Carrier differential positioning solution method and device

Abstract

The present application provides a carrier differential positioning solution method and device, including: calculating the difference between the pseudo-range difference of the two epochs before and after and the Doppler reference value in n epochs of multiple satellites, and normalizing the difference results of the first m epochs; training the parameters of the kernel function of the support vector machine and the model parameters to obtain a prediction model; using the prediction model to predict the difference results of the next n-m epochs to obtain a prediction value, and performing a denormalization process on the prediction value to obtain a prediction result; obtaining the number of epochs in which the prediction result and the difference result have the same positive and negative signs in the same epoch; marking the satellite when the number of epochs consistent with a certain satellite is greater than or equal to a first threshold; obtaining the number of unmarked satellites, and when the number of unmarked satellites is greater than or equal to a second threshold, using the carrier of the unmarked satellite to perform ambiguity fixation and carrier differential positioning solution. The present invention can eliminate multipath signals and improve the fixation rate and positioning accuracy.

Description

Technical Field

[0001] The present specification generally relates to the field of navigation and positioning technology, and more particularly to a carrier differential positioning solution method and a device thereof. Background Art

[0002] In GNSS carrier differential positioning, the error of carrier phase observations caused by multipath in the mobile station can reach several centimeters, while the multipath errors of the base station and the mobile station are not correlated, which makes it impossible to eliminate the multipath errors by differential means. When the integer ambiguity is fixed, due to the existence of multipath errors in the carrier phase observations, the integer characteristics of the double difference ambiguity are poor, which makes it difficult to fix the integer ambiguity. Whether the integer ambiguity can be successfully fixed directly determines whether high-precision positioning at the centimeter level or even the millimeter level can be performed. Therefore, in carrier differential positioning, the processing of multipath carrier phase observations becomes particularly important. Summary of the invention

[0003] In order to solve the technical problems in the prior art, the present application provides a carrier differential positioning solution method to eliminate multipath signals and improve the integer ambiguity fixation rate and positioning accuracy in a multipath environment.

[0004] In one embodiment of the present application, a carrier differential positioning solution method is disclosed, including:

[0005] Calculate the difference between the pseudorange difference between each epoch of n epochs of multiple satellites and the previous epoch and the Doppler reference value, and normalize the difference results of the first m epochs of the n epochs of the multiple satellites;

[0006] Training the parameters of the kernel function of the support vector machine and the model parameters of the support vector machine according to the difference results of the first m epochs after the normalization process to obtain a prediction model;

[0007] Using the prediction model to predict the difference results of the last nm epochs of the n epochs of the multiple satellites to obtain predicted values, and performing a denormalization process on the predicted values ​​to obtain a predicted result;

[0008] Obtaining the number of epochs in which the prediction results and the difference results of the last nm epochs of the multiple satellites have the same positive and negative signs as the prediction results and the difference results in the same epoch;

[0009] When the number of epochs in which the symbols of any satellite of the plurality of satellites are consistent is greater than or equal to a first threshold, marking the satellite;

[0010] The number of unmarked satellites among the multiple satellites is obtained, and when the number of unmarked satellites is greater than or equal to a second threshold, the carriers of the unmarked satellites are used to perform ambiguity fixation and carrier differential positioning solution.

[0011] In a preferred example, when the number of unmarked satellites is less than the second threshold, the carrier of the epoch corresponding to the sign change of the difference result of the marked satellite is obtained, and is used to perform ambiguity fixation and carrier differential positioning solution in conjunction with the carrier of the unmarked satellite.

[0012] In a preferred example, in the step of normalizing the differences of the first m epochs among the n epochs of the multiple satellites, the normalized value is calculated using the following formula:

[0013]

[0014] in, is the normalized value, X i is the difference result of the i-th epoch among the n epochs of the multiple satellites, x max and x min are respectively the maximum and minimum values ​​of the difference results in the n epochs.

[0015] In a preferred example, the kernel function adopts any one of the following: RBF kernel function, radial basis kernel function, polynomial kernel function, linear kernel function, neural network kernel function.

[0016] In a preferred example, the Doppler reference value is the Doppler average value of each epoch in the n epochs and its previous epoch.

[0017] In a preferred example, the method is used for real-time carrier differential positioning solution or post-processing carrier differential positioning solution.

[0018] Another embodiment of the present application further discloses a carrier differential positioning solution device, comprising:

[0019] A difference result calculation module is configured to calculate a difference result between a pseudorange difference value of each epoch in n epochs of multiple satellites and a previous epoch and a Doppler reference value, and normalize the difference results of the first m epochs in the n epochs of the multiple satellites;

[0020] A training module is configured to train the parameters of the kernel function of the support vector machine and the model parameters of the support vector machine according to the difference results of the first m epochs after the normalization process to obtain a prediction model;

[0021] A prediction module is configured to use the prediction model to predict the difference results of the last nm epochs of the n epochs of the multiple satellites to obtain a prediction value, and perform a denormalization process on the prediction value to obtain a prediction result;

[0022] A statistical module is configured to obtain the number of epochs in which the prediction results and the difference results of the last nm epochs of the multiple satellites have the same positive and negative signs;

[0023] a marking module configured to mark any satellite of the plurality of satellites when the number of epochs for which the symbols of the satellite are consistent is greater than or equal to a first threshold;

[0024] The solution module is configured to obtain the number of unmarked satellites among the multiple satellites, and when the number of unmarked satellites is greater than or equal to a second threshold, use the carriers of the unmarked satellites to perform ambiguity fixation and carrier differential positioning solution.

[0025] In a preferred example, the solution module is further configured to: when the number of unmarked satellites is less than the second threshold, obtain the carrier of the epoch corresponding to the sign of the difference result of the marked satellite when the sign changes, and use it to jointly perform ambiguity fixation and carrier differential positioning solution with the carrier of the unmarked satellite.

[0026] In another embodiment of the present application, a carrier differential positioning solution device is also disclosed, including:

[0027] a memory for storing computer executable instructions; and

[0028] A processor, coupled to the memory, is configured to implement the steps in the method described above when executing the computer executable instructions.

[0029] In another embodiment of the present application, a computer-readable storage medium is disclosed. The computer-readable storage medium stores computer-executable instructions. When the computer-executable instructions are executed by a processor, the steps in the method described above are implemented.

[0030] Compared with the prior art, this application has the following beneficial effects:

[0031] In the present invention, the time series of the difference between the pseudorange variation and the Doppler mean is used for analysis. The stability of the Doppler is much more stable than that of the carrier, and intermittent unavailability rarely occurs. The time series analysis of the difference between the pseudorange variation and the Doppler mean is performed by a support vector machine, and the accuracy of judging whether it is periodic is high, which greatly reduces the situation of misjudging normal signals as multipath signals. The peak-shifting characteristics of the extreme points of the pseudorange multipath error and the carrier multipath error are used to judge the availability of the multipath carrier, and when the whole cycle ambiguity is fixed, it is ensured that the multipath signal with poor quality will not participate in the ambiguity fixation, thereby improving the fixation rate and positioning accuracy in the multipath environment.

[0032] A large number of technical features are recorded in this specification, which are distributed in various technical solutions. If all possible combinations of technical features of this application (i.e., technical solutions) are to be listed, the specification will be too long. In order to avoid this problem, the various technical features disclosed in the above invention content of this specification, the various technical features disclosed in the various embodiments and examples below, and the various technical features disclosed in the accompanying drawings can be freely combined with each other to form various new technical solutions (these technical solutions should all be deemed to have been recorded in this specification), unless such a combination of technical features is technically infeasible. For example, in one example, feature A+B+C is disclosed, and in another example, feature A+B+D+E is disclosed, and features C and D are equivalent technical means that play the same role. Technically, only one can be used, and it is impossible to use them at the same time. Feature E can be combined with feature C technically. Then, the solution of A+B+C+D should not be deemed to have been recorded because it is technically infeasible, and the solution of A+B+C+E should be deemed to have been recorded. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 It is a flow chart of a carrier differential positioning solution method according to one embodiment of the present specification.

[0034] Figure 2 It is a detailed flow chart of a carrier differential positioning solution method according to one embodiment of the present specification.

[0035] Figure 3 It is a block diagram of a carrier differential positioning solution device according to one embodiment of the present specification. DETAILED DESCRIPTION

[0036] In the following description, many technical details are provided to help readers better understand the present application. However, those skilled in the art can understand that the technical solution claimed in the present application can be implemented even without these technical details and various changes and modifications based on the following embodiments.

[0037] Description of some concepts:

[0038] Ambiguity of Whole Cycles: Also known as the whole cycle unknown, it is the whole cycle unknown corresponding to the first observed value of the phase difference between the carrier phase and the reference phase when measuring the carrier phase of the global positioning system technology.

[0039] Support Vector Machine (SVM): A type of generalized linear classifier that performs binary classification of data in a supervised learning manner. Its decision boundary is the maximum margin hyperplane solved for the learning sample. The principle of using support vector machines to determine whether data has periodic changes is that support vector machines can predict time series. The prediction problem is essentially equivalent to the regression estimation problem. This is because the purpose of prediction is to try to find a function to determine the relationship between future values ​​and past values. In other words, prediction is to use past and current observations to estimate future values. This is actually based on the assumption that there is a certain functional relationship between future values ​​and past values. Time series prediction is to use the previous data as the cause of the later data, and the later data is the result of the previous data. The training process is to find the relevant functional relationship between them. Support vector machines can solve the problem of nonlinear data characteristics and perform well in small sample spaces.

[0040] Mathematical symbolic function (Sign): It can isolate the sign of a function. In mathematics and computer operations, its function is to take the sign (positive or negative) of a number.

[0041] Cross-validation: Mainly used in modeling applications, such as PCR and PLS regression modeling. In a given modeling sample, most of the samples are taken out for model building, and a small number of samples are reserved for prediction using the newly established model. The prediction error of this small number of samples is calculated, and their square sum is recorded.

[0042] In order to make the objectives, technical solutions and advantages of the present application clearer, the implementation methods of the present application will be further described in detail below in conjunction with the accompanying drawings.

[0043] In one embodiment of the present application, a carrier differential positioning solution method is disclosed. Figure 1 Flow chart of the carrier differential positioning solution method. The method includes:

[0044] Step 101, calculate the difference between the pseudorange difference of each epoch in n epochs of each satellite of multiple satellites and the previous epoch and the Doppler reference value, and normalize the difference results of the first m epochs in the n epochs of the multiple satellites.

[0045] In one embodiment, in the step of normalizing the difference values ​​of the first m epochs among the n epochs of the plurality of satellites, the normalized value is calculated using the following formula:

[0046]

[0047] in, is the normalized value, Xi is the difference result of the i-th epoch among the n epochs of the multiple satellites, x max and x min are respectively the maximum and minimum values ​​of the difference results in the n epochs.

[0048] In one embodiment, the Doppler reference value is the Doppler average value of each epoch in the n epochs and the previous epoch. It should be understood that in other embodiments of the present invention, when calculating the difference result, the Doppler reference value can also take other values, for example, it can be the Doppler of the corresponding epoch, or the Doppler average value of n epochs, or the Doppler value of any epoch in the n epochs.

[0049] Step 102, training the parameters of the kernel function of the support vector machine and the model parameters of the support vector machine according to the difference results of the first m epochs after the normalization process to obtain a prediction model. In one embodiment, the kernel function adopts any one of the following: RBF kernel function, radial basis kernel function, polynomial kernel function, linear kernel function, neural network kernel function. In this embodiment, the kernel function selects the RBF kernel function.

[0050] Step 103: Use the prediction model to predict the difference results of the last nm epochs of the n epochs of the multiple satellites to obtain predicted values, and perform denormalization processing on the predicted values ​​to obtain predicted results.

[0051] In one embodiment, in the step of performing denormalization processing on the predicted value to obtain the predicted result, the following formula is used to calculate the predicted result:

[0052]

[0053] Among them, X i is the prediction result after anti-normalization, is the predicted value of the ith epoch in the last nm epochs among the n epochs of the plurality of satellites, x max and x min are the maximum and minimum values ​​of the difference results respectively.

[0054] Step 104, for the prediction results and difference results of the last nm epochs of the plurality of satellites, obtain the number of epochs in which the prediction results and difference results have the same positive and negative signs. In one embodiment, the prediction results and difference results of the last nm epochs of the plurality of satellites are converted using a mathematical sign function.

[0055] Step 105, when the number of epochs of the symbols of any satellite of the plurality of satellites being consistent is greater than or equal to the first threshold, the satellite is marked. It should be understood that when the number of consistent epochs is greater than or equal to the first threshold, it is considered that the time series of the current difference result has periodicity, and the observed quantity (pseudorange, carrier) is a multipath signal. In one embodiment, the first threshold can be set to 0.8 (nm) to 0.9 (nm), for example, if nm is 20, then the first threshold can be set to 16 to 18.

[0056] Step 106, obtaining the number of unmarked satellites in the plurality of satellites, when the number of unmarked satellites is greater than or equal to the second threshold, using the carrier of the unmarked satellite to perform ambiguity fixation and carrier differential positioning solution. It can be understood that the number of unmarked satellites is the satellites without multipath signals, when the number of satellites without multipath signals is greater than the second threshold, using the carrier signals of these satellites without multipath signals to perform ambiguity fixation and perform carrier differential positioning solution. In one embodiment, the second threshold can be set to 10 to 15.

[0057] In one embodiment, when the number of unmarked satellites is less than the second threshold, the carrier of the epoch corresponding to the epoch when the sign of the difference result of the marked satellite changes is obtained, and is used to perform ambiguity fixation and carrier differential positioning solution in conjunction with the carrier of the unmarked satellite.

[0058] It should be noted that when the difference result of the marked satellite changes its sign, it can be the maximum point corresponding to the change from positive to negative, or the minimum point corresponding to the change from negative to positive, or the point of the epoch adjacent to the maximum point, or the point of the epoch adjacent to the minimum point, such as 1-5 adjacent epochs. The carrier signal of the epoch corresponding to the maximum point and the minimum point or its adjacent epoch is obtained, and the ambiguity is fixed in combination with the carrier signal of the satellite without multipath signal, and the carrier differential positioning solution is performed.

[0059] In the present application, for a static receiver, the multipath error of the pseudorange observation fluctuates periodically over time, while the Doppler observation is much less affected by multipath than the pseudorange. Therefore, it is possible to determine whether the signal has a multipath error by determining whether the difference between the pseudorange change and the Doppler average value of multiple epochs has a periodic change, and to determine the availability of the multipath carrier by utilizing the peak-shifting characteristics of the extreme points of the pseudorange multipath error and the carrier multipath error, so as to ensure that the carrier multipath signal with poor quality will not be involved in the ambiguity fixation when the integer ambiguity is fixed.

[0060] In one embodiment, the method is used for real-time carrier differential positioning solution or post-processing carrier differential positioning solution.

[0061] In order to better understand the technical solution of this specification, a specific example is used for illustration below. The details listed in this example are mainly for ease of understanding and are not intended to limit the scope of protection of this application.

[0062] refer to Figure 2 As shown, the carrier differential positioning solution in this embodiment includes the following steps:

[0063] Step 201, calculate the difference between the pseudoranges of the two epochs before and after the receiver and the average value of the Doppler of the two epochs before and after.

[0064] prdiff=pr2-pr1

[0065]

[0066] Where prdiff is the difference between the pseudoranges of the two epochs, pr1 and pr2 are the pseudoranges of the two epochs, dr is the average value of the Doppler of the two epochs, dr1 and dr2 are the Doppler of the two epochs.

[0067] Step 202, calculating the difference between the pseudorange of the two previous and next epochs and the Doppler average value.

[0068] diff=prdiff-dr

[0069] The difference results of n epochs of multiple satellites are stored, and these diffs are continuous. If there is an interruption, the calculation and storage need to be restarted. The difference results of the first m epochs are used as training sample data, and the difference results of the last nm epochs are used as verification data.

[0070] Step 203, normalize the stored first m epoch difference time series to reduce the complexity of numerical calculations during the training process and avoid large values ​​dominating the training process. The normalization formula is as follows:

[0071]

[0072] In the formula, is the normalized value, x max and x min They are the maximum and minimum values ​​of the difference result diff respectively.

[0073] Step 204, select a kernel function. In the modeling of support vector machine SVM, the selection of kernel function is directly related to the performance of the established model. The commonly used kernel functions in SVM mainly include: RBF kernel function, radial basis kernel function, polynomial kernel function, linear kernel function, neural network kernel function. The RBF kernel function selected in the present invention is specifically:

[0074]

[0075] Cross-validation is used to determine the best SVM model parameters ε and C and the parameter σ of the RBF kernel function. Among them, ε is used to control the number of support vectors and generalization ability. The larger the value of ε, the lower the accuracy and the fewer support vectors; C is a compromise between the complexity of the function regression model and the sample fitting accuracy. The larger C is, the higher the degree of numerical fitting; σ is used to control the complexity of the final solution. If its value is too large or too small, the generalization ability of the system will deteriorate.

[0076] Step 205: Use the best model parameters to train the entire training sample data to obtain a prediction model.

[0077] Step 206, using the SVM model to predict the difference results of the next nm epochs to obtain a predicted value.

[0078] Step 207, perform denormalization on the predicted value, and the denormalization formula is as follows:

[0079]

[0080] Among them, X i is the denormalized value, x max and x min They are the maximum and minimum values ​​of the difference result diff respectively.

[0081] Step 208, using a mathematical sign function to convert the signs of the prediction results and the difference results. The difference result diff of the multipath signal changes periodically, and the consistency between the prediction result and the difference result is good. The difference result diff of the normal signal is a random time series with a very small value, and the consistency between the prediction result and the difference result is poor. If the consistency between the prediction result and the difference result diff is measured by the absolute error between the two, the difference between the multipath signal and the normal signal is not obvious. The sign of the difference result diff of the multipath signal also changes periodically, and the sign of the difference result diff of the normal signal is random. Therefore, the present invention uses a mathematical sign function to convert the prediction results and the difference results diff of the last nm epochs of each satellite, and uses the consistency of the positive and negative signs of the converted prediction results and the difference results to judge the multipath signal.

[0082] Step 209: Compare the positive and negative signs of the converted prediction result and the difference result, and count the number of times the signs of the two are consistent.

[0083] Step 210, determine whether the number of consistent symbols of a certain satellite is greater than or equal to the first threshold. If the number of consistent symbols of a certain satellite is greater than or equal to the first threshold, then enter step 211, consider that the current difference result time series has periodicity, the observed quantity is a multipath signal, and the satellite is marked as a multipath signal. The carrier signal of the unmarked satellite is the carrier of the non-multipath signal, and then enter step 212 to count the number of non-multipath signal carriers. If the number of consistent symbols of a certain satellite is less than the first threshold, directly enter step 212.

[0084] Step 213 determines whether the number of carriers of the current non-multipath signal is greater than or equal to the second threshold. If the number of carriers of the non-multipath signal is greater than or equal to the second threshold, then enters step 214 to directly remove the carrier signals of all marked satellites, and then enters step 215 to perform ambiguity fixation and carrier differential positioning solution.

[0085] If the number of carriers of the non-multipath signal is less than the second threshold, the process proceeds to step 216 to determine the epoch corresponding to the extreme value of the pseudorange of the marked satellite, and to mark the carrier of the epoch as a multipath signal for ambiguity fixation. This can prevent the situation in which too few carriers participate in ambiguity fixation in a harsh multipath environment make it difficult to fix the ambiguity or the ambiguity is fixed incorrectly. When the sign of the prediction result is in a positive-negative transition, the multipath error of the pseudorange reaches the extreme value point and its adjacent point. At this time, the multipath error of the carrier is often small. The carrier of the epoch corresponding to the extreme value point or its adjacent point is marked as a multipath signal that can be used for ambiguity fixation.

[0086] Step 217, remove the carrier of the epoch that is not used for ambiguity fixation, and combine the carrier of the epoch marked in step 216 with the carrier signal of the unmarked satellite to perform ambiguity fixation and carrier differential positioning solution to obtain a high-precision fixed solution.

[0087] In the second embodiment of the present application, a carrier differential positioning solution device is disclosed. Figure 3 The structure diagram of the carrier differential positioning solution device is shown, including: a difference result calculation module, a training module, a statistical module, a marking module and a solution module, wherein:

[0088] The difference result calculation module is configured to calculate the difference result between the pseudorange difference value of each epoch in the n epochs of the multiple satellites and the previous epoch and the Doppler reference value, and normalize the difference results of the first m epochs in the n epochs of the multiple satellites;

[0089] The training module is configured to train the parameters of the kernel function of the support vector machine and the model parameters of the support vector machine according to the difference results of the first m epochs after the normalization process to obtain a prediction model;

[0090] The prediction module is configured to use the prediction model to predict the difference results of the last nm epochs of the n epochs of the multiple satellites to obtain a prediction value, and perform a denormalization process on the prediction value to obtain a prediction result;

[0091] The statistical module is configured to obtain the number of epochs in which the positive and negative signs of the prediction results and the difference results are consistent in the same epoch for the prediction results and the difference results of the last nm epochs of the multiple satellites;

[0092] The marking module is configured to mark any satellite of the plurality of satellites when the number of epochs for which the symbols of the satellite are consistent is greater than or equal to a first threshold;

[0093] The solution module is configured to obtain the number of unmarked satellites among the multiple satellites, and when the number of unmarked satellites is greater than or equal to a second threshold, use the carrier of the unmarked satellite to perform ambiguity fixation and carrier differential positioning solution.

[0094] The solution module is also configured to: when the number of unmarked satellites is less than the second threshold, obtain the carrier of the epoch corresponding to the positive and negative signs of the difference results of the marked satellites, and use it to jointly perform ambiguity fixation and carrier differential positioning solution with the carrier of the unmarked satellites.

[0095] The first embodiment is a system embodiment corresponding to the present embodiment. The technical details in the first embodiment can be applied to the present embodiment, and the technical details in the present embodiment can also be applied to the first embodiment.

[0096] It should be noted that those skilled in the art should understand that the implementation functions of each module shown in the implementation of the above-mentioned carrier differential positioning and solving device can be understood with reference to the relevant description of the above-mentioned carrier differential positioning and solving method. The functions of each module shown in the implementation of the above-mentioned carrier differential positioning and solving device can be realized by a program (executable instruction) running on a processor, or by a specific logic circuit. If the above-mentioned carrier differential positioning and solving device in the embodiment of this specification is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of this specification, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in each embodiment of this specification. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read Only Memory), disk or optical disk and other media that can store program code. In this way, the embodiment of this specification is not limited to any specific combination of hardware and software.

[0097] Accordingly, the embodiments of the present specification also provide a computer-readable storage medium, in which computer executable instructions are stored, and when the computer executable instructions are executed by the processor, the various method embodiments of the present specification are implemented. Computer-readable storage media include permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. Information can be a computer-readable instruction, a data structure, a module of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, read-only compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable storage media does not include temporary computer-readable media (transitory media), such as modulated data signals and carriers.

[0098] It should be noted that in the application documents of this patent, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "including one" do not exclude the existence of other identical elements in the process, method, article or device including the elements. In the application documents of this patent, if it is mentioned that an action is performed according to an element, it means that the action is performed at least according to the element, which includes two situations: performing the action only according to the element, and performing the action according to the element and other elements. Expressions such as multiple, multiple, and multiple include 2, 2 times, 2 kinds, and more than 2, more than 2 times, and more than 2 kinds.

[0099] All documents mentioned in this specification are considered to be included in the disclosure of this specification as a whole, so that they can be used as a basis for modification when necessary. In addition, it should be understood that the above is only a preferred embodiment of this specification and is not intended to limit the scope of protection of this specification. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of one or more embodiments of this specification should be included in the scope of protection of one or more embodiments of this specification.

[0100] In some cases, the actions or steps described in the claims may be performed in a different order than in the embodiments and still achieve the desired results. In addition, the processes described in the drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A carrier differential positioning solution method, characterized in that: include: Calculate the difference between the pseudorange difference between each epoch of n epochs of multiple satellites and the previous epoch and the Doppler reference value, and normalize the difference results of the first m epochs of the n epochs of the multiple satellites; Training the parameters of the kernel function of the support vector machine and the model parameters of the support vector machine according to the difference results of the first m epochs after the normalization process to obtain a prediction model; Using the prediction model to predict the difference results of the last nm epochs of the n epochs of the multiple satellites to obtain predicted values, and performing a denormalization process on the predicted values ​​to obtain a predicted result; Obtaining the number of epochs in which the prediction results and the difference results of the last nm epochs of the multiple satellites have the same positive and negative signs as the prediction results and the difference results in the same epoch; When the number of epochs in which the symbols of any satellite of the plurality of satellites are consistent is greater than or equal to a first threshold, marking the satellite; The number of unmarked satellites among the multiple satellites is obtained, and when the number of unmarked satellites is greater than or equal to a second threshold, the carriers of the unmarked satellites are used to perform ambiguity fixation and carrier differential positioning solution.

2. The carrier differential positioning solution method according to claim 1, characterized in that: When the number of unmarked satellites is less than the second threshold, the carrier of the epoch corresponding to the time when the sign of the difference result of the marked satellite changes is obtained, and is used to perform ambiguity fixation and carrier differential positioning solution in conjunction with the carrier of the unmarked satellite.

3. The carrier differential positioning solution method according to claim 1, characterized in that: In the step of normalizing the difference values ​​of the first m epochs among the n epochs of the multiple satellites, the normalized value is calculated using the following formula: in, is the normalized value, X i is the difference result of the i-th epoch among the n epochs of the multiple satellites, x max and x min are respectively the maximum and minimum values ​​of the difference results in the n epochs.

4. The carrier differential positioning solution method according to claim 1, characterized in that: The kernel function adopts any one of the following: RBF kernel function, radial basis kernel function, polynomial kernel function, linear kernel function, neural network kernel function.

5. The carrier differential positioning solution method according to claim 1, characterized in that: The Doppler reference value is the Doppler average value of each epoch in the n epochs and its previous epoch.

6. The carrier differential positioning solution method according to claim 1, characterized in that: The method is used for real-time carrier differential positioning solution or post-processing carrier differential positioning solution.

7. A carrier differential positioning solution device, characterized in that: A difference result calculation module is configured to calculate a difference result between a pseudorange difference value of each epoch in n epochs of multiple satellites and a previous epoch and a Doppler reference value, and normalize the difference results of the first m epochs in the n epochs of the multiple satellites; A training module is configured to train the parameters of the kernel function of the support vector machine and the model parameters of the support vector machine according to the difference results of the first m epochs after the normalization process to obtain a prediction model; A prediction module is configured to use the prediction model to predict the difference results of the last nm epochs of the n epochs of the multiple satellites to obtain a prediction value, and perform a denormalization process on the prediction value to obtain a prediction result; A statistical module is configured to obtain the number of epochs in which the positive and negative signs of the prediction results and the difference results of the last nm epochs of the multiple satellites are consistent; a marking module configured to mark any satellite of the plurality of satellites when the number of epochs for which the symbols of the satellite are consistent is greater than or equal to a first threshold; The solution module is configured to obtain the number of unmarked satellites among the multiple satellites, and when the number of unmarked satellites is greater than or equal to a second threshold, use the carriers of the unmarked satellites to perform ambiguity fixation and carrier differential positioning solution.

8. The carrier differential positioning solution device according to claim 7, characterized in that: The solution module is also configured to: when the number of unmarked satellites is less than the second threshold, obtain the carrier of the epoch corresponding to the positive and negative signs of the difference results of the marked satellites, and use it to jointly perform ambiguity fixation and carrier differential positioning solution with the carrier of the unmarked satellites.

9. A carrier differential positioning solution device, characterized in that: include: A memory for storing computer executable instructions; as well as A processor, coupled to the memory, configured to implement the steps of the method according to any one of claims 1 to 6 when executing the computer executable instructions.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are executed by a processor, the steps in the method according to any one of claims 1 to 6 are implemented.

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