CN114325777B - Cycle slip detection and repair method, device and equipment - Google Patents
Cycle slip detection and repair method, device and equipment Download PDFInfo
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Abstract
The application belongs to the technical field of satellite navigation, and provides a cycle slip detection and repair method, a device and equipment. The method of the application comprises the following steps: preprocessing data, and eliminating a satellite with a fault; combining a plurality of cycle slip detection models, and if cycle slip exists, acquiring a rough output value of the cycle slip; determining a target cycle slip value in the constructed search space based on an adaptive moment estimation algorithm; the search space is determined by the coarse output value. By adopting the technical scheme in the embodiment of the application, the coverage of cycle slip detection and repair is improved and the repair effect is improved through the combination of a plurality of cycle slip detection and repair models. Meanwhile, based on the reliability evaluation model, reliability evaluation values can be obtained for the repairing effect, and the repairing quality and accuracy can be judged.
Description
Technical Field
The application relates to the technical field of satellite navigation, in particular to a cycle slip detection and repair method, a device and equipment.
Background
Along with the development of GNSS technology and the change of social requirements, the requirements on navigation positioning measurement accuracy are higher and higher. Positioning by using carrier phase observables is a GNSS high-precision positioning method with highest precision at present, but if a high-precision measurement result is desired, the whole-cycle ambiguity must be correctly resolved. In order to obtain a carrier phase measurement result that correctly solves the whole-cycle ambiguity and thus determines high accuracy, cycle slip detection and repair are required. The cycle slip has a great influence on the result of GNSS data processing, once the cycle slip occurs, the whole cycle number of the carrier phase observed value under the epoch is wrong, and all the observed values after the epoch have the wrong, so that the positioning result is seriously affected. Therefore, the detection and repair of cycle slip is a problem which must be solved when processing carrier phase observation data, and belongs to a research hot spot in the field of high-precision positioning and orientation.
In the process of GNSS actually measured data, due to influence of factors such as signal blocking, hardware faults, atmospheric disturbance, surrounding observation environments and the like, various conditions such as big and small cycle slips, continuous cycle slips, special combination cycle slips and the like can be caused. Aiming at the problem, scholars at home and abroad propose various cycle slip detection and repair methods (such as MW combination method, GF combination method, high order difference method, ionosphere residual error method and the like), however, all the algorithms have own limitations and application ranges, and partial cycle slip conditions cannot be effectively detected; secondly, the cycle slip repairing precision is insufficient, if MW combination is influenced by the precision of pseudo-range observance, the repairing effect on cycle slip is poor, and when the sampling rate is low, the high-order difference method is influenced by ionosphere residual errors, and the repairing effect on cycle slip is poor; finally, the quality and accuracy of the cycle slip repair value cannot be evaluated after the cycle slip repair value is calculated, and the applicability of the algorithm per se is further lacking.
Disclosure of Invention
Aiming at the defects in the prior art, the application provides a cycle slip detection and repair method, a device and equipment, which are used for solving the problem of poor repair effect caused by low universality of cycle slip detection and repair in the existing method.
In a first aspect, the present application provides a cycle slip detection and repair method, including:
preprocessing data, and eliminating a satellite with a fault;
combining a plurality of cycle slip detection models, and if cycle slip exists, acquiring a rough output value of the cycle slip;
determining a target cycle slip value in the constructed search space based on an adaptive moment estimation algorithm;
and outputting the credibility evaluation value of the target cycle slip value based on the credibility evaluation model after training.
According to the technical scheme, the cycle slip detection and repair method provided by the application improves the coverage of cycle slip detection and repair and improves the repair effect through the combination of a plurality of cycle slip detection and repair models. Meanwhile, based on the reliability evaluation model, reliability evaluation values can be obtained for the repairing effect, and the repairing quality and accuracy can be judged.
Optionally, the combining the cycle slip detection models, if there is a cycle slip, obtains a rough output value of the cycle slip, including:
building a combined model based on a plurality of cycle slip detection methodsJudging whether cycle slip exists or not based on the combined model; wherein DeltaN 1 ，ΔN 2 ，…，ΔN m The number of the cycle slip detection methods;
if the cycle slip exists, a combined cycle slip repair model is established;
obtaining a solution N of the combined cycle slip repair model based on a least square method OPT ；N OPT I.e. the coarse output value.
Optionally, the determining the target cycle slip value in the constructed search space based on the adaptive moment estimation algorithm includes:
constructing an objective function J (x) based on the combined model;
determining a search space by taking the rough output value as a search center and taking + -n weeks as a search range;
searching in the search space based on an adaptive moment estimation algorithm, and determining the target cycle slip value.
According to the technical scheme, the target cycle slip value is searched based on the self-adaptive moment estimation algorithm, so that the influence of various residual errors on the cycle slip value can be effectively eliminated, the cycle slip searching efficiency can be improved compared with a traversing method, the target cycle slip value can be obtained in a large-scale searching space without traversing, and the searching efficiency is improved.
Optionally, the determining the target cycle slip value in the constructed search space based on the adaptive moment estimation algorithm includes:
inputting the rough output value as a search starting point X 0 Phasors and input step length alpha of self-adaptive moment estimation, moment estimation exponential decay rate beta 1 、β 2 Search termination threshold V th A stable small constant delta;
updating partial first moment estimate s t Sum bias moment estimate r t And correcting the first moment estimation deviation s t ' sum bias moment estimated bias r t ′；
Obtaining cycle slip search variation DeltaX t ；
Updating cycle slip search value X t ；
If the cycle slip search value X t Meeting the search termination threshold V th Outputting the target cycle slip value.
Optionally, the method further comprises:
and outputting the credibility evaluation value of the target cycle slip value based on the credibility evaluation model after training.
According to the technical scheme, the traditional cycle slip detection and repair method is difficult to evaluate the cycle slip repair method, the reliability evaluation value is obtained based on the reliability evaluation model, the cycle slip repair effect can be obtained, and the reliability of the target cycle slip value is determined.
Optionally, the outputting the reliability evaluation value of the target cycle slip value based on the reliability evaluation model after completing training includes:
acquiring an evaluation function based on a reliability evaluation model for completing training; wherein the evaluation function eval_cs=ω 1 Cs 1 +ω 2 Cs 2 +ω 3 Cs 3 +...+ω m Cs m ；
And inputting the target cycle slip value into the evaluation function to obtain a credibility evaluation value.
According to the scheme, the reliability evaluation model is built, so that the accuracy of the target cycle slip value can be effectively evaluated, and the repair quality is ensured.
Optionally, the credibility evaluation model is trained by the following method:
obtaining an evaluation letterNumber eval_cs=w i Cs i (i=1, 2,., m-1); wherein m is the number of models involved in the evaluation, W i Is coefficient matrix of each cycle slip model, cs i Is a model expression of each cycle;
obtaining a plurality of groups of target cycle slip value samples X;
based on one set of samples and weight matrix among several sets of the target cycle slip value samples XDetermining an initial output matrix->
Re-inputting another set of samples, updating the weight matrix w t Output matrix y t ；
If the loss function L meets the preset threshold condition, training the credibility evaluation model is completed; otherwise, re-inputting other groups of samples and updating the weight matrix w t Output matrix y t Until the loss function L meets a preset threshold condition.
According to the technical scheme, the output matrix and the weight matrix in the reliability evaluation model are gradually converged by setting the preset threshold condition of the loss function L, so that the reliability evaluation model is formed, and the accuracy of restoration quality judgment is ensured.
Optionally, the loss functionWherein N is the number of input sample groups, y is the output matrix, X is the target cycle slip value sample, and ω is the weight value.
In a second aspect, the present application provides a cycle slip detection and repair device, comprising:
the preprocessing module is used for preprocessing the data and eliminating the satellite with the fault;
the first output module is used for combining a plurality of cycle slip detection models, and acquiring a rough output value of cycle slip if the cycle slip exists;
and the second output module is used for determining a target cycle slip value in the constructed search space based on the adaptive moment estimation algorithm.
Optionally, the first output module is specifically configured to:
building a combined model based on a plurality of cycle slip detection methodsJudging whether cycle slip exists or not based on the combined model; wherein DeltaN 1 ，ΔN 2 ，…，ΔN m The number of the cycle slip detection methods;
if the cycle slip exists, a combined cycle slip repair model is established;
obtaining a solution N of the combined cycle slip repair model based on a least square method OPT ；N OPT I.e. the coarse output value.
Optionally, the second output module is specifically configured to:
constructing an objective function J (x) based on the combined model;
determining a search space by taking the rough output value as a search center and taking + -n weeks as a search range;
searching in the search space based on an adaptive moment estimation algorithm, and determining the target cycle slip value.
Optionally, the second output module is specifically further configured to:
inputting the rough output value as a search starting point X 0 Phasors and input step length alpha of self-adaptive moment estimation, moment estimation exponential decay rate beta 1 、β 2 Search termination threshold V th A stable small constant delta;
updating partial first moment estimate s t Sum bias moment estimate r t And correcting the first moment estimation deviation s t ' sum bias moment estimated bias r t ′；
Obtaining cycle slip search variation DeltaX t ；
Updating cycle slip search value X t ；
If the cycle slip search value X t Meeting the search termination threshold V th Outputting the target cycle slip value.
Optionally, the device further comprises an evaluation module, wherein the evaluation module is used for outputting the credibility evaluation value of the target cycle slip value based on the trained credibility evaluation model.
Optionally, the evaluation module is specifically further configured to:
acquiring an evaluation function based on a reliability evaluation model for completing training; wherein the evaluation function eval_cs=ω 1 Cs 1 +ω 2 Cs 2 +ω 3 Cs 3 +...+ω m Cs m ；
And inputting the target cycle slip value into the evaluation function to obtain a credibility evaluation value.
Optionally, in the evaluation module, the credibility evaluation model is trained by the following method:
acquisition of evaluation function eval_cs=w i Cs i (i=1, 2,., m-1); wherein m is the number of models involved in the evaluation, W i Is coefficient matrix of each cycle slip model, cs i Is a model expression of each cycle;
obtaining a plurality of groups of target cycle slip value samples X;
based on one set of samples and weight matrix among several sets of the target cycle slip value samples XDetermining an initial output matrix->
Re-inputting another set of samples, updating the weight matrix w t Output matrix y t ；
If the loss function L meets the preset threshold condition, training the credibility evaluation model is completed; otherwise, re-inputting other groups of samples and updating the weight matrix w t Output matrix y t Until the loss function L meets a preset threshold condition.
Optionally, the evaluation module, anThe loss functionWherein N is the number of input sample groups, y is the output matrix, X is the target cycle slip value sample, and ω is the weight value.
In a third aspect, an embodiment of the present application provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of any of the methods described above when the processor executes the computer program.
In a fourth aspect, an embodiment of the application provides a computer readable storage medium having stored thereon computer program instructions which when executed by a processor perform the steps of any of the methods described above.
By adopting the technical scheme, the application has the following beneficial effects:
(1) The application provides a multi-cycle slip detection and repair algorithm combined model architecture, which performs comprehensive voting on cycle slip detection results through a combined model, can effectively overcome the problems of a single model or a specific combined model, greatly improves the sensitivity of cycle slip detection, and realizes more comprehensive detection.
(2) After the cycle slip is solved by the traditional cycle slip detection and restoration algorithm, the optimal solution is considered to be obtained, however, the precision of the obtained cycle slip restoration value is insufficient due to the fact that the pseudo-range observed quantity is low in precision, the ambiguity is uncertain, and various factors such as residual errors and the like are not considered for some error items in the resolving process. According to the application, the cycle slip optimal value searching space is constructed, and a cycle slip optimal value rapid searching algorithm based on a self-adaptive moment estimation algorithm is provided, so that the influence of various residual errors on cycle slip is effectively eliminated; compared with the traditional traversal algorithm, the method has the advantages that the cycle slip optimal value can be obtained quickly in a large range of search space without traversal, and the operation efficiency is greatly improved.
(3) Aiming at the problem that the traditional cycle slip detection and repair algorithm is difficult to evaluate the calculated cycle slip value and the algorithm itself effectively, the application provides a reliability evaluation model, and the quality and accuracy of the cycle slip and the algorithm can be effectively evaluated by gradually adjusting the weight of each algorithm until convergence by utilizing a cycle slip optimal value sample to form the reliability model.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. Like elements or portions are generally identified by like reference numerals throughout the several figures. In the drawings, elements or portions thereof are not necessarily drawn to scale.
FIG. 1 shows a flowchart of a cycle slip detection and repair method provided by an embodiment of the present application;
FIG. 2 shows a flowchart of a cycle slip detection and repair method provided by an embodiment of the present application;
FIG. 3 shows a block diagram of a cycle slip detection and repair device provided by an embodiment of the present application;
fig. 4 shows a block diagram of an electronic device according to an embodiment of the present application.
Detailed Description
Embodiments of the technical scheme of the present application will be described in detail below with reference to the accompanying drawings. The following examples are only for more clearly illustrating the technical aspects of the present application, and thus are merely examples, which should not be construed as limiting the scope of the present application.
It is noted that unless otherwise indicated, technical or scientific terms used herein should be given the ordinary meaning as understood by one of ordinary skill in the art to which this application belongs.
In the process of GNSS actually measured data, due to influence of factors such as signal blocking, hardware faults, atmospheric disturbance, surrounding observation environments and the like, various conditions such as big and small cycle slips, continuous cycle slips, special combination cycle slips and the like can be caused. The current determined cycle slip detection and repair methods are aimed at improving the detection effect of partial cycle slips, and although the detection and repair effects can be improved aiming at applicable cycle slips, the universality is not high, and the improvement of most of cycle slip repair effects still cannot be met, so that the universality of cycle slip detection and repair is improved, the detection effect is improved, and a method with high coverage and good repair effect for cycle slip detection and repair is urgently needed.
In order to solve the above-mentioned problems, fig. 1 shows a flowchart of a cycle slip detection and repair method according to an embodiment of the present application. As shown in fig. 1, a cycle slip detection and repair method according to an embodiment of the present application includes:
s101, preprocessing data, and eliminating a satellite with a fault.
S1011: establishing an analytical model of international mainstream data formats such as a RENIX format, a Novatel format, an RTCM format and the like, and acquiring information such as orbit parameters of each satellite in the epoch data, pseudo-range observed quantity and carrier phase observed quantity of each frequency point of each satellite.
S1012: and setting a pseudo-range observed quantity threshold, and eliminating satellite frequency point information corresponding to the observed quantity for the pseudo-range observed quantity which does not meet the threshold requirement.
S1013: and performing positioning calculation to obtain the rough position of the receiver.
S1014: performing receiver autonomous straightness monitoring (RAIM), and obtaining pseudo-range residual information by adopting a pseudo-range residual detection method (after positioning);
S＝I-G(G T CG) -1 G T C，
where vector b is the pre-positioning pseudorange residual,for the pseudo-range observation quantity of the nth satellite, r (n) (x 0 ) For the geometric distance between the nth satellite of the previous epoch and the target, δt u,0 Receiver clock difference for last epoch, vector +.>For the post-positioning pseudo-range residual, the matrix S is a state transition matrix for converting the pre-positioning residual into the post-positioning residual, the jacobian matrix G is a geometric matrix, and the matrix C is a weight matrix.
S1015: setting χ by least squares residual method 2 The distributed degrees of freedom and false alarm rate parameters are used for detecting pseudo-range residual information after positioning, and error satellites are removed;
wherein the scalar ε WSSE The length square of the weighted residual vector.
S1016: and (4) re-executing positioning calculation, acquiring a target rough position, executing RAIM detection, returning to S1014 if a fault satellite is detected, otherwise completing RAIM detection of pseudo-range observables, and proceeding to S1017.
S1017: and calculating altitude information, deleting satellites with elevation angles lower than 10 degrees, and ensuring accuracy of pseudo-range observed quantity.
S102, combining a plurality of cycle slip detection models to obtain a rough output value of cycle slip.
Specifically, cycle slip detection is performed based on a combined architecture of a plurality of cycle slip detection models, and if cycle slip is detected, a detected rough cycle slip output value is determined. Otherwise, the cycle slip is not detected, and the detection processing result of the cycle slip is directly output, namely the cycle slip is not detected.
S103, determining a target cycle slip value in the constructed search space based on the adaptive moment estimation algorithm.
Optionally, step S102 includes:
s1021, establishing a combined model based on a plurality of cycle slip detection methodsJudging whether cycle slip exists or not based on the combined model; wherein DeltaN 1 ，ΔN 2 ，…，ΔN m For cycle slip detectionNumber of methods.
Specifically, a set of various cycle slip detection and repair algorithms is constructedWhere N is the number of models of cycle slip detection and repair algorithms, CSDR i Is a specific model expression, namely a pseudo-range observed quantity matrix P, a carrier phase observed quantity matrix L, a wavelength matrix lambda of each frequency point, an error epsilon and a detection threshold matrix V th A function of the cycle slip matrix deltan correlation. Through the mode of algorithm combination, the cycle slip search range can be enlarged, and cycle slip detection is more comprehensive.
In one possible implementation mode, the combination of the MW combination, GF combination, high order difference method and ionosphere residual method is selected for comprehensive judgment, so that detection of large and small cycle slips and special combination cycle slips can be basically realized, and the searching range of cycle slips is covered as far as possible.
And S1022, if the cycle slip exists, establishing a combined cycle slip repair model.
Specifically, if a cycle slip is detected, each cycle slip repair base model may be constructed using Cs i (P, L, λ, ε, Δn) =0. For a system to be tested, parameters such as the frequency point number P, the pseudo range L, the error item epsilon and the like are fixed values, and the variable is only the cycle slip value of each frequency point, the cycle slip repair basic model can be described as y i ＝Cs i (ΔN 1 ,ΔN 2 ,...,ΔN m ) Wherein y is i And outputting the combination of the parameters. Then combining n cycle slip repair models can be expressed as
S1023, obtaining a solution N of a combined cycle slip repair model based on a least square method OPT ；N OPT I.e. the coarse output value.
Specifically, the vector N is obtained by the least square method based on combining N cycle slip repair models OPT The obtained vector N OPT An unstable solution with a set of decimal places, which may also contain pseudoranges and residuals of some error terms.
Optionally, referring to fig. 2, step S103 includes:
s1031, constructing an objective function J (x) based on a combined cycle slip repair model;
s1032, determining a search space by taking the rough output value as a search center and taking + -n weeks as a search range;
s1033, searching in a search space based on the adaptive moment estimation algorithm, and determining a target cycle slip value.
Specifically, for the combined cycle slip repair model, an objective function J (x) is constructed with the least squares sum as an evaluation criterion,vector N obtained in step S1023 OPT And (3) taking uncertainty caused by incomplete elimination of partial error terms in pseudo-range observed quantity errors and resolving processes into consideration for a search center, and constructing a cycle slip target value search space by taking + -n cycles as a search range. And then, continuously correcting the cycle slip search variable quantity based on the adaptive moment estimation algorithm, and updating the cycle slip search value.
Optionally, step S1033 includes:
s1033.1, inputting coarse output value as search starting point X 0 Phasors and input step length alpha of self-adaptive moment estimation, moment estimation exponential decay rate beta 1 、β 2 Search termination threshold V th The value stabilizes by a small constant delta.
S1033.2 updating the partial first moment estimate S t Sum bias moment estimate r t And correcting the first moment estimation deviation s t ' sum bias moment estimated bias r t ′。
The objective function gradient f (x) is calculated,wherein t is the search times, and the vector X is the cycle slip search value.
Updating partial first moment estimate s t ，s t ＝β 1 s t-1 +(1-β 1 )/f(X t )。
Updating bias moment estimatesMeter r t ，r t ＝β 2 r t-1 +(1-β 2 )/f(X t ) 2 。
Correcting the bias first moment estimation deviation s t ′，
Correcting bias moment estimated deviation r t ′，
S1033.3 obtaining cycle slip search variation DeltaX t ，
S1033.4 updating cycle slip search value X t ，X t ＝X t-1 +ΔX t 。
S1033.5, if cycle slip search value X t Meeting the search termination threshold V th Outputting the target cycle slip value.
Specifically, if the cycle slip search value X t Meeting the search termination threshold V th The cycle slip search value in step S1033.4 is the cycle slip target value; if cycle slip search value X t Does not satisfy the search termination threshold V th Returning to step S1033.2, the objective function gradient f (X) is re-acquired, and the updated cycle slip search value X is acquired t Until the search termination threshold V is satisfied th 。
Optionally, the method further comprises:
and outputting the credibility evaluation value of the target cycle slip value based on the credibility evaluation model after training.
Specifically, the traditional cycle slip detection and repair method is difficult to effectively evaluate the calculated cycle slip value and the algorithm, and aiming at the problem, the embodiment of the application provides a reliability evaluation model, and the weight of each algorithm is gradually adjusted until convergence by utilizing a cycle slip optimal value sample to form the reliability model, so that the quality and accuracy of the cycle slip and the algorithm can be effectively evaluated.
Optionally, outputting the reliability evaluation value of the target cycle slip value based on the reliability evaluation model after training, specifically including the following steps:
acquiring an evaluation function based on a reliability evaluation model for completing training; wherein the evaluation function eval_cs=ω 1 Cs 1 +ω 2 Cs 2 +ω 3 Cs 3 +...+ω m Cs m ；
And inputting the target cycle slip value into an evaluation function to obtain a credibility evaluation value.
Specifically, based on the reliability evaluation model for completing the training, a reliability evaluation value corresponding to the target cycle slip value may be obtained from the determined target cycle slip value, and the quality and accuracy evaluation of the cycle slip target value obtained in step S103 may be determined by the reliability evaluation value. In step S103, if the target cycle slip value is obtained, the reliability evaluation is not performed in this step.
Optionally, the reliability evaluation model is trained by the following method:
acquisition of evaluation function eval_cs=w i Cs i (i=1, 2,., m-1); wherein m is the number of models involved in the evaluation, W i Is coefficient matrix of each cycle slip model, cs i Is a model expression of each cycle;
obtaining a plurality of groups of target cycle slip value samples X;
based on one set of samples and weight matrix in several sets of target cycle slip value samples XDetermining an initial output matrix->
Re-inputting another set of samples, updating the weight matrix w t Output matrix y t ；
If the loss function L meets the preset threshold condition, the reliability evaluation model training is completed; otherwise, re-inputting other groups of samples and updating the weight matrix w t Output matrixy t Until the loss function L meets a preset threshold condition.
Specifically, the multiple sets of target cycle slip values obtained in step S103 are used to train the reliability evaluation model. And inputting a group of target cycle slip values in a group of target cycle slip value samples into a reliability evaluation model to obtain an initial output matrix y. Then, another set of target cycle slip values is input again, and the weight matrix w is updated t And output matrix y t . And repeating the process of continuously inputting different sets of target cycle slip values until the grand shot function L meets the preset threshold condition.
Alternatively, the loss functionWhere N is the number of input sample sets.
Specifically, the loss functionWherein N is the number of input sample groups; wherein y is an output matrix, and X is input sample data; omega is a weight value.
When L is minimum, the bias on the weight is 0, and then there are:
based on the formula ω= (X T X) -1 X T y, updating weight matrix w t And based on the formula eval_cs (w, Δn) =w T Cs i Updating the output matrix y (delta N) t And determining that the reliability evaluation model training is completed until the loss function L meets a preset threshold condition. By setting specific threshold conditions of the loss function, cycle slip detection and repair accuracy can be guaranteed, and repair effect can be improved.
In a specific example, the preset threshold condition may be set such that the number of iterations 10000 or the accuracy of L satisfies less than 0.001.
In one embodiment, referring to fig. 3, there is provided a cycle slip detection and repair device 20 comprising:
the preprocessing module 201 is used for preprocessing data and eliminating a satellite with a fault;
a first output module 202, configured to combine the plurality of cycle slip detection models, and obtain a rough output value of cycle slip if there is cycle slip;
a second output module 203 for determining a target cycle slip value in the constructed search space based on an adaptive moment estimation algorithm.
Optionally, the first output module is specifically configured to:
building a combined model based on a plurality of cycle slip detection methodsJudging whether cycle slip exists or not based on the combined model; wherein DeltaN 1 ，ΔN 2 ，…，ΔN m The number of the cycle slip detection methods;
if the cycle slip exists, a combined cycle slip repair model is established;
obtaining a solution N of the combined cycle slip repair model based on a least square method OPT ；N OPT I.e. the coarse output value.
Optionally, the second output module is specifically configured to:
constructing an objective function J (x) based on the combined model;
determining a search space by taking the rough output value as a search center and taking + -n weeks as a search range;
searching in the search space based on an adaptive moment estimation algorithm, and determining the target cycle slip value.
Optionally, the second output module is specifically further configured to:
inputting the rough output value as a search starting point X 0 Phasors and input step length alpha of self-adaptive moment estimation, moment estimation exponential decay rate beta 1 、β 2 Search termination threshold V th A stable small constant delta;
updating partial first moment estimate s t Sum bias moment estimate r t And correcting the first moment estimation deviation s t ' sum bias moment estimated bias r t ′；
Obtaining cycle slip search variation DeltaX t ；
Updating cycle slip search value X t ；
If the cycle slip search value X t Meeting the search termination threshold V th Outputting the target cycle slip value.
Optionally, the device further comprises an evaluation module, wherein the evaluation module is used for outputting the credibility evaluation value of the target cycle slip value based on the trained credibility evaluation model.
Optionally, the evaluation module is specifically further configured to:
acquiring an evaluation function based on a reliability evaluation model for completing training; wherein the evaluation function eval_cs=ω 1 Cs 1 +ω 2 Cs 2 +ω 3 Cs 3 +...+ω m Cs m ；
And inputting the target cycle slip value into the evaluation function to obtain a credibility evaluation value.
Optionally, in the evaluation module, the credibility evaluation model is trained by the following method:
acquisition of evaluation function eval_cs=w i Cs i (i=1, 2,., m-1); wherein m is the number of models involved in the evaluation, W i Is coefficient matrix of each cycle slip model, cs i Is a model expression of each cycle;
obtaining a plurality of groups of target cycle slip value samples X;
based on one set of samples and weight matrix among several sets of the target cycle slip value samples XDetermining an initial output matrix->
Re-inputting another set of samples, updating the weight matrix w t Output matrix y t ；
If the loss function L meets the preset threshold condition, training the credibility evaluation model is completed; otherwise, re-inputting other groups of samples and updating the weight matrix w t Output matrix y t Until the loss function L meets a preset threshold condition.
Optionally, the evaluation module, the loss functionWherein N is the number of input sample groups, y is the output matrix, X is the target cycle slip value sample, and ω is the weight value.
The cycle slip detection and repair device 20 provided in the embodiment of the present application adopts the same inventive concept as the cycle slip detection and repair method described above, and can achieve the same beneficial effects, and will not be described in detail herein.
Based on the same inventive concept as the cycle slip detection and repair method described above, an embodiment of the present application further provides an electronic device 30, as shown in fig. 4, where the electronic device 30 may include a processor 301 and a memory 302.
The processor 301 may be a general purpose processor such as a Central Processing Unit (CPU), digital signal processor (Digital Signal Processor, DSP), application specific integrated circuit (Application Specific Integrated Circuit, ASIC), field programmable gate array (Field Programmable Gate Array, FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, and may implement or perform the methods, steps, and logic blocks disclosed in embodiments of the application. The steps of a method disclosed in connection with the embodiments of the present application may be embodied directly in a hardware processor for execution, or in a combination of hardware and software modules in the processor for execution.
The memory 302 serves as a non-volatile computer-readable storage medium that can be used to store non-volatile software programs, non-volatile computer-executable programs, and modules. The Memory may include at least one type of storage medium, which may include, for example, flash Memory, hard disk, multimedia card, card Memory, random access Memory (Random Access Memory, RAM), static random access Memory (Static Random Access Memory, SRAM), programmable Read-Only Memory (Programmable Read Only Memory, PROM), read-Only Memory (ROM), charged erasable programmable Read-Only Memory (Electrically Erasable Programmable Read-Only Memory, EEPROM), magnetic Memory, magnetic disk, optical disk, and the like. Memory 302 is any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer, but is not limited to such. The memory 302 in embodiments of the present application may also be circuitry or any other device capable of performing memory functions for storing program instructions and/or data.
Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the above method embodiments may be implemented by hardware associated with program instructions, where the foregoing program may be stored in a computer readable storage medium, and when executed, the program performs steps including the above method embodiments; such computer storage media can be any available media or data storage device that can be accessed by a computer including, but not limited to: various media that can store program code, such as a mobile storage device, a random access memory (RAM, random Access Memory), a magnetic memory (e.g., a floppy disk, a hard disk, a magnetic tape, a magneto-optical disk (MO), etc.), an optical memory (e.g., CD, DVD, BD, HVD, etc.), and a semiconductor memory (e.g., ROM, EPROM, EEPROM, a nonvolatile memory (NAND FLASH), a Solid State Disk (SSD)), etc. Alternatively, the above-described integrated units of the present application may be stored in a computer-readable storage medium if implemented in the form of software functional modules and sold or used as separate products.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present application, and not for limiting the same; although the application has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the application, and are intended to be included within the scope of the appended claims and description.
Claims (8)
1. A cycle slip detection and repair method, comprising:
preprocessing data, and eliminating a satellite with a fault;
combining a plurality of cycle slip detection models, and if cycle slip exists, acquiring a rough output value of the cycle slip;
determining a target cycle slip value in the constructed search space based on an adaptive moment estimation algorithm; the search space is determined by the coarse output value, comprising:
constructing an objective function based on the combined model；
Determining a search space by taking the rough output value as a search center and taking + -n weeks as a search range;
searching in the search space based on an adaptive moment estimation algorithm, and determining the target cycle slip value;
inputting the rough output value as a search starting pointPhasors and input step size of adaptive moment estimation +.>Moment estimation exponential decay Rate->、/>Search termination threshold->Numerical stability small constant->；
Updating partial first moment estimatesAnd bias moment estimation +>And corrects the bias of the first moment estimation>' and bias moment estimation bias +>；
Obtaining cycle slip search variation；
Updating cycle slip search values；
If the cycle slip search valueSatisfying the search termination threshold->Outputting the target cycle slip value.
2. The method of claim 1, wherein combining the plurality of cycle slip detection models, if there is a cycle slip, obtains a coarse output value of the cycle slip, comprising:
method for establishing cycle slip detection based on multiple cyclesConstructing a set of multiple cycle slip detection and repair algorithmsJudging whether cycle slip exists or not based on the combined model; />The number of models of cycle slip detection and repair algorithm; />Is a specific model expression, is a sum pseudo-range observed quantity matrix +.>Carrier phase observation matrix>Wavelength matrix of each frequency point>Error->Detection threshold matrix->Cycle slip matrix->A related function;
if the cycle slip exists, a combined cycle slip repair model is established;
obtaining a solution of the combined cycle slip repair model based on a least square method；/>I.e. the coarse output value.
3. The method according to claim 1, wherein the method further comprises:
and outputting the credibility evaluation value of the target cycle slip value based on the credibility evaluation model after training.
4. The method of claim 3, wherein outputting the confidence rating for the target cycle slip value based on the trained confidence rating model comprises:
acquiring an evaluation function based on a reliability evaluation model for completing training; wherein the evaluation function；
And inputting the target cycle slip value into the evaluation function to obtain a credibility evaluation value.
5. The method of claim 4, wherein the confidence rating model is trained by:
obtaining an evaluation functionThe method comprises the steps of carrying out a first treatment on the surface of the Wherein->Is the number of models involved in the evaluation, < >>Is a coefficient matrix of each cycle slip model, +.>Is a model expression of each cycle;
obtaining a plurality of groups of target cycle slip value samples X;
based on one set of samples and weight matrix among several sets of the target cycle slip value samples XDetermining an initial output matrix +.>；
Re-inputting another set of samples, updating the weight matrixOutput matrix->；
If the loss function L meets the preset threshold condition, training the credibility evaluation model is completed; otherwise, re-inputting other groups of samples and updating the weight matrixOutput matrix->Until the loss function L meets a preset threshold condition.
6. The method of claim 5, wherein the loss functionWherein N is the number of input samples, X is the target cycle slip value sample, ++>Is a weight value.
7. A cycle slip detection and repair device, comprising:
the preprocessing module is used for preprocessing the data and eliminating the satellite with the fault;
the first output module is used for combining a plurality of cycle slip detection models, and acquiring a rough output value of cycle slip if the cycle slip exists;
the second output module is used for determining a target cycle slip value in the constructed search space based on the adaptive moment estimation algorithm; the method is particularly used for: constructing an objective function based on the combined model；
Determining a search space by taking the rough output value as a search center and taking + -n weeks as a search range;
searching in the search space based on an adaptive moment estimation algorithm, and determining the target cycle slip value;
inputting the rough output value as a search starting pointPhasors and input step size of adaptive moment estimation +.>Moment estimation exponential decay Rate->、/>Search termination threshold->Numerical stability small constant->；
Updating partial first moment estimatesAnd bias moment estimation +>And corrects the bias of the first moment estimation>' and bias moment estimation bias +>；
Obtaining cycle slip search variation；
Updating cycle slip search values；
If the cycle slip search valueSatisfying the search termination threshold->Outputting a target cycle slip value;
and the evaluation module is used for outputting the credibility evaluation value of the target cycle slip value based on the trained credibility evaluation model.
8. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the method of any one of claims 1 to 6 when the computer program is executed by the processor.
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