated is eliminated from the initialized ambiguity set; if the ADOP screening strategy is used for the second time for the ambiguity elimination, 2 ambiguities to be eliminated can be determined according to the corresponding ambiguity elimination sequence of each ambiguity subset after eliminating 2 ambiguities, and the 2 ambiguities to be eliminated are eliminated … … from the initialized ambiguity set

The fourth embodiment of S103 is described above.

The above are four embodiments of S103, in which the order of blur degree elimination of the blur degree set may be obtained according to the target policy, so that the blur degree to be eliminated can be eliminated from the blur degree set in the order of blur degree elimination later.

In addition, further studies have found that: in dynamic positioning, the number of satellites participating in the ambiguity fixing process is increased due to the initial rising of satellites and the occurrence of interruption or cycle slip reset of the satellites, and when the satellites participate in the ambiguity fixing process for the first time, the Ratio value of the ambiguity test is suddenly pulled down and even is lower than a preset threshold value, so that the ambiguity fixing fails.

In order to facilitate explanation and understanding of the problem that occurs when the satellite is initially lifted and the satellite with the interruption or cycle slip reset ambiguity first participates in the ambiguity fixing process, the description will be made below with reference to fig. 2, where fig. 2 is a Ratio and satellite change timing diagram obtained by fixing the ambiguity of the real-time dynamic data provided in the embodiment of the present application by using the LAMBDA algorithm.

In fig. 2, GPST is used to represent seconds within a week of the global positioning system GPS time system; NSAT is used to indicate the number of satellites participating in the fix; ratio represents the Ratio value of the quadratic form of the suboptimal solution with fixed ambiguity and the optimal solution. In addition, as can be seen from the data shown in fig. 2, each epoch with abrupt change of Ratio value corresponds to an increase of the satellite number, and the main reason is that the ambiguity variance included in the satellite participating in the ambiguity fixing process for the first time is larger, so that the overall accuracy of the floating ambiguity which has been filtered for a longer time is destroyed, and the distinguishability of the optimal solution and the suboptimal solution of the ambiguity is seriously reduced, which is shown by that the Ratio value is greatly reduced.

Based on the above, in order to avoid the problem of partial ambiguity reduction caused by the initial satellite and the satellite having the interruption or cycle slip reset ambiguity first participating in the ambiguity fixing process, the ambiguities included in the satellites first participating in the ambiguity fixing process may be preferentially removed from the ambiguity set. As such, the present application provides a fifth embodiment of S103, in which S103 may specifically be:

s1031: and sequencing the ambiguities in the ambiguity set according to a target strategy to obtain an ambiguity eliminating sequence.

The specific embodiment of S1031 may employ any one of the first to fourth embodiments of S103 provided above.

S1032: and if the ambiguity which participates in the ambiguity fixing process for the first time exists in the ambiguity set, adjusting the ambiguity eliminating sequence, so that the ambiguity which participates in the ambiguity fixing process for the first time is in the sequence which is eliminated first in the adjusted ambiguity eliminating sequence.

The ambiguity of the first participation in the ambiguity fixing process may be determined according to the ambiguity corresponding to the satellite of the first participation in the ambiguity fixing process. In addition, satellites that first participate in the ambiguity fixing process may include initial rising satellites, satellites that reset ambiguities due to breaks or cycle slips.

It should be noted that when the ionosphere-free mode is used for the resolution, since the fixing of the narrow lane (original) ambiguity is limited to the fixing of the wide lane ambiguity, the satellites to which the wide lane ambiguity is first fixed should be considered at the same time.

As an example, assuming that the ambiguity set includes 30 ambiguities, and in the ambiguity-removal sequence obtained in S1031, the earlier ambiguities in the order position are removed later, and the later ambiguities in the order position are removed earlier, when the first satellite including the first ambiguity is the satellite that participates in the ambiguity fixing process for the first time, and the first ambiguity is located at the twenty-third position in the ambiguity-removal sequence, S1032 may specifically be: and (3) moving the first ambiguity from a twenty-third position to a thirty-third position in the ambiguity eliminating sequence obtained in the step S1031 so as to eliminate the first ambiguity from the ambiguity set firstly in the subsequent ambiguity eliminating process.

In the fifth embodiment of S103, after obtaining the order of removing the ambiguities according to the target policy, the order of removing the ambiguities that participate in the ambiguity fixing process for the first time needs to be adjusted, so that in the adjusted order of removing the ambiguities, the ambiguities that participate in the ambiguity fixing process for the first time are in the order of being removed first. Therefore, the problem that the Ratio value is reduced when the satellite is initially lifted and the satellite with the interruption or cycle slip reset ambiguity participates in the ambiguity fixing process for the first time can be avoided, and the success rate and the efficiency of partial ambiguity fixing are improved.

In the above five specific embodiments of S103, in this embodiment, the ambiguity eliminating sequence of the ambiguity set may be obtained according to the target policy, so that the ambiguity to be eliminated can be eliminated from the ambiguity set according to the ambiguity eliminating sequence.

A specific embodiment of S104 is described below.

In S104, the first condition is: the number of the ambiguities in the ambiguity set is larger than a first threshold, the number of the ambiguities which are removed is smaller than a second threshold, and the position precision factor (Position Dilution of Precision, PDOP) is smaller than a third threshold

The first threshold may be preset, for example, the first threshold may be preset according to an application scenario. As an example, the first threshold value may be preset to 4.

The second threshold may be preset, for example, the second threshold may be preset according to an application scenario. In addition, to further ensure accuracy and efficiency of the acquired partial ambiguities, in the present application, the second threshold is determined according to 8 and the minimum value of one half of the number of ambiguities included in the initialized ambiguity set, that is, the second threshold may be determined according to equation (4).

Wherein T is ₂ Representing a second threshold; min (·) represents taking the minimum value; n represents the total number of ambiguities, i.e. the number of ambiguities comprised by the initialized set of ambiguities.

The third threshold may be preset, for example, the third threshold may be preset according to the application scenario. As an example, the third threshold may be preset to 3.

The above is the relevant content of the first condition.

In S104, if the first condition can be met, it means that under the current target policy, the ambiguity may also be fixed according to the current ambiguity set, so as to continue to find the ambiguity set that can be fixed successfully under the current target policy.

If the first condition cannot be met, the fact that the ambiguity set capable of being fixed successfully cannot be found under the current target strategy is indicated, and therefore the fact that the current target strategy is not suitable for partial ambiguity fixing is indicated, and at the moment, other strategies need to be replaced to continuously find the ambiguity set capable of being fixed successfully. Therefore, the defect that the ambiguity set which can be successfully fixed cannot be found due to the limitation of a single strategy can be overcome, the respective limitation is overcome to the maximum extent, the advantages are complementary, and the success rate of the ambiguity fixation is improved as a whole.

The above is a specific embodiment of S104.

The following describes a specific embodiment of S105.

In S105, the floating ambiguity and its covariance matrix refer to the floating ambiguity and the covariance matrix of the floating ambiguity; furthermore, the variance information and the covariance information are included in the covariance matrix of the floating ambiguity, wherein the diagonal line of the covariance matrix is the variance information of the floating ambiguity.

The Ratio is determined according to a quadratic form proportion value of a suboptimal solution and an optimal solution of the ambiguity; the Ratio represents the distinguishable degree of the ambiguity fixing optimal solution and the suboptimal solution, and can be used for reflecting the possibility that the obtained fixed ambiguity can be fixed successfully, wherein if the Ratio is larger, the possibility that the obtained fixed ambiguity can be fixed successfully is larger; if Ratio is smaller, it is less likely that the obtained fix ambiguity can be fixed successfully.

