CN112083464A

CN112083464A - Method and device for fixing partial ambiguity

Info

Publication number: CN112083464A
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: 2020-12-15
Anticipated expiration: 2039-06-14
Also published as: CN112083464B

Abstract

The application discloses a method and a device for fixing partial ambiguity, wherein after an ambiguity elimination sequence is determined according to a target strategy, ambiguity fixing is carried out if a first condition is met, and ambiguity fixing failure is indicated when a Ratio for fixing the ambiguity does not exceed a fourth threshold value, the ambiguity elimination is carried out according to the ambiguity elimination sequence, and whether the first condition is met is continuously judged; repeating the execution until the ambiguity is successfully fixed or the first condition is not met; if the first condition is not met, 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 fuzzy degree elimination sequence according to the target strategy is returned to be executed. Therefore, in order to fully exert the advantage of fixing the partial ambiguity, a proper strategy can be adaptively selected from at least three strategies to screen the ambiguity, the self limitation of each strategy set can be overcome, the advantage complementation is realized, and the success rate of fixing the ambiguity is improved.

Description

Method and device for fixing partial ambiguity

Technical Field

The present application relates to the field of satellite navigation and positioning technologies, and in particular, to a method and an apparatus for fixing 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, due to the complex and variable observation environment, the spatial orientation of each satellite relative to the receiver and the error on the signal propagation path are different, which makes it difficult to fix the ambiguity quickly and reliably.

Further, since it is difficult and unnecessary to fix all ambiguities at once, the position parameters can be updated as long as the partial ambiguities are correctly fixed, and a highly accurate positioning result can be obtained, so that the partial ambiguity fixing is necessary. However, how to realize quick fixing of partial ambiguity is an urgent 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 the partial ambiguity, which can obtain an accurate and reliable ambiguity removing sequence, thereby improving the success rate of fixing the partial ambiguity and further improving the accuracy of a positioning result.

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

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

acquiring a fuzzy degree elimination sequence of a fuzzy degree set according to a target strategy, and judging whether a first condition is met; wherein the first condition is: the number of ambiguities in the ambiguity set is greater than a first threshold, the number of rejected ambiguities is less than a second threshold, and a position precision factor PDOP is less than a third threshold;

when the first condition is met, acquiring the fixed ambiguity and the Ratio value Ratio thereof according to the floating ambiguity and the covariance matrix thereof; if the Ratio exceeds a fourth threshold value, determining that the fixing of the partial ambiguity succeeds, and outputting the fixed ambiguity; if the Ratio does not exceed a fourth threshold, removing the fuzziness to be removed from the fuzziness set according to the fuzziness 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 to serve as the target strategy, and initializing the ambiguity set so as to continuously execute the step of obtaining the ambiguity elimination sequence of the ambiguity set according to the target strategy.

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

Optionally, the selecting, as the target policy, another 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;

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

Optionally, before selecting other policies from the policy set according to the preset sequence, the method further includes:

judging whether all the strategies in the strategy set are traversed or not;

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

and if the existing strategies in the strategy set are not traversed, continuously executing the step of selecting other strategies except the target strategy from the strategy set according to the preset sequence.

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

sorting the fuzziness in the fuzziness set according to a target strategy to obtain a fuzziness elimination sequence;

and if the ambiguity in the ambiguity set exists in the ambiguity fixing process for the first time, adjusting the ambiguity removing sequence to enable the ambiguity in the adjusted ambiguity removing sequence participating in the ambiguity fixing process for the first time to be in the sequence removed first.

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

removing the ambiguity included by the satellite meeting a 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 set of ambiguities comprises all ambiguities to be fixed; 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 initializing the ambiguity set specifically includes: 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 using a least square ambiguity decorrelation method LAMBDA to obtain 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 included in the initialized ambiguity set.

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

The present application further provides a fixing device of partial ambiguity, including:

the acquiring unit is used for acquiring the ambiguity elimination sequence of the ambiguity set according to a target strategy;

a judging unit configured to judge whether a first condition is satisfied; wherein the first condition is: the number of ambiguities in the ambiguity set is greater than a first threshold, the number of rejected ambiguities is less than a second threshold, and a position precision factor PDOP is less than a third threshold;

the removing unit is used for obtaining the fixed ambiguity and the Ratio value Ratio thereof according to the floating ambiguity and the covariance matrix thereof when a first condition is met; if the Ratio exceeds a fourth threshold value, successfully fixing the determined partial ambiguity and outputting the fixed ambiguity; if the Ratio does not exceed a fourth threshold, removing the fuzziness to be removed from the fuzziness set according to the fuzziness 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 and initializing the ambiguity set when the first condition is not met so as to continuously execute the step of acquiring the ambiguity elimination sequence of the ambiguity set according to the target strategy.

Compared with the prior art, the method has the advantages that:

according to the method for fixing the partial ambiguity, after the ambiguity removing sequence corresponding to the ambiguity set is determined according to a target strategy, if the first condition is met, ambiguity fixing is carried out, and if the Ratio does not exceed a fourth threshold value, the partial ambiguity fixing fails, at the moment, the ambiguity removing is carried out according to the ambiguity removing sequence, and whether the first condition is met or not is determined continuously; repeating the execution until the ambiguity is successfully fixed 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 as the target strategy, and the step of determining the fuzzy elimination sequence according to the target strategy is returned to be executed. In the method, after the fuzziness with lower precision is intensively removed from the fuzziness, the adverse effect of the fuzziness with lower precision on the fuzziness fixing process can be reduced, so that the fixing success rate of partial fuzziness can be improved, the rapid fixing of partial fuzziness can be realized, and the precision of a positioning result is improved.

In addition, in order to fully exert the advantage of fixing the partial ambiguity, the method and the device can also adaptively select proper strategies from the strategy set comprising at least three strategies to screen the ambiguity, overcome the self limitation of each strategy set to the maximum extent, complement the advantages and improve the success rate of fixing the ambiguity.

In addition, in the application, because the ambiguity removing sequence is not invariable, the ambiguity removing sequence is updated along with the update of the target strategy, so that the method for fixing the part of the ambiguities can be more suitable for various different application scenes, in particular to dynamic positioning scenes.

Furthermore, a reasonable dynamic ambiguity elimination 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.

Drawings

In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments described in the present application, and other drawings can be obtained by those skilled in the art without creative efforts.

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

fig. 2 is a Ratio and satellite change timing diagram obtained by fixing ambiguity in the LAMBDA algorithm for real-time dynamic data provided by the embodiment of the present application;

fig. 3 is a schematic structural diagram of a fixing device for partial ambiguity provided by an embodiment of the apparatus of the present application.

Detailed Description

In view of the technical problems provided in the background art, the inventors have found through research that, in the process of fixing partial ambiguities, the core problem is how to select an ambiguity subset, that is, how to select an ambiguity subset that can be successfully fixed from an ambiguity set including all ambiguities.

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

However, in the present application, four ambiguity filtering strategies may be used to filter ambiguities in an ambiguity set, wherein the four ambiguity filtering strategies may be: a satellite altitude screening strategy, an Ambiguity variance screening strategy, an integer least square boosting success rate screening strategy (hereinafter, may be simply referred to as boosting success rate), and an Ambiguity resolution of Precision (ADOP) screening strategy.

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

First, the relevant contents of the satellite altitude screening strategy are introduced.

The satellite altitude angle can reflect the precision 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 sorting according to the altitude angles of the satellites, and then sequentially and iteratively removing the ambiguity included by the satellite with the lowest altitude angle from the ambiguity set until the ambiguity is successfully fixed.

It should be noted that, since each satellite may include at least one ambiguity, when performing ambiguity elimination using the satellite altitude screening strategy, at least one ambiguity may be eliminated each time.

The above is the relevant content of the satellite altitude screening strategy.

The relevant contents of the ambiguity variance filtering strategy are described below.

The ambiguity variance is an important index for measuring the precision of the floating ambiguity, and the smaller the variance is, the higher the precision of the floating ambiguity is, the higher the possibility of being fixed is, so that an ambiguity variance screening strategy is provided. The ambiguity variance screening strategy specifically comprises the following steps: firstly, before ambiguity fixing, acquiring covariance matrixes of floating ambiguities corresponding to all satellites, and acquiring variances of all ambiguities according to diagonal elements of the covariance matrixes; then, sorting is carried out according to the variance of each ambiguity, and then the ambiguities are sequentially and iteratively removed from the ambiguity set until the ambiguity is successfully fixed.

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

The related contents of the Bootstrapping success rate screening strategy are described below.

The success rate of ambiguity fixing refers to the probability of fixing the floating ambiguity to the correct integer ambiguity.

The success rate of ambiguity fixing can represent the strength of a GNSS mathematical model, so that the success rate of ambiguity fixing becomes a quantitative index for measuring the probability of ambiguity correct fixing. However, when the Ambiguity fixing is performed using the Least Square Ambiguity Decorrelation (lamb a), since the lamb a algorithm is based on the integer Least squares principle, the calculation of the integer Least squares success rate is obtained by integrating the probability density function of the Ambiguity in its regular domain, and the integration result cannot be directly calculated by the numerical integration method. The Bootstrapping success rate is the lower bound of the integer least squares success rate and is considered as an approximate solution that most closely approximates the integer least squares success rate, and therefore, the 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 using equations (1) to (2).

In the formula, P_sIndicating a boosting success rate;

representing a continuous multiplication operation; n represents the dimension of the ambiguity;

represents the conditional variance of the ith ambiguity fixed to the integer ambiguity with the previous (i-1) floating ambiguity.

Based on the above provided calculation formula of Bootstrapping success rate, the Bootstrapping success rate screening strategy specifically is as follows: when fixing the partial ambiguity, an initial success rate P is preset₀Then, the ambiguity elimination is carried out according to the standard iteration of the ambiguity variance reduction until the fixed success rate exceeds P₀Then, searching and fixing the ambiguity, if the fixing fails, increasing P₀And repeating the steps until the fixation is successful.

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

The relevant content of the ADOP screening strategy is presented below.

ADOP (ambiguity resolution of precision) refers to an ambiguity precision reduction factor that describes the precision level of a floating ambiguity, and can be calculated using equation (3).

In the formula (I), the compound is shown in the specification,

a covariance matrix representing the floating ambiguity; det [. to]Representing a matrix determinant calculation; n denotes the dimension of the floating ambiguity.

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

Therefore, when the ADOP filtering strategy is used to fix the partial ambiguities, if the fixing fails, it is necessary to find out the subset with the minimum ADOP after the removal to perform the LAMBDA fixing in the case of removing one ambiguity from the ambiguity set including all ambiguities, and if the fixing fails again, it is necessary to find out the subset with the minimum ADOP after the removal to perform the LAMBDA fixing in the case of removing two ambiguities from the ambiguity set including all ambiguities until the ambiguity fixing succeeds.

The above is the 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:

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

In order to solve the technical problems described in the background section and the problems further found by the inventor, the embodiment of the present application provides a method for fixing a partial ambiguity, in which, after the ambiguity with lower precision is intensively removed from the ambiguity, the adverse effect of the ambiguity with lower precision on the ambiguity fixing process can be reduced, so that the success rate of fixing the partial ambiguity can be improved, the fixation of the partial ambiguity can be quickly realized, and the precision of a positioning result can be improved. In addition, in order to fully exert the advantage of fixing the partial ambiguity, the method and the device can also adaptively select proper strategies from the strategy set comprising at least three strategies to screen the ambiguity, overcome the self limitation of each strategy set to the maximum extent, complement the advantages and improve the success rate of fixing the ambiguity. In addition, in the application, because the ambiguity elimination sequence is not invariable, the ambiguity elimination sequence is updated along with the update of the target strategy, so that the method for fixing the part of the ambiguity can be more suitable for various different application scenes, in particular to dynamic positioning scenes.

In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

Method embodiment

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

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: a target policy is determined.

S103: and acquiring the ambiguity elimination sequence of the ambiguity set according to a target strategy.

S104: judging whether a first condition is met, if so, executing S105; if not, S109 is executed.

S105: and acquiring 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 value, if so, executing S107; if not, go to S108.

S107: and determining that the fixing of the partial fuzziness is successful, and outputting the fixed fuzziness.

S108: and removing the fuzziness to be removed from the fuzziness set according to the fuzziness removing sequence, and returning to execute the step S104.

S109: selecting, as the target policy, a policy other than the target policy from a policy set including at least three policies.

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

It should be noted that S101 and S102 do not have 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.

In order to facilitate explanation and understanding of the specific implementation of the method for fixing partial ambiguity provided in the first embodiment of the present application method, 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, in the present application, the ambiguity to be fixed is the floating ambiguity.

The ambiguity set can comprise all ambiguities to be fixed, and can also comprise part of ambiguities to be fixed; also, the ambiguity set may be set in advance, for example, the ambiguity set may be set in advance according to an 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 fixing partial ambiguities, all ambiguity sets can be preliminarily screened so as to remove the ambiguities with lower precision. Thus, the present application provides a second embodiment, in which S101 may specifically include S1011 to S1013:

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

S1012: and removing the ambiguities included by the satellites meeting the second condition from the original ambiguity set to obtain a target ambiguity set.

Wherein the second condition is: the altitude angle of the satellite is lower than a preset altitude angle 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 height angle threshold may be preset, for example, the preset height angle threshold may be preset according to an application scenario. As an example, the preset height angle threshold 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 value 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: and taking the target ambiguity set as an ambiguity set.

In the second embodiment of S101, the target ambiguity set from which the ambiguities included in the satellites satisfying the second condition are removed may be used as the ambiguity set, so as to improve the accuracy of the ambiguities included in the ambiguity set, which is beneficial to improving the success rate of fixing the partial ambiguities.

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

A specific embodiment of S102 is described below.

In S102, the target policy is a policy for sorting the ambiguities according to factors affecting the accuracy of the ambiguities, so that the sorted 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 present application, the target policy used for the first time may be set in advance, for example, may be set in advance according to an application scenario. In this case, the present application provides an embodiment of S102, in which S102 may specifically be: determining a preset strategy as a target strategy; the preset strategy is preset, and the preset strategy is any one strategy included in the strategy set.

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

In addition, because different epochs have different correlations, the target policy used for the first time can also be the policy used when the ambiguity fixing of the previous epoch data succeeds. In this case, the present application provides an embodiment of S102, in which S102 may specifically be: and taking the 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 using 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 implementation of S102, in this implementation, 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 fuzzy degree iterative rejection times are reduced, the fuzzy degree fixing time is shortened, and the fuzzy degree fixing efficiency is improved.

A specific embodiment of S103 is described below.

In S103, the ambiguity set includes a plurality of ambiguities to be fixed. The ambiguity culling order corresponds to the ambiguity set, and the ambiguity culling order is used to describe different culled orders corresponding to different ambiguities during ambiguity fixing.

Since different target strategies will obtain different ambiguity elimination sequences, the following describes four specific embodiments of determining the ambiguity elimination sequence by different target strategies in turn.

First, a first embodiment of S103 will be described.

As a first implementation, when the target policy is a satellite altitude screening policy, 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 angles to obtain an ambiguity elimination sequence.

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

As an example, S103 may specifically be: and according to a satellite altitude angle screening strategy, sequencing all the ambiguities in the ambiguity set from large to small according to the altitude angle to obtain an ambiguity elimination sequence, so that in the process of ambiguity elimination, elimination is started from the last ambiguity in the ambiguity elimination sequence.

As another example, S103 may specifically be: and according to a satellite altitude angle screening strategy, sequencing all the ambiguities in the ambiguity set from small to large according to the altitude angles to obtain an ambiguity elimination sequence, so that in the process of ambiguity elimination, elimination is started from the most previous ambiguity in the ambiguity elimination sequence.

The above is the first embodiment of S103.

Next, a second embodiment of S103 will be described.

As a second implementation, when the target policy is an ambiguity variance filtering policy, S103 may specifically be: and sequencing all the fuzziness in the fuzziness set according to the fuzziness variance screening strategy and the fuzziness variance to obtain a fuzziness elimination sequence.

In this embodiment, since the smaller the ambiguity variance of the floating ambiguity, the higher the precision of the floating ambiguity, and the greater the possibility of being fixed, the second embodiment of S103 provided in this application corresponds to two examples.

As an example, S103 may specifically be: and according to the fuzzy degree variance screening strategy, sequencing all the fuzzy degrees in the fuzzy degree set from small to large according to the fuzzy degree variance to obtain a fuzzy degree rejection sequence, so that in the fuzzy degree rejection process, the rejection is started from the last fuzzy degree in the fuzzy degree rejection sequence.

As another example, S103 may specifically be: and according to the fuzzy degree variance screening strategy, sequencing all fuzzy degrees in the fuzzy degree set from large to small according to the fuzzy degree variance to obtain a fuzzy degree rejection sequence, so that in the fuzzy degree rejection process, the rejection is carried out from the most front fuzzy degree in the fuzzy degree rejection sequence.

The above is the second embodiment of S103.

Next, a third embodiment of S103 will be described.

As a third implementation manner, when the target policy is a Bootstrapping success rate screening policy, then S103 may specifically be: and sorting the ambiguities in the ambiguity set according to the Bootstrapping success rate screening strategy to obtain an ambiguity elimination sequence.

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

As an example, S103 may specifically be: and according to a boosting success rate screening strategy, sequencing the ambiguities in the ambiguity set from large to small according to the boosting success rate to obtain an ambiguity elimination sequence, so that in the process of eliminating the ambiguities, eliminating is carried out from the most posterior ambiguity in the ambiguity elimination sequence.

As another example, S103 may specifically be: and according to a boosting success rate screening strategy, sequencing the ambiguities in the ambiguity set from small to large according to the boosting success rate to obtain an ambiguity elimination sequence, so that in the process of eliminating the ambiguities, the elimination is carried out from the most previous ambiguity in the ambiguity elimination sequence.

The above is the third embodiment of S103.

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 ambiguity with the preset number needs to be removed from the ambiguity set, the ADOP values of the ambiguity subsets with the different removed ambiguity with the preset number can be sorted according to the ADOP screening strategy, so as to obtain the ambiguity removal sequence.

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

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

In this embodiment, since the smaller the ADOP value of the floating ambiguity, the higher the average precision of the ambiguity, and the easier the overall fixing, the fourth embodiment of S103 is provided in this application as two examples.

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

As another example, S103 may specifically be: calculating ADOP values corresponding to all ambiguity subsets after the ambiguity with the preset number is removed according to an ADOP screening strategy; and sequencing the fuzzy degree subsets after the elimination of the preset number of fuzzy degrees according to the ADOP value from small to large to obtain a fuzzy degree elimination sequence corresponding to the fuzzy degree subsets after the elimination of the preset number of fuzzy degrees, so that the most-expensive preset number of fuzzy degrees in the fuzzy degree elimination sequence are eliminated in the elimination process of the preset number of fuzzy degrees.

It should be noted that, in the present application, the process of screening ambiguity using the ADOP screening strategy is different from the process of screening ambiguity using the above three screening strategies, and the difference is as follows: when the three screening strategies are used for screening the fuzziness, the fuzziness set according to which the fuzziness removing process of the current round is based on the fuzziness set obtained after the fuzziness removing action is executed in the previous round of fuzziness removing process; however, when the ADOP filtering strategy is used to perform the ambiguity filtering, the ambiguity set to be used in each ambiguity elimination process is the ambiguity set obtained in S101 (or the initialized ambiguity set).

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

The above is the fourth embodiment of S103.

In the above four embodiments of S103, in these embodiments, the ambiguity removing order of the ambiguity set may be obtained according to the target policy, so that the ambiguity to be removed can be subsequently removed from the ambiguity set according to the ambiguity removing order.

In addition, through further research, the following results are found: in dynamic positioning, the number of satellites participating in the ambiguity fixing process is increased due to the initial rising of the satellites and the satellites with interruption or cycle slip resetting ambiguity, and when the satellites participate in the ambiguity fixing process for the first time, the Ratio value of ambiguity detection is suddenly reduced and even is lower than a preset threshold value, so that ambiguity fixing failure is caused.

For convenience of explanation and understanding of the problems generated when a satellite in initial rise and a satellite in which an interruption or cycle slip resetting ambiguity occurs first participate in the ambiguity fixing process, reference will be made to fig. 2, where fig. 2 is a Ratio and satellite change timing chart obtained by fixing ambiguity by using the lamb da algorithm for real-time dynamic data provided by the embodiment of the present application.

In FIG. 2, GPST is used to represent the intra-week seconds of the Global positioning System GPS time System; NSAT is used to indicate the number of satellites participating in a fix; ratio represents a proportional value of a quadratic form of the sub-optimal solution and the optimal solution in which the fixed ambiguity is fixed. In addition, as can be seen from the data shown in fig. 2, each epoch of the Ratio value is corresponding to the increase of the number of satellites, and the main reason is that the variance of the ambiguity included in the satellite which participates in the ambiguity fixing process for the first time is large, which destroys the overall accuracy of the floating ambiguity which has been filtered for a long time originally, and causes the differentiability between the optimal solution and the sub-optimal solution of the ambiguity to be seriously reduced, which means that the Ratio value is greatly reduced.

Based on the above, in order to avoid the problem that the ambiguity is partially reduced when the initial satellite and the satellite having the interruption or the cycle slip reset ambiguity first participate in the ambiguity fixing process, the ambiguity included in the satellite which first participates in the ambiguity fixing process can be preferentially removed from the ambiguity set. Thus, the present application provides a fifth embodiment of S103, in which S103 may specifically be:

s1031: and sequencing the fuzziness in the fuzziness set according to a target strategy to obtain a fuzziness elimination sequence.

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

S1032: and if the ambiguity in the ambiguity set exists in the ambiguity fixing process for the first time, adjusting the ambiguity removing sequence to enable the ambiguity in the adjusted ambiguity removing sequence participating in the ambiguity fixing process for the first time to be in the sequence removed first.

The ambiguity of the first participation in the ambiguity fixing process can be determined according to the ambiguity corresponding to the satellite participating in the ambiguity fixing process for the first time. In addition, satellites that first participate in the ambiguity fixing process may include first-rise satellites, satellites that reset ambiguities due to an interruption or a cycle slip.

It should be noted that, when using the ionosphere-free mode solution, since the fixing of the narrow-lane (original) ambiguity is limited by the fixing of the widelane ambiguity, the satellite whose widelane 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 culling order obtained in S1031, the ambiguities that are earlier in the ranking position are culled later, and the ambiguities that are later in the ranking position are culled earlier, when the first satellite including the first ambiguity is a satellite that first participates in the ambiguity fixing process, and the first ambiguity is located at the twenty-third position in the ambiguity culling order, S1032 may specifically be: and moving the first ambiguity from the twenty-third position to the thirtieth position in the ambiguity removing sequence obtained in the step S1031, so that the first ambiguity is firstly removed from the ambiguity set in the subsequent ambiguity removing process.

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

In the above five specific embodiments of S103, in this embodiment, the ambiguity removing order of the ambiguity set may be obtained according to the target policy, so that the ambiguity to be removed can be subsequently removed from the ambiguity set according to the ambiguity removing order.

A specific embodiment of S104 is described below.

In S104, the first condition is: the number of ambiguities in the ambiguity set is greater than a first threshold, the number of removed ambiguities is less than a second threshold, and a Position Precision factor (PDOP) is less than a third threshold

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

The second threshold may be set in advance, for example, the second threshold may be set in advance according to an application scenario. In addition, in order to further ensure the 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 the content of the first and second substances,T₂represents 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 ambiguity set.

The third threshold may be set in advance, for example, the third threshold may be set in advance according to an application scenario. As an example, the third threshold value may be set to 3 in advance.

The above is the content related to the first condition.

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

If the first condition cannot be met, it indicates that the ambiguity set which can be successfully fixed cannot be found under the current target strategy, and thus indicates that the current target strategy is not suitable for fixing partial ambiguity, at this time, other strategies need to be replaced to continuously find the ambiguity set which can be successfully fixed. Therefore, the defect that the ambiguity set which can be successfully fixed cannot be found due to the self limitation of a single strategy can be overcome, so that the respective limitations are overcome to the maximum extent, the advantages are complementary, and the success rate of ambiguity fixing is integrally improved.

The above is a specific embodiment of S104.

A specific embodiment of S105 is described below.

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 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 fuzzy degree fixed suboptimal solution and an optimal solution; the Ratio represents the distinguishable degree of the ambiguity fixed optimal solution and the suboptimal solution, and can be used for reflecting the probability that the obtained fixed ambiguity can be successfully fixed, wherein if the Ratio is larger, the probability that the obtained fixed ambiguity can be successfully fixed is higher; if the Ratio is smaller, it is less likely that the obtained fixing ambiguity can be successfully fixed.

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

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

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 Ratio 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 an application scenario. As an example, the fourth threshold value may be set to 2.5 in advance.

If the Ratio exceeds the fourth threshold, it indicates that the obtained fixed ambiguity is sufficiently high in the possibility of successful fixing, and other fixed ambiguities do not need to be obtained again, so that the fixed ambiguity exceeding the fourth threshold can be output or stored.

If the Ratio does not exceed the fourth threshold, it indicates that the ambiguity fixing fails, and then it needs to try to perform the next round of fixing ambiguity according to the ambiguity set after the ambiguity elimination, so as to obtain a better fixed ambiguity, and finally obtain an ambiguity set with successful fixing.

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 the fixing of the partial ambiguity succeeds, outputting the fixed ambiguity, and storing the fixed ambiguity.

In addition, in order to enable the policy used in the current determination of the partial ambiguity to be used in the next acquisition process of the partial ambiguity, the present application further provides another embodiment of S107, where S107 specifically may be: and determining that the fixing of the partial ambiguity is successful, outputting the fixed ambiguity, and storing the fixed ambiguity and the 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 fixing of the partial ambiguity succeeds, and the fixed ambiguity is output.

A specific embodiment of S108 is described below.

In S108, the to-be-deblurred ambiguities are ambiguities that should be removed from the ambiguity set in the current state, determined according to the ambiguity removal order. Therefore, the ambiguity to be eliminated can be determined according to the ambiguity elimination sequence and the ambiguity set.

For ease of explanation and understanding of the ambiguity to be resolved, the following description will be made in conjunction with two examples.

As a first example, it is assumed that the ambiguity set includes 30 ambiguities, the target policy is any one of a satellite altitude screening policy, an ambiguity variance screening policy, and a Bootstrapping success rate screening policy, and in the ambiguity elimination sequence obtained in S103, the ambiguity closer to the front is eliminated later, and the ambiguity closer to the back is eliminated earlier, so that based on the assumption, when the target policy is not replaced, in the first ambiguity elimination process, the ambiguity to be eliminated is the ambiguity corresponding to the thirtieth position in the ambiguity elimination sequence; in the second process of removing ambiguity, the ambiguity to be removed is the ambiguity … … corresponding to the twenty-ninth position in the ambiguity removing 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 elimination order obtained in S103, the earlier ambiguity of the ranking position is eliminated later, and the later ambiguity of the ranking position is eliminated earlier, based on the above assumptions, when the target policy is not changed, the ambiguity to be eliminated is the ambiguity at the thirtieth position in the ambiguity elimination order in the first ambiguity elimination procedure, and the ambiguity elimination order is determined according to each ambiguity subset after eliminating 1 ambiguity; in the second process of removing ambiguities, the ambiguities to be removed are the ambiguities at the thirtieth and twenty-ninth positions in the ambiguity removal sequence, and the ambiguity removal sequence is … … (analogized in turn) determined according to each ambiguity subset 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 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 the strategy set according to a preset selection method as the target strategy.

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 randomly selected.

For convenience of explanation and understanding of the first embodiment of S109, the following description will be made with reference to an example.

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

In the first embodiment of S109, in this embodiment, other policies than the target policy may be selected from the policy set according to a preset selection method 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 an application scenario.

For convenience of explanation and understanding of the second embodiment of S109, the following description will be made with reference to an example.

By way of example, assume that a policy set includes: satellite altitude angle screening strategy, ambiguity variance screening strategy, Bootstrapping success rate screening strategy and ADOP screening strategy; moreover, the preset sequence can be that a satellite altitude angle screening strategy, an ambiguity variance screening strategy, a Bootstrapping success rate screening strategy and an ADOP screening strategy are sequentially and circularly selected.

Based on the above assumptions, if the current target policy is the satellite altitude screening policy, 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 a satellite altitude angle screening strategy from the strategy set as the target strategy according to a preset sequence.

In the second embodiment of S109, other policies than the target policy may be selected from the policy set in a preset order as the target policy.

In addition, in order to improve the efficiency of ambiguity fixing, the process of fixing some ambiguities may be ended after all policies in the policy set are traversed, and therefore, the present application further provides a third implementation manner of S109, where S109 may specifically be:

s1091: judging whether all the strategies in the strategy set are traversed or not, if so, executing S1092; if not, S1093 is executed.

The strategy set is traversed, that is, each strategy included in the strategy set is used for finding a fixed ambiguity which can fix success.

By way of example, assume that a policy set includes: when a satellite altitude angle screening strategy, an ambiguity variance screening strategy, a Bootstrapping success rate screening strategy and an ADOP screening strategy are used, 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 in the process of searching for the fixed ambiguity which can be successfully fixed, then all the strategies in the strategy set can be determined to be traversed.

S1092: and determining that the ambiguity fixing fails, and outputting the 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 implementation manner of S1093 is the same as the second implementation manner of S109, and for the sake of brevity, will not be described again.

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

In the above three embodiments of S109, in these embodiments, 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 step S101, so that the initialized ambiguity set is the same as the ambiguity set determined in step S101, and for convenience of explanation and illustration, two examples will be described below.

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 taken as the ambiguity set in S101, the "initializing the ambiguity set" in S110 may specifically be: and taking the target ambiguity set as an ambiguity set.

The above is a specific implementation of S110, in this implementation, 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 this embodiment, after determining a fuzzy degree rejection sequence corresponding to a fuzzy degree set according to a target strategy, if a first condition is satisfied, fixing the fuzzy degree, and if the Ratio does not exceed a fourth threshold, indicating that fixing the partial fuzzy degree fails, at this time, the fuzzy degree needs to be rejected according to the fuzzy degree rejection sequence, and whether the first condition is satisfied is determined; repeating the execution until the ambiguity is successfully fixed 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 as the target strategy, and the step of determining the fuzzy elimination sequence according to the target strategy is returned to be executed. In the method, after the fuzziness with lower precision is intensively removed from the fuzziness, the adverse effect of the fuzziness with lower precision on the fuzziness fixing process can be reduced, so that the fixing success rate of the partial fuzziness can be improved, the fixation of the partial fuzziness can be quickly realized, and the precision of a positioning result is improved.

In addition, the method also adopts a mode of combining the coarse screening (step S1012) based on the satellite level and the fine screening (step S103 to step S110) based on the ambiguity level, thereby effectively avoiding the waste of observation information and enhancing the reliability of ambiguity fixing; meanwhile, a reasonable dynamic ambiguity elimination sequence is constructed according to the actual characteristics of dynamic positioning scenes and data, and the method is more suitable for the actual dynamic positioning situation compared with the prior art; in addition, in order to fully exert the advantage of fixing the partial ambiguity, at least three screening strategies are adopted for screening in an adaptive mode, the respective limitations are overcome to the maximum extent, the advantages are complemented, and the success rate of fixing the ambiguity is improved. The results of a large amount of data show that compared with the prior art, the method for fixing the partial ambiguity can effectively improve the performance of the fixing solution, the improvement degree is about 5-10% for the epoch fixing rate, the first fixing time of the medium and long baselines is shortened from 33.4 minutes to 27.1 minutes on average, and 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 explained below with reference to the accompanying drawings.

Device embodiment

Referring to fig. 3, the figure is a schematic structural diagram of a fixing device for partial ambiguity provided by an embodiment of the device of the present application.

The fixing device of partial ambiguity that this application embodiment provided includes:

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

a judging unit 302 for judging whether a first condition is satisfied; wherein the first condition is: the number of ambiguities in the ambiguity set is greater than a first threshold, the number of rejected ambiguities is less than a second threshold, and a position precision factor PDOP is less than a third threshold;

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

an updating unit 304, configured to, when the first condition is not satisfied, select another policy other than 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 elimination order of the ambiguity set according to the target policy.

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 policy set includes: the satellite altitude angle screening strategy, the ambiguity variance screening strategy, the integer least square Bootstrapping success rate screening strategy and the ambiguity precision attenuation factor ADOP screening strategy are at least three.

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 updating unit 304 specifically includes:

a target strategy updating subunit, configured to select, according to a preset sequence, other strategies from the strategy set except the target strategy as the target strategy;

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 updating unit 304 further includes:

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

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

the target policy updating subunit specifically includes: and if the existing strategies in the strategy set are not traversed, continuously executing the step of selecting other strategies except the target strategy from the strategy set according to the preset sequence.

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 obtaining unit 301 specifically includes:

the first obtaining subunit is used for sequencing the fuzziness in the fuzziness set according to a target strategy to obtain a fuzziness removing sequence;

a second obtaining subunit, configured to, if there is a ambiguity in the ambiguity set that first participates in the ambiguity fixing process, adjust the ambiguity removing order so that, in the adjusted ambiguity removing order, the ambiguity that first participates in the ambiguity fixing process is in an order of being removed first; the ambiguity participating in the ambiguity fixing process for the first time refers to the ambiguity included by the satellite participating in the ambiguity fixing process for the first time.

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 apparatus for fixing the partial ambiguity further includes:

the target ambiguity set acquiring unit is used for eliminating the ambiguities included by the satellites meeting the second condition from the original ambiguity set to obtain a target ambiguity set before acquiring the ambiguity elimination sequence of the ambiguity set according to a target strategy, and taking the target ambiguity set as the ambiguity set; wherein the original set of ambiguities comprises all ambiguities to be fixed; 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 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 fixing the partial ambiguity, and further improve the accuracy of the positioning result, the removing unit 303 specifically includes:

and the fixed ambiguity acquiring subunit is used for carrying out ambiguity fixing on the ambiguity set by using a least square ambiguity decorrelation method LAMBDA according to the floating ambiguity and the covariance matrix thereof to obtain a fixed ambiguity and a Ratio value Ratio thereof.

As an embodiment, in order to further improve the success rate and efficiency of fixing the partial ambiguities 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 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 first threshold is 4; the third threshold is 3; the fourth threshold is 2.5.

In the specific implementation manner of the fixing device for partial ambiguity provided above for the device embodiment, after determining the ambiguity elimination sequence corresponding to the ambiguity set according to the target policy, if the first condition is satisfied, the ambiguity is fixed, and when the Ratio does not exceed the fourth threshold, it indicates that the partial ambiguity fails to be fixed, at this time, the ambiguity elimination needs to be performed according to the ambiguity elimination sequence, and the step of determining whether the first condition is satisfied is continued; repeating the execution until the ambiguity is successfully fixed 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 as the target strategy, and the step of determining the fuzzy elimination sequence according to the target strategy is returned to be executed. In the method, after the fuzziness with lower precision is intensively removed from the fuzziness, the adverse effect of the fuzziness with lower precision on the fuzziness fixing process can be reduced, so that the fixing success rate of the partial fuzziness can be improved, the fixation of the partial fuzziness can be quickly realized, and the precision of a positioning result is improved.

It should be understood that in the present application, "at least one" means one or more, "a plurality" means two or more. "and/or" for describing an association relationship of associated objects, indicating that there may be three relationships, e.g., "a and/or B" may indicate: only A, only B and both A and B are present, wherein A and B may be singular or plural. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. "at least one of the following" or similar expressions refer to any combination of these items, including any combination of single item(s) or plural items. 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 foregoing is merely a preferred embodiment of the invention and is not intended to limit the invention in any manner. Although the present invention has been described with reference to the preferred embodiments, it is not intended to be limited thereto. Those skilled in the art can make numerous possible variations and modifications to the present teachings, or modify equivalent embodiments to equivalent variations, without departing from the scope of the present teachings, using the methods and techniques disclosed above. Therefore, any simple modification, equivalent change and modification made to the above embodiments according to the technical essence of the present invention are still within the scope of the protection of the technical solution of the present invention, unless the contents of the technical solution of the present invention are departed.

Claims

1. A method for fixing partial ambiguities, comprising:

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

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

4. The method of claim 3, wherein prior to selecting the other policies from the set of policies in the predetermined order, further comprising:

judging whether all the strategies in the strategy set are traversed or not;

5. The method according to claim 1, wherein the obtaining of the ambiguity elimination order of the ambiguity set according to the target strategy specifically comprises:

6. The method of claim 1, wherein before the obtaining the ambiguity culling order of the ambiguity set according to the target strategy, further comprising:

7. The method according to claim 1, wherein the obtaining the fixed ambiguity and the Ratio value 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 according to 8 and a minimum value of one-half of the number of ambiguities included in the initialized ambiguity set.

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: