CN114325777A - Cycle slip detection and restoration method, device and equipment - Google Patents
Cycle slip detection and restoration method, device and equipment Download PDFInfo
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Abstract
The invention belongs to the technical field of satellite navigation, and provides a cycle slip detection and restoration method, device and equipment. The method of the invention comprises the following steps: preprocessing the data and eliminating the failed satellite; combining multiple cycle slip detection models, and if cycle slips exist, acquiring a rough output value of the cycle slips; 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 values. By adopting the technical scheme in the embodiment of the application, the cycle slip detection and restoration coverage is improved and the restoration effect is improved by combining various cycle slip detection and restoration models. Meanwhile, based on the credibility evaluation model, the credibility evaluation value of the repairing effect can be obtained, and the repairing quality and accuracy can be judged.
Description
Technical Field
The invention relates to the technical field of satellite navigation, in particular to a cycle slip detection and restoration method, device and equipment.
Background
With the development of GNSS technology and the change of social requirements, the requirement for the accuracy of navigation positioning measurement becomes higher and higher. Positioning by using carrier phase observation is a GNSS high-precision positioning method with the highest precision at present, but if a high-precision measurement result is to be obtained, the whole-cycle ambiguity must be correctly solved. In order to obtain a correct solution for the integer ambiguity and thus determine a high-precision carrier phase measurement result, cycle slip detection and repair are required. Cycle slip has a great influence on the result of GNSS data processing, once cycle slip occurs, the whole cycle number of the carrier phase observed quantity in the epoch is wrong, and all observed values after the epoch have the mistake, which seriously affects the positioning result. Therefore, the detection and repair of cycle slip are problems that need to be solved when carrier phase observation data are processed, and belong to a research hotspot in the field of high-precision positioning and orientation.
In the GNSS measured data process, due to the influence of factors such as signal blockage, hardware failure, atmospheric disturbance, surrounding observation environment and the like, various conditions such as large and small cycle slips, continuous cycle slips, special combined cycle slips and the like may occur. To solve the problem, scholars at home and abroad propose various cycle slip detection and repair methods (such as a MW combination method, a GF combination method, a high order difference method, an ionospheric residual method and the like), however, the algorithms have limitations and application ranges of the algorithms, and partial cycle slip conditions cannot be effectively detected; secondly, the cycle slip repairing precision is insufficient, if the MW combination is influenced by the precision of pseudo-range observed quantity, the cycle slip repairing effect is poor, and when the sampling rate is low, the high-order difference method is influenced by an ionized layer residual error, and the cycle slip repairing effect is poor; finally, the evaluation of cycle slip repairing quality is lacked, and after the cycle slip repairing value is calculated, the evaluation of quality and accuracy cannot be carried out on the cycle slip repairing value, and the evaluation of the applicability of the algorithm is lacked.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a cycle slip detection and restoration method, a cycle slip detection and restoration device and equipment, and aims to solve the problem that the existing method is low in universality on cycle slip detection and restoration and poor in restoration effect.
In a first aspect, the present invention provides a cycle slip detection and repair method, including:
preprocessing the data and eliminating the failed satellite;
combining multiple cycle slip detection models, and if cycle slips exist, acquiring a rough output value of the cycle slips;
determining a target cycle slip value in the constructed search space based on an adaptive moment estimation algorithm;
and outputting the reliability evaluation value of the target cycle slip value based on the trained reliability evaluation model.
According to the technical scheme, the cycle slip detection and restoration method provided by the invention has the advantages that the coverage of cycle slip detection and restoration is improved and the restoration effect is improved through the combination of various cycle slip detection and restoration models. Meanwhile, based on the credibility evaluation model, the credibility evaluation value of the repairing effect can be obtained, and the repairing quality and accuracy can be judged.
Optionally, the combining the multiple cycle slip detection models, and if cycle slips exist, acquiring a rough output value of the cycle slips includes:
establishing a combined model based on multiple cycle slip detection methods
Judging whether cycle slip exists or not based on the combined model; wherein, Δ N1，ΔN2，…，ΔNmThe number of cycle slip detection methods;
if the cycle slip exists, establishing a combined cycle slip repairing model;
obtaining solution N of the combined cycle slip repairing model based on least square methodOPT；NOPTI.e. the coarse output value.
Optionally, the determining a 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;
and searching in the search space based on an adaptive moment estimation algorithm to determine the target cycle slip value.
According to the technical scheme, the target cycle slip value is searched based on the adaptive moment estimation algorithm, 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 traversal method, the target cycle slip value can be obtained without traversal in a large-range searching space, and the searching efficiency is improved.
Optionally, the determining a target cycle slip value in the constructed search space based on the adaptive moment estimation algorithm includes:
inputting the coarse output value as a search starting point X0Phasor, and inputting step length alpha of self-adaptive moment estimation, and exponential decay rate beta of moment estimation1、β2Search termination threshold VthA numerical stability small constant δ;
updating the partial first moment estimate stSum-biased second moment estimate rtAnd correcting the estimated deviation s of the first order momentt' sum-bias second moment estimation bias rt′；
Obtaining cycle slip search variation quantity delta Xt；
Updating cycle slip search value Xt；
If the cycle slip search value XtSatisfying a search termination threshold VthThen the target cycle slip value is output.
Optionally, the method further comprises:
and outputting the reliability evaluation value of the target cycle slip value based on the trained reliability evaluation model.
According to the technical scheme, the traditional cycle slip detection and restoration method is difficult to evaluate the restoration method of the cycle slip, and the reliability evaluation value is obtained based on the reliability evaluation model, so that the cycle slip restoration 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 trained reliability evaluation model includes:
obtaining an evaluation function based on the trained credibility evaluation model; wherein the evaluation letterNumber Eval _ Cs ═ ω1Cs1+ω2Cs2+ω3Cs3+...+ωmCsm；
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 established, so that the accuracy of the target cycle slip value can be effectively evaluated, and the repair quality is guaranteed.
Optionally, the reliability evaluation model is trained by:
acquiring an evaluation function Eval _ Cs ═ WiCsi(i ═ 1,2,. multidot., m-1); where m is the number of models participating in the evaluation, WiIs a coefficient matrix, Cs, of each cycle slip modeliIs a cycle slip model expression;
acquiring a plurality of groups of target cycle skip value samples X;
based on a group of samples in a plurality of groups of target cycle skip value samples X and a weight matrix
Determining an initial output matrix
Re-inputting another set of samples, updating the weight matrix wtAnd an output matrix yt；
If the loss function L meets a preset threshold condition, the reliability evaluation model is trained; otherwise, re-inputting other groups of samples and updating the weight matrix wtAnd an output matrix ytUntil the loss function L satisfies 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 repair quality judgment is ensured.
Optionally, the loss function
Wherein N is the number of input sample groups, y is the output matrix, X is the target cycle slip value sample, and omega is the weight value.
In a second aspect, the present invention provides a cycle slip detecting and repairing apparatus, comprising:
the preprocessing module is used for preprocessing the data and eliminating the failed satellite;
the first output module is used for combining various cycle slip detection models, and acquiring a rough output value of the 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:
establishing a combined model based on multiple cycle slip detection methods
Judging whether cycle slip exists or not based on the combined model; wherein, Δ N1，ΔN2，…，ΔNmThe number of cycle slip detection methods;
if the cycle slip exists, establishing a combined cycle slip repairing model;
obtaining solution N of the combined cycle slip repairing model based on least square methodOPT；NOPTI.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;
and searching in the search space based on an adaptive moment estimation algorithm to determine the target cycle slip value.
Optionally, the second output module is specifically further configured to:
inputting the coarse output value as a search starting point X0Phasors and step sizes of the input adaptive moment estimatesα, moment estimation exponential decay rate β1、β2Search termination threshold VthA numerical stability small constant δ;
updating the partial first moment estimate stSum-biased second moment estimate rtAnd correcting the estimated deviation s of the first order momentt' sum-bias second moment estimation bias rt′；
Obtaining cycle slip search variation quantity delta Xt；
Updating cycle slip search value Xt；
If the cycle slip search value XtSatisfying a search termination threshold VthThen the target cycle slip value is output.
Optionally, the apparatus further includes an evaluation module, configured to output a reliability evaluation value of the target cycle slip value based on a trained reliability evaluation model.
Optionally, the evaluation module is specifically further configured to:
obtaining an evaluation function based on the trained credibility evaluation model; wherein the evaluation function Eval _ Cs is ω1Cs1+ω2Cs2+ω3Cs3+...+ωmCsm；
And inputting the target cycle slip value into the evaluation function to obtain a credibility evaluation value.
Optionally, in the evaluation module, the reliability evaluation model is trained by the following method:
acquiring an evaluation function Eval _ Cs ═ WiCsi(i ═ 1,2,. multidot., m-1); where m is the number of models participating in the evaluation, WiIs a coefficient matrix, Cs, of each cycle slip modeliIs a cycle slip model expression;
acquiring a plurality of groups of target cycle skip value samples X;
based on a group of samples in a plurality of groups of target cycle skip value samples X and a weight matrix
Determining an initial output matrix
Re-inputting another set of samples, updating the weight matrix wtAnd an output matrix yt；
If the loss function L meets a preset threshold condition, the reliability evaluation model is trained; otherwise, re-inputting other groups of samples and updating the weight matrix wtAnd an output matrix ytUntil the loss function L satisfies a preset threshold condition.
Optionally, the evaluation module, the loss function
Wherein N is the number of input sample groups, y is the output matrix, X is the target cycle slip value sample, and omega is the weight value.
In a third aspect, an embodiment of the present invention 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 one of the methods when executing the computer program.
In a fourth aspect, an embodiment of the invention provides a computer-readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement the steps of any of the methods described above.
By adopting the technical scheme, the application has the following beneficial effects:
(1) the invention provides a multi-cycle slip detection and restoration algorithm combined model architecture, which can effectively overcome the problems of a single model or a specific combined model by comprehensively voting the cycle slip detection result through a combined model, greatly improve the sensitivity of cycle slip detection and realize more comprehensive detection.
(2) After the traditional cycle slip detection and repair algorithm is used for calculating the cycle slip, an optimal solution is obtained, however, due to the fact that pseudo-range observed quantity is low in precision, ambiguity is uncertain, and various factors such as residual errors caused by incomplete consideration in a resolving process are not used, the precision of the obtained cycle slip repair value is insufficient. According to the cycle slip optimal value fast search method, the cycle slip optimal value search space is constructed, and a cycle slip optimal value fast search algorithm based on the adaptive moment estimation algorithm is provided, so that the influence of various residual errors on the cycle slip is effectively eliminated; in the aspect of search efficiency, compared with the traditional traversal algorithm, the cycle slip optimal value can be quickly obtained without traversal in a large-scale search space, and the operation efficiency is greatly improved.
(3) Aiming at the problem that the traditional cycle slip detection and repair algorithm is difficult to effectively evaluate the solved cycle slip value and the algorithm, the invention provides a reliability evaluation model.
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In order to more clearly illustrate the detailed description of the invention or the technical solutions in the prior art, the drawings that are needed in the detailed description of the invention or the prior art will be briefly described below. Throughout the drawings, like elements or portions are generally identified by like reference numerals. In the drawings, elements or portions are not necessarily drawn to scale.
FIG. 1 is a flow chart of a cycle slip detection and recovery method according to an embodiment of the present invention;
FIG. 2 is a flow chart of a cycle slip detection and recovery method according to an embodiment of the present invention;
FIG. 3 is a block diagram of a cycle slip detection and remediation apparatus according to an embodiment of the present invention;
fig. 4 shows a block diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
Embodiments of the present invention will be described in detail below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and therefore are only examples, and the protection scope of the present invention is not limited thereby.
It is to be noted that, unless otherwise specified, technical or scientific terms used herein shall have the ordinary meaning as understood by those skilled in the art to which the invention pertains.
In the GNSS measured data process, due to the influence of factors such as signal blockage, hardware failure, atmospheric disturbance, surrounding observation environment and the like, various conditions such as large and small cycle slips, continuous cycle slips, special combined cycle slips and the like may occur. The existing determined cycle slip detection and restoration methods are all directed at improving the detection effect of partial cycle slips, and although the detection and restoration effects can be improved for the applicable cycle slips, the universality is not high, the improvement of the restoration effect of most cycle slips cannot be met, and a method with high coverage and good restoration effect on cycle slip detection and restoration is urgently needed for improving the universality and the restoration effect of cycle slip detection and restoration.
To solve the above problem, fig. 1 shows a flowchart of a cycle slip detection and recovery method according to an embodiment of the present invention. As shown in fig. 1, the cycle slip detection and repair method according to the embodiment of the present invention includes:
s101, preprocessing the data and eliminating the failed satellite.
S1011: and establishing analytic models 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: setting a pseudo-range observation threshold, and removing satellite frequency point information corresponding to the observation for pseudo-range observations which do not meet the threshold requirement.
S1013: and performing positioning calculation to obtain the rough position of the receiver.
S1014: performing Receiver Autonomous Integrity Monitoring (RAIM), and obtaining pseudo-range residual information by adopting a pseudo-range residual detection method (after positioning);
S＝I-G(GTCG)-1GTC，
wherein the vector b is a pseudo range residue before positioning,
is a pseudo-range observation, r, for the nth satellite(n)(x0) Is the geometric distance, deltat, between the nth satellite of the previous epoch and the targetu,0The receiver clock difference, vector, for the previous epoch
For pseudo-range residue after positioning, a matrix S is a state transition matrix for converting residue before positioning into residue after positioning, a Jacobian matrix G is a geometric matrix, and C is a weight matrix.
S1015: setting chi by using the least square residue method2The distributed freedom degree and false alarm rate parameters detect pseudo-range residual information after positioning and eliminate wrong satellites;
wherein the scalar εWSSEIs the length squared of the weighted residual vector.
S1016: and re-executing positioning calculation to obtain a target rough position, executing RAIM detection, returning to S1014 if a fault satellite is detected, otherwise completing RAIM detection of pseudo-range observed quantity, and entering S1017.
S1017: and (4) calculating altitude angle information, and deleting satellites with elevation angles lower than 10 degrees to ensure the accuracy of pseudo-range observed quantity.
S102, combining the multiple cycle slip detection models to obtain a rough output value of the cycle slip.
Specifically, cycle slip detection is performed based on a combined framework of multiple 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 an adaptive moment estimation algorithm.
Optionally, step S102 includes:
s1021, establishing a combined model based on multiple cycle slip detection methods
Judging whether cycle slip exists or not based on the combined model; wherein, Δ N1，ΔN2，…，ΔNmThe number of cycle slip detection methods.
Specifically, a set of various cycle slip detection and repair algorithms is constructed
Where N is the model number of the cycle slip detection and repair algorithm, CSDRiIs a specific model expression comprising a pseudorange 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 VthThe cycle slip matrix Δ N is a function of the correlation. By means of algorithm combination, the cycle slip searching range can be expanded, and cycle slip detection is more comprehensive.
In a possible implementation mode, the algorithm combination of MW combination + GF combination + high-order difference method + ionosphere residue method is selected for comprehensive judgment, so that the detection of the cycle slip of the large and small cycle slips and the cycle slip of the special combination can be basically realized, and the search range of the cycle slip is covered as much as possible.
And S1022, if the cycle slip exists, establishing a combined cycle slip repair model.
Specifically, if the cycle slip is detected, Cs can be used for the fundamental model of cycle slip restorationiAnd (P, L, λ, ∈, Δ N) ═ 0. For a system to be tested, parameters such as frequency point number P, pseudo range L, error term epsilon and the like are fixed values, variables are only cycle slip values of all frequency points, and then a cycle slip restoration basic model can be described as yi＝Csi(ΔN1,ΔN2,...,ΔNm) Wherein y isiAnd outputting the combination of the parameters. Then combining the n cycle slip repair models can be expressed as
S1023, obtaining solution N of combined cycle slip restoration model based on least square methodOPT；NOPTI.e. the coarse output value.
Specifically, a vector N is obtained by a least square method based on N types of combined cycle slip repairing modelsOPTObtaining a vector NOPTIs a set of fractional unstable solutions that may also contain pseudoranges and some residual errors.
Alternatively, referring to fig. 2, step S103 includes:
s1031, constructing an objective function J (x) based on the combined cycle slip repairing model;
s1032, determining a search space by taking the rough output value as a search center and taking +/-n weeks as a search range;
and S1033, searching in a search space based on an adaptive moment estimation algorithm, and determining a target cycle slip value.
Specifically, aiming at the combined cycle slip repair model, an objective function J (x) is constructed by taking the minimum sum of squares as an evaluation standard,
vector N obtained in step S1023OPTAnd (4) for a search center, considering pseudo-range observed quantity errors and uncertainty caused by incomplete elimination of partial error terms in the calculation process, and constructing a cycle slip target value search space by taking +/-n weeks 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, comprising:
s1033.1, inputting the rough output value as the search starting point X0Phasor, and inputting step length alpha of self-adaptive moment estimation, and exponential decay rate beta of moment estimation1、β2Search termination threshold VthThe numerical stability is small constant δ.
S1033.2, updating the partial first moment estimation StSum-biased second moment estimate rtAnd correcting the estimated deviation s of the first order momentt' sum-bias second moment estimation bias rt′。
The objective function gradient f (x) is calculated,
wherein t is the number of searches and the vector X is the cycle slip search value.
Updating the partial first moment estimate st，st＝β1st-1+(1-β1)/f(Xt)。
Updating the partial second moment estimate rt，rt＝β2rt-1+(1-β2)/f(Xt)2。
Correcting the offset first order moment estimate deviation st′，
Correcting the estimated bias r of the second momentt′，
S1033.3, acquiring cycle slip search variation quantity delta Xt，
S1033.4, updating the cycle slip searching value Xt，Xt＝Xt-1+ΔXt。
S1033.5, searching value X for cycle sliptSatisfying a search termination threshold VthThen the target cycle slip value is output.
Specifically, if the cycle slip search value XtSatisfying a search termination threshold VthIf yes, the cycle slip search value in step S1033.4 is the cycle slip target value; if cycle slip search value XtUnsatisfied search termination threshold VthThen, the procedure returns to step S1033.2 to obtain the gradient f (X) of the objective function again and obtain the updated cycle slip search value XtUntil a search termination threshold V is metth。
Optionally, the method further comprises:
and outputting the reliability evaluation value of the target cycle slip value based on the trained reliability evaluation model.
Specifically, the traditional cycle slip detection and restoration method is difficult to effectively evaluate a calculated cycle slip value and an algorithm, and aiming at the problem, the embodiment of the application provides a reliability evaluation model.
Optionally, the method for outputting the reliability evaluation value of the target cycle slip value based on the trained reliability evaluation model specifically includes the following steps:
obtaining an evaluation function based on the trained credibility evaluation model; wherein the evaluation function Eval _ Cs is ω1Cs1+ω2Cs2+ω3Cs3+...+ωmCsm；
And inputting the target cycle slip value into an evaluation function to obtain a credibility evaluation value.
Specifically, based on the trained reliability evaluation model, the 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. If the target cycle slip value is acquired in step S103, the reliability evaluation is not performed in this step.
Optionally, the reliability evaluation model is trained by:
acquiring an evaluation function Eval _ Cs ═ WiCsi(i ═ 1,2,. multidot., m-1); where m is the number of models participating in the evaluation, WiIs a coefficient matrix, Cs, of each cycle slip modeliIs a cycle slip model expression;
acquiring a plurality of groups of target cycle skip value samples X;
based on a set of samples in a plurality of sets of target cycle skip value samples X and a weight matrix
Determining an initial output matrix
Re-inputting another set of samples, updating the weight matrix wtAnd an output matrix yt；
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 wtAnd an output matrix ytUntil the loss function L satisfies 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. Firstly, a group of target cycle slip values in a group of target cycle slip value samples are input into a reliability evaluation model, and an initial output matrix y is obtained. Then, re-inputting another set of target cycle slip values, updating the weight matrix wtAnd an output matrix yt. And repeating the process of continuously inputting different groups of target week jump values until the grandson function L meets the preset threshold condition.
Optionally, a loss function
Where N is the number of input sample sets.
In particular, the loss function
Wherein N is the number of input sample groups; wherein y is an output matrix, and X is input sample data; and omega is a weight value.
When L is minimum, its partial derivative to the weight is 0, then there are:
based on the formula ω ═ XTX)-1XTy, updating the weight matrix wtAnd based on the formula Eval _ Cs (w, Δ N) ═ wTCsi(Δ N) updating output matrix ytAnd determining that the reliability evaluation model training is finished until the loss function L meets the preset threshold condition. By setting the specific threshold condition of the loss function, the cycle slip detection and restoration precision can be ensured, and the restoration effect is improved.
In one specific example, the preset threshold condition may be set to 10000 iterations or L satisfying less than 0.001 precision.
In one embodiment, referring to fig. 3, there is provided a cycle slip detection and repair apparatus 20 comprising:
the preprocessing module 201 is used for preprocessing data and eliminating failed satellites;
the first output module 202 is configured to combine multiple cycle slip detection models, and if cycle slips exist, obtain a rough output value of the cycle slips;
and a second output module 203, configured to determine 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:
establishing a combined model based on multiple cycle slip detection methods
Judging whether cycle slip exists or not based on the combined model; wherein, Δ N1，ΔN2，…，ΔNmThe number of cycle slip detection methods;
if the cycle slip exists, establishing a combined cycle slip repairing model;
obtaining solution N of the combined cycle slip repairing model based on least square methodOPT；NOPTI.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;
and searching in the search space based on an adaptive moment estimation algorithm to determine the target cycle slip value.
Optionally, the second output module is specifically further configured to:
inputting the coarse output value as a search starting point X0Phasor, and inputting step length alpha of self-adaptive moment estimation, and exponential decay rate beta of moment estimation1、β2Search termination threshold VthA numerical stability small constant δ;
updating the partial first moment estimate stSum-biased second moment estimate rtAnd correcting the estimated deviation s of the first order momentt' sum-bias second moment estimation bias rt′；
Obtaining cycle slip search variation quantity delta Xt；
Updating cycle slip search value Xt；
If the cycle slip search value XtSatisfying a search termination threshold VthThen the target cycle slip value is output.
Optionally, the apparatus further includes an evaluation module, configured to output a reliability evaluation value of the target cycle slip value based on a trained reliability evaluation model.
Optionally, the evaluation module is specifically further configured to:
obtaining an evaluation function based on the trained credibility evaluation model; wherein the evaluation function Eval _ Cs is ω1Cs1+ω2Cs2+ω3Cs3+...+ωmCsm；
And inputting the target cycle slip value into the evaluation function to obtain a credibility evaluation value.
Optionally, in the evaluation module, the reliability evaluation model is trained by the following method:
acquiring an evaluation function Eval _ Cs ═ WiCsi(i ═ 1,2,. multidot., m-1); wherein m is the participation in the evaluationNumber of model (W)iIs a coefficient matrix, Cs, of each cycle slip modeliIs a cycle slip model expression;
acquiring a plurality of groups of target cycle skip value samples X;
based on a group of samples in a plurality of groups of target cycle skip value samples X and a weight matrix
Determining an initial output matrix
Re-inputting another set of samples, updating the weight matrix wtAnd an output matrix yt；
If the loss function L meets a preset threshold condition, the reliability evaluation model is trained; otherwise, re-inputting other groups of samples and updating the weight matrix wtAnd an output matrix ytUntil the loss function L satisfies a preset threshold condition.
Optionally, the evaluation module, the loss function
Wherein N is the number of input sample groups, y is the output matrix, X is the target cycle slip value sample, and omega is the weight value.
The cycle slip detecting and repairing device 20 provided in the embodiment of the present application and the cycle slip detecting and repairing method described above adopt the same inventive concept, and can obtain the same beneficial effects, which are not described herein again.
Based on the same inventive concept as the cycle slip detection and repair method, the embodiment of the present application further provides an electronic device 30, as shown in fig. 4, 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), a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, a discrete Gate or transistor logic device, or a discrete hardware component, and may implement or execute the methods, steps, and logic blocks disclosed in the embodiments of the present Application. The steps of a method disclosed in connection with the embodiments of the present application may be directly implemented by a hardware processor, or may be implemented by a combination of hardware and software modules in a processor.
Those of ordinary skill in the art will understand that: all or part of the steps for implementing the method embodiments may be implemented by hardware related to program instructions, and the program may be stored in a computer readable storage medium, and when executed, the program performs the steps including the method embodiments; the computer storage media may 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 codes include a removable Memory device, a Random Access Memory (RAM), a magnetic Memory (e.g., a flexible disk, a hard disk, a magnetic tape, a magneto-optical disk (MO), etc.), an optical Memory (e.g., a CD, a DVD, a BD, an HVD, etc.), and a semiconductor Memory (e.g., a ROM, an EPROM, an EEPROM, a nonvolatile Memory (NAND FLASH), a Solid State Disk (SSD)). Alternatively, the integrated units described above in the present application may be stored in a computer-readable storage medium if they are implemented in the form of software functional modules and sold or used as independent products.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the present invention, and they should be construed as being included in the following claims and description.
Claims (10)
1. A cycle slip detection and remediation method, comprising:
preprocessing the data and eliminating the failed satellite;
combining multiple cycle slip detection models, and if cycle slips exist, acquiring a rough output value of the cycle slips;
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 values.
2. The method of claim 1, wherein said combining the plurality of cycle slip detection models to obtain a coarse output value of cycle slip if cycle slip is present comprises:
establishing a combined model based on multiple cycle slip detection methods
Judging whether cycle slip exists or not based on the combined model; wherein, Δ N1，ΔN2，…，ΔNmThe number of cycle slip detection methods;
if the cycle slip exists, establishing a combined cycle slip repairing model;
obtaining solution N of the combined cycle slip repairing model based on least square methodOPT；NOPTI.e. the coarse output value.
3. The method of claim 2, wherein the determining a target cycle slip value in the constructed search space based on the adaptive moment estimation algorithm comprises:
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;
and searching in the search space based on an adaptive moment estimation algorithm to determine the target cycle slip value.
4. The method of claim 3, wherein the determining a target cycle slip value in the constructed search space based on the adaptive moment estimation algorithm comprises:
inputting the coarse output value as a search starting point X0Phasor, and inputting step length alpha of self-adaptive moment estimation, and exponential decay rate beta of moment estimation1、β2Search termination threshold VthA numerical stability small constant δ;
updating the partial first moment estimate stSum-biased second moment estimate rtAnd correcting the estimated deviation s of the first order momentt' sum-bias second moment estimation bias rt′；
Obtaining cycle slip search variation quantity delta Xt；
Updating cycle slip search value Xt；
If the cycle slip search value XtSatisfying a search termination threshold VthThen the target cycle slip value is output.
5. The method of claim 1, further comprising:
and outputting the reliability evaluation value of the target cycle slip value based on the trained reliability evaluation model.
6. The method of claim 5, wherein outputting the confidence evaluation value of the target cycle slip value based on the trained confidence evaluation model comprises:
obtaining an evaluation function based on the trained credibility evaluation model; wherein the evaluation function Eval _ Cs is ω1Cs1+ω2Cs2+ω3Cs3+...+ωmCsm；
And inputting the target cycle slip value into the evaluation function to obtain a credibility evaluation value.
7. The method of claim 6, wherein the confidence evaluation model is trained by:
acquiring an evaluation function Eval _ Cs ═ WiCsi(i ═ 1,2,. multidot., m-1); where m is the number of models participating in the evaluation, WiIs a coefficient matrix, Cs, of each cycle slip modeliIs a cycle slip model expression;
acquiring a plurality of groups of target cycle skip value samples X;
based on a group of samples in a plurality of groups of target cycle skip value samples X and a weight matrix
Determining an initial output matrix
Re-inputting another set of samples, updating the weight matrix wtAnd an output matrix yt；
If the loss function L meets a preset threshold condition, the reliability evaluation model is trained; otherwise, re-inputting other groups of samples and updating the weight matrix wtAnd an output matrix ytUntil the loss function L satisfies a preset threshold condition.
8. The method of claim 7, wherein the loss function
Wherein N is the number of input sample groups, y is the output matrix, X is the target cycle slip value sample, and omega is the weight value.
9. A cycle slip detection and remediation device, comprising:
the preprocessing module is used for preprocessing the data and eliminating the failed satellite;
the first output module is used for combining various cycle slip detection models, and acquiring a rough output value of the 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;
and the evaluation module is used for outputting the reliability evaluation value of the target cycle slip value based on the trained reliability evaluation model.
10. 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 steps of the method of any of claims 1 to 8 are implemented when the computer program is executed by the processor.
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