As an embodiment, S105 may specifically be: and according to the floating ambiguity and the covariance matrix thereof, carrying out ambiguity fixing on the ambiguity set by using a least square ambiguity reduction correlation method to obtain a fixed ambiguity and a Ratio value Ratio thereof.

The present application is not limited to the execution time for acquiring the floating ambiguity and the covariance matrix thereof, and may be acquired before S101 is executed.

The above is a specific embodiment of S105, in which the ambiguity set may be fixed by using the LAMBDA algorithm, so as to obtain a fixed ambiguity and its proportional value Ratio.

A specific embodiment of S106 is described below.

In S106, the fourth threshold may be set in advance, for example, the fourth threshold may be set in advance according to the application scenario. As an example, the fourth threshold may be preset to 2.5.

If Ratio exceeds the fourth threshold, it indicates that the obtained fixed ambiguity has a sufficiently high probability of being able to fix successfully, and other fixed ambiguities do not need to be acquired again, so that the fixed ambiguity exceeding the fourth threshold can be output or stored.

If Ratio does not exceed the fourth threshold, the ambiguity fixing failure is indicated, and the next round of fixed ambiguity is tried according to the ambiguity set after the ambiguity is removed, so that better fixed ambiguity can be obtained, and finally, the ambiguity set with successful fixation can be obtained.

The above is a specific embodiment of S106.

A specific embodiment of S107 is described below.

As an embodiment, S107 may specifically be: and determining that partial ambiguity is successfully fixed, outputting the fixed ambiguity, and storing the fixed ambiguity.

In addition, in order to facilitate the next acquisition process of the partial ambiguity to use the strategy used when determining the partial ambiguity this time, the present application further provides another embodiment of S107, where S107 may specifically be: and determining that partial ambiguity is successfully fixed, outputting the fixed ambiguity, and storing the fixed ambiguity and a target strategy.

The above is a specific embodiment of S107, in which when the fixed ambiguity exceeds the fourth threshold, it may be determined that the partial ambiguity fixing is successful, and the fixed ambiguity is output.

The following describes a specific embodiment of S108.

In S108, the ambiguity to be picked is an ambiguity that should be picked from the ambiguity set in the current state, which is determined according to the ambiguity picking order. It can be known that the degree of ambiguity to be resolved can be determined according to the degree of ambiguity resolution sequence and the degree of ambiguity set.

To facilitate explanation and understanding of the ambiguity to be resolved, two examples will be described below.

As a first example, assuming that the ambiguity set includes 30 ambiguities, the target policy is any one of a satellite altitude angle screening policy, an ambiguity variance screening policy, and a Bootstrapping success rate screening policy, and in the ambiguity rejection order obtained in S103, the earlier the ambiguity of the ranking position is rejected later, the later the ranking position is rejected earlier, and based on the foregoing assumption, when the target policy is not replaced, in the process of first rejecting the ambiguities, the ambiguity to be rejected is the ambiguity corresponding to the thirty th position in the ambiguity rejection order; in the second process of eliminating ambiguity, the ambiguity to be eliminated is the ambiguity … … corresponding to the twenty-ninth position in the ambiguity eliminating sequence (and so on).

As a second example, assuming that the ambiguity set includes 30 ambiguities, when the target policy is an ADOP filtering policy, and in the ambiguity eliminating sequence obtained in S103, the earlier the ambiguity is eliminated, the later the ambiguity is eliminated, and the earlier the ordering position is eliminated, then based on the foregoing assumption, when the target policy is not replaced, in the first ambiguity eliminating process, the ambiguity to be eliminated is the ambiguity at the thirty-th position in the ambiguity eliminating sequence, and the ambiguity eliminating sequence is determined according to each ambiguity subset after eliminating 1 ambiguity; in the second process of removing ambiguity, the ambiguity to be removed is the ambiguity at the thirty-and twenty-ninth positions in the ambiguity removal order, and the ambiguity removal order is … … (and so on) determined from each of the ambiguity subsets after removing 2 ambiguities.

The above is a specific embodiment of S108.

A specific embodiment of S109 is described below.

In S109, the policy set may include: at least three of a satellite altitude angle screening strategy, an ambiguity variance screening strategy, a Bootstrapping success rate screening strategy and an ADOP screening strategy.

As a first embodiment of S109, S109 may specifically be: and selecting other strategies except the target strategy from a strategy set as the target strategy according to a preset selection method.

The preset selection method may be preset, for example, the preset selection method may be preset according to an application scenario. As an example, the preset selection method may be selected randomly.

In order to facilitate explanation and understanding of the first embodiment of S109, description will be made below with reference to examples.

As an example, when the policy set includes: the satellite altitude angle screening strategy, the ambiguity variance screening strategy, the Bootstrapping success rate screening strategy and the ADOP screening strategy, and when the current target strategy is the satellite altitude angle screening strategy, S109 may specifically be: according to a preset selection method, selecting one strategy from an ambiguity variance screening strategy, a Bootstrapping success rate screening strategy and an ADOP screening strategy which are included in a strategy set as a target strategy.

The above is the first embodiment of S109, in which, according to a preset selection method, other policies than the target policy may be selected from among a policy set as the target policy.

As a second embodiment of S109, S109 may specifically be: and selecting other strategies except the target strategy from the strategy set according to a preset sequence to serve as the target strategy.

The preset sequence is used for recording the selected sequence of at least three strategies included in the strategy set; also, the preset order may be preset, for example, the preset order may be preset according to the application scenario.

In order to facilitate explanation and understanding of the second embodiment of S109, description will be made below with reference to examples.

As an example, assume that the policy set includes: a satellite altitude angle screening strategy, a ambiguity variance screening strategy, a Bootstrapping success rate screening strategy and an ADOP screening strategy; moreover, the preset sequence can be used for sequentially and circularly selecting a satellite altitude angle screening strategy, an ambiguity variance screening strategy, a Bootstrapping success rate screening strategy and an ADOP screening strategy.

Based on the above assumption, if the current target strategy is a satellite altitude screening strategy, S109 may specifically be: and selecting an ambiguity variance screening strategy from the strategy set according to a preset sequence as the target strategy. If the current target policy is an ADOP screening policy, S109 may specifically be: and selecting satellite altitude angle screening strategies from the strategy set according to a preset sequence as the target strategies.

In the second embodiment of S109, the target policy may be selected from the policy set according to a predetermined order, and other policies than the target policy may be selected as the target policy.

In addition, in order to improve the efficiency of the ambiguity fixing, the process of partial ambiguity fixing may be ended after traversing all policies in the policy set, so the present application further provides a third embodiment of S109, where S109 may specifically be:

s1091: judging whether all strategies in the strategy set have been traversed, if so, executing S1092; if not, S1093 is performed.

The policy set has been traversed meaning that each policy included in the policy set has been used to find a fixed ambiguity that can fix success.

As an example, assume that the policy set includes: when the satellite altitude angle screening strategy, the ambiguity variance screening strategy, the Bootstrapping success rate screening strategy and the ADOP screening strategy are used in the process of searching the fixed ambiguities capable of fixing success, if the satellite altitude angle screening strategy, the ambiguity variance screening strategy, the Bootstrapping success rate screening strategy and the ADOP screening strategy are already used, each strategy in the strategy set can be determined to be traversed.

S1092: and determining the ambiguity fixing failure and outputting floating ambiguity.

S1093: and selecting other strategies except the target strategy from the strategy set according to a preset sequence to serve as the target strategy.

The specific embodiment of S1093 is the same as the second embodiment of S109, and will not be described here again for brevity.

In the third embodiment of S109, the process of fixing the partial ambiguity may be ended after each policy of the policy set is traversed, so that the same fixing process is prevented from being repeated, and the efficiency of fixing the partial ambiguity can be improved.

The above are three embodiments of S109, in which other policies than the target policy may be selected from a policy set including at least three policies as the target policy.

A specific embodiment of S110 is described below.

In S110, the process of initializing the ambiguity set is a process of restoring the ambiguity set from which at least one ambiguity is removed to the ambiguity set determined in S101, and thus the ambiguity set after initialization and the ambiguity set determined in S101 are the same, and for convenience of explanation and explanation, the following will be described with two examples.

As a first example, assuming that the ambiguity set determined in S101 includes all ambiguities, the initialized ambiguity set also includes all ambiguities.

As a second example, assuming that the target ambiguity set is used as the ambiguity set in S101, the "initializing the ambiguity set" in S110 may specifically be: the target ambiguity set is taken as an ambiguity set.

The above is a specific embodiment of S110, and in this embodiment, the process of initializing the ambiguity set is a process of restoring the ambiguity set from which at least one ambiguity is removed to the ambiguity set determined in step S101.

In the embodiment, after determining the order of removing the ambiguities corresponding to the ambiguity set according to the target policy, if the first condition is met, the ambiguity fixing is performed, and if the Ratio does not exceed the fourth threshold, the partial ambiguity fixing fails, and at this time, the step of removing the ambiguities according to the order of removing the ambiguities is required, and whether the first condition is met is continuously determined; repeating the steps until the ambiguity fixing is successful or the first condition is not met; if the first condition is not met, the current target strategy is not used in the current application scene, at this time, other strategies except the target strategy can be selected from the strategy set to serve as target strategies, and the step of determining the ambiguity eliminating sequence according to the target strategies is carried out in a returning mode. In the method, the bad influence of the ambiguity with lower precision on the ambiguity fixing process can be reduced after the ambiguity with lower precision is removed from the ambiguity set, so that the fixing success rate of the partial ambiguity can be improved, the partial ambiguity can be quickly fixed, and the precision of a positioning result is improved.

In addition, the method combines the rough screening based on the satellite level (step S1012) and the fine screening based on the ambiguity level (steps S103 to S110), so that the waste of observation information is effectively avoided, and the reliability of the ambiguity fixing is enhanced; meanwhile, a reasonable dynamic ambiguity eliminating sequence is constructed according to the actual characteristics of dynamic positioning scenes and data, and compared with the prior art, the method is more suitable for the actual situation of dynamic positioning; in addition, in order to fully exert the advantages of partial ambiguity fixation, at least three screening strategies are adopted for screening adaptively, so that the limitations of the screening strategies are overcome to the maximum extent, the advantages are complementary, and the success rate of the ambiguity fixation is improved. A large number of data results show that compared with the prior art, the partial ambiguity fixing method provided by the application can effectively improve the performance of a fixed solution, the improvement degree is about 5% -10% for epoch fixing rate, the first time fixing time of a medium-length baseline is shortened to 27.1 minutes from 33.4 minutes on average, and the first time fixing time is shortened by about 18.9%; and meanwhile, the positioning precision is improved to a certain extent.

Based on the method for fixing the partial ambiguity provided by the above method embodiment, the embodiment of the present application further provides a device for fixing the partial ambiguity, which will be explained and illustrated with reference to the accompanying drawings.

Device embodiment

Referring to fig. 3, a schematic structural diagram of a partial ambiguity fixing apparatus according to an embodiment of the present application is shown.

The device for fixing the partial ambiguity provided by the embodiment of the application comprises:

an obtaining unit 301, configured to obtain an ambiguity rejection order of an ambiguity set according to a target policy;

a judging unit 302, configured to judge whether the first condition is satisfied; wherein the first condition is: the number of the ambiguities in the ambiguity set is larger than a first threshold, the number of the ambiguities which are removed is smaller than a second threshold, and the position precision factor PDOP is smaller than a third threshold;

a rejection unit 303, configured to obtain a fixed ambiguity and a Ratio value Ratio thereof according to the floating ambiguity and a covariance matrix thereof when the first condition is satisfied; if the Ratio exceeds a fourth threshold, determining that partial ambiguity is successfully fixed, and outputting the fixed ambiguity; if the Ratio does not exceed the fourth threshold, removing the ambiguity to be removed from the ambiguity set according to the ambiguity removing sequence, and continuing to execute the step of judging whether the first condition is met;

and an updating unit 304, configured to select, when the first condition is not satisfied, other policies except the target policy from a policy set including at least three policies, as the target policy, and initialize the ambiguity set so as to continue to execute the step of obtaining the ambiguity rejection order of the ambiguity set according to the target policy.

As an embodiment, to further improve the success rate and efficiency of the partial ambiguity fixing, and further improve the accuracy of the positioning result, the policy set includes: at least three of a satellite altitude angle screening strategy, a ambiguity variance screening strategy, an integer least squares Bootstrapping success rate screening strategy and a ambiguity precision attenuation factor ADOP screening strategy.

As an embodiment, in order to further improve the success rate and efficiency of the partial ambiguity fixing and further improve the accuracy of the positioning result, the updating unit 304 specifically includes:

a target policy updating subunit, configured to select, from the policy set, other policies except the target policy according to a preset order, as the target policy;

As an embodiment, in order to further improve the success rate and efficiency of the partial ambiguity fixing and further improve the accuracy of the positioning result, the updating unit 304 further includes:

a judging subunit, configured to judge whether all policies in the policy set have been traversed;

A partial ambiguity acquisition subunit, configured to determine that the ambiguity fixing fails and output a floating ambiguity if all policies in the policy set have been traversed;

the target policy updating subunit specifically comprises: and if the existence strategies in the strategy set are not traversed, continuing to execute the step of selecting other strategies except the target strategy from the strategy set according to a preset sequence.

As an embodiment, in order to further improve the success rate and efficiency of the partial ambiguity fixing and further improve the accuracy of the positioning result, the acquiring unit 301 specifically includes:

the first acquisition subunit is used for sequencing the ambiguities in the ambiguity set according to a target strategy to acquire an ambiguity eliminating sequence;

the second acquisition subunit is used for adjusting the ambiguity eliminating sequence if the ambiguity which participates in the ambiguity fixing process for the first time exists in the ambiguity set, so that the ambiguity which participates in the ambiguity fixing process for the first time is in the sequence which is eliminated first in the adjusted ambiguity eliminating sequence; the ambiguity of the first participation in the ambiguity fixing process refers to the ambiguity included in the satellite of the first participation in the ambiguity fixing process.

As an embodiment, in order to further improve the success rate and efficiency of fixing the partial ambiguity, and further improve the accuracy of the positioning result, the device for fixing the partial ambiguity further includes:

the target ambiguity set acquisition unit is used for eliminating the ambiguities included in the satellites meeting the second condition from the original ambiguity set before acquiring the ambiguity eliminating sequence of the ambiguity set according to the target strategy to obtain a target ambiguity set, and taking the target ambiguity set as the ambiguity set; wherein the original ambiguity set includes all ambiguities to be fixed; the second condition is: the satellite altitude is lower than a preset altitude threshold, the signal to noise ratio of the satellite is lower than a preset signal to noise ratio threshold, and the number of continuous tracking epochs of the satellite is lower than a preset number threshold;

the updating unit 304 specifically includes:

and the ambiguity set updating subunit is used for taking the target ambiguity set as the ambiguity set.

As an embodiment, in order to further improve the success rate and efficiency of the partial ambiguity fixing and further improve the accuracy of the positioning result, the rejection unit 303 specifically includes:

and the fixed ambiguity acquisition subunit is used for carrying out ambiguity fixation on the ambiguity set by utilizing a least square ambiguity reduction correlation method LAMBDA according to the floating ambiguity and the covariance matrix thereof to obtain fixed ambiguity and a Ratio value Ratio thereof.

In one embodiment, to further improve the success rate and efficiency of the partial ambiguity fixing and further improve the accuracy of the positioning result, the second threshold is determined according to 8 and the minimum value of one half of the number of ambiguities included in the initialized ambiguity set.

As an implementation manner, in order to further improve the success rate and efficiency of the partial ambiguity fixing and further improve the accuracy of the positioning result, the first threshold is 4; the third threshold is 3; the fourth threshold is 2.5.

In the specific implementation manner of the partial ambiguity fixing device provided in the embodiment of the present invention, after determining the ambiguity eliminating sequence corresponding to the ambiguity set according to the target policy, if the first condition is met, the ambiguity fixing is performed, and if the Ratio does not exceed the fourth threshold, the partial ambiguity fixing failure is indicated, at this time, the ambiguity eliminating is required to be performed according to the ambiguity eliminating sequence, and the step of determining whether the first condition is met is continued; repeating the steps until the ambiguity fixing is successful or the first condition is not met; if the first condition is not met, the current target strategy is not used in the current application scene, at this time, other strategies except the target strategy can be selected from the strategy set to serve as target strategies, and the step of determining the ambiguity eliminating sequence according to the target strategies is carried out in a returning mode. In the method, the bad influence of the ambiguity with lower precision on the ambiguity fixing process can be reduced after the ambiguity with lower precision is removed from the ambiguity set, so that the fixing success rate of the partial ambiguity can be improved, the partial ambiguity can be quickly fixed, and the precision of a positioning result is improved.

It should be understood that in this application, "at least one" means one or more, and "a plurality" means two or more. "and/or" for describing the association relationship of the association object, the representation may have three relationships, for example, "a and/or B" may represent: only a, only B and both a and B are present, wherein a, B may be singular or plural. The character "/" generally indicates that the context-dependent object is an "or" relationship. "at least one of" or the like means any combination of these items, including any combination of single item(s) or plural items(s). For example, at least one (one) of a, b or c may represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", wherein a, b, c may be single or plural.

The above description is only of the preferred embodiment of the present invention, and is not intended to limit the present invention in any way. While the invention has been described with reference to preferred embodiments, it is not intended to be limiting. Any person skilled in the art can make many possible variations and modifications to the technical solution of the present invention or modifications to equivalent embodiments using the methods and technical contents disclosed above, without departing from the scope of the technical solution of the present invention. Therefore, any simple modification, equivalent variation and modification of the above embodiments according to the technical substance of the present invention still fall within the scope of the technical solution of the present invention.

Claims

1. A method for fixing a partial ambiguity, comprising:

2. The method of claim 1, wherein the set of policies comprises: at least three of a satellite altitude angle screening strategy, a ambiguity variance screening strategy, an integer least squares Bootstrapping success rate screening strategy and a ambiguity precision attenuation factor ADOP screening strategy.

3. The method according to claim 1, wherein the selecting, as the target policy, a policy other than the target policy from a policy set including at least three policies, specifically includes:

4. A method according to claim 3, wherein before selecting the other policies than the target policy from the set of policies in the predetermined order, further comprising:

Judging whether all strategies in the strategy set have been traversed;

5. The method according to claim 1, wherein the obtaining the order of blur degree elimination of the blur degree set according to the target policy specifically includes:

6. The method of claim 1, further comprising, prior to the obtaining the order of blur elimination for the set of blurriers according to the target policy:

7. The method of claim 1, wherein the obtaining the fixed ambiguity and the Ratio thereof according to the floating ambiguity and the covariance matrix thereof specifically comprises:

8. The method of claim 1, wherein the second threshold is determined based on 8 and a minimum of one-half of the number of ambiguities comprised by the initialized set of ambiguities.

9. The method of claim 1, wherein the first threshold is 4; the third threshold is 3; the fourth threshold is 2.5.

10. A partial ambiguity fixing apparatus, comprising: