CN115629398A - Satellite fault identification method and device, electronic equipment and storage medium - Google Patents

Satellite fault identification method and device, electronic equipment and storage medium Download PDF

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Publication number
CN115629398A
CN115629398A CN202211227991.8A CN202211227991A CN115629398A CN 115629398 A CN115629398 A CN 115629398A CN 202211227991 A CN202211227991 A CN 202211227991A CN 115629398 A CN115629398 A CN 115629398A
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singular value
matrix
satellite
value space
vector
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陈兵
杨志坤
韩依萌
汤青
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Chongqing Starnav Systems Co ltd
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Chongqing Starnav Systems Co ltd
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/01—Satellite radio beacon positioning systems transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/13—Receivers
    • G01S19/20—Integrity monitoring, fault detection or fault isolation of space segment

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Abstract

The application relates to the technical field of satellite navigation, and provides a satellite fault identification method, a satellite fault identification device, electronic equipment and a storage medium, wherein the method comprises the following steps: acquiring an observation coefficient matrix; performing singular value decomposition on the observation coefficient matrix to obtain an orthogonal matrix; obtaining a singular value space vector according to the orthogonal matrix; calculating test statistic according to the singular value space vector; and performing fault identification on the satellite according to the test statistic so as to identify the fault satellite. The method can better meet the requirement of integrity monitoring and effectively improve the accuracy of the fault identification result of the satellite.

Description

Satellite fault identification method and device, electronic equipment and storage medium
Technical Field
The present application relates to the field of satellite navigation technologies, and in particular, to a method and an apparatus for identifying a satellite fault, an electronic device, and a storage medium.
Background
The Receiver Autonomous Integrity Monitoring (RAIM) technology provides an important guarantee for civil aviation safety Monitoring, and the Receiver Autonomous Integrity Monitoring technology can also be used for satellite fault identification. The existing satellite fault identification method based on the receiver autonomous integrity monitoring technology adopts a least square residual method to eliminate errors. However, when the least square residual method is adopted for error elimination, the problem of ill-condition of the coefficient matrix exists, and the ill-condition of the coefficient matrix can cause large fluctuation of a final calculation result due to small fluctuation of observed quantity, so that the accuracy of autonomous integrity monitoring of the receiver is reduced.
Therefore, in other satellite fault identification methods, a weighted total least square method is introduced to determine the square sum of the pseudo-range residual vector and the weighted pseudo-range residual vector on the basis of the original least square residual method, so that the error square sum of the data quantity and the observed quantity can be controlled to be minimum, and the problem of coefficient matrix morbidity is solved. However, each component in the pseudo-range residual error vector has certain relevance, so that some important inconsistency information is covered, and the fault identification accuracy of the satellite is further influenced.
Based on the above problems, no effective solution exists at present.
Disclosure of Invention
The invention aims to provide a satellite fault identification method, a satellite fault identification device, electronic equipment and a storage medium, which can improve the fault identification accuracy of a satellite.
In a first aspect, the present application provides a method for identifying a satellite fault, including the following steps:
s1, acquiring an observation coefficient matrix;
s2, performing singular value decomposition on the observation coefficient matrix to obtain an orthogonal matrix;
s3, obtaining a singular value space vector according to the orthogonal matrix;
s4, calculating test statistic according to the singular value space vector;
and S5, carrying out fault identification on the satellite according to the test statistic.
According to the satellite fault identification method, an observation coefficient matrix is obtained; performing singular value decomposition on the observation coefficient matrix to obtain an orthogonal matrix; obtaining a singular value space vector according to the orthogonal matrix; calculating test statistic according to the singular value space vector; and performing fault identification on the satellite according to the test statistic so as to identify the fault satellite. According to the method, the observation coefficient matrix in the pseudo-range observation matrix is decomposed by singular value decomposition to obtain singular value space vectors, test statistics capable of directly reflecting deviation information of a fault satellite is constructed based on the singular value space vectors, so that the fault satellite can be identified, the requirement of integrity monitoring is better met, the problems that due to the fact that each component in a residual vector has certain relevance in satellite integrity monitoring, certain inconsistent information is hidden by the existing least square residual method are effectively avoided, the algorithm implementation complexity is high, and the satellite fault identification is prevented from being inaccurate.
Optionally, in the satellite fault identification method provided by the present application, in step S1, the observation coefficient matrix is obtained by solving the following equation:
Figure 378320DEST_PATH_IMAGE002
wherein the content of the first and second substances,
Figure 100002_DEST_PATH_IMAGE003
a residual vector of the pseudo-range observation; h is an observation coefficient matrix;
Figure 648896DEST_PATH_IMAGE004
an observed pseudo-range noise vector;
Figure 100002_DEST_PATH_IMAGE005
is a user state vector.
Optionally, in the satellite fault identification method provided by the present application, in step S2, singular value decomposition is performed on the observation coefficient matrix according to the following formula, so as to obtain the orthogonal matrix:
Figure 100002_DEST_PATH_IMAGE007
wherein, the first and the second end of the pipe are connected with each other,
Figure 666530DEST_PATH_IMAGE008
is an observation coefficient matrix;
Figure 100002_DEST_PATH_IMAGE009
is an orthogonal matrix; d is a diagonal singular value matrix;
Figure 629938DEST_PATH_IMAGE010
is a feature vector; t is the transposed symbol.
Optionally, in the satellite fault identification method provided by the present application, step S3 includes:
A1. acquiring a singular value space matrix according to the orthogonal matrix;
A2. and calculating the singular value space vector according to the singular value space matrix and the residual vector of the pseudo-range observation value.
Optionally, in the satellite fault identification method provided by the present application, in step A2, the singular value space vector is calculated according to the following formula:
Figure 740431DEST_PATH_IMAGE012
wherein the content of the first and second substances,
Figure 100002_DEST_PATH_IMAGE013
the singular value space vector is taken as the singular value space vector;
Figure 798386DEST_PATH_IMAGE014
is a singular value space matrix;
Figure 217997DEST_PATH_IMAGE003
a residual vector that is the pseudorange observation.
Optionally, in the satellite fault identification method provided by the present application, step S3 includes:
B1. obtaining a singular value space matrix according to the orthogonal matrix;
B2. and calculating the singular value space vector according to the singular value space matrix and the observed pseudo-range noise vector.
Optionally, in the satellite fault identification method provided by the present application, in step S4, a calculation formula of the test statistic is as follows:
Figure 804836DEST_PATH_IMAGE016
wherein, the first and the second end of the pipe are connected with each other,
Figure 100002_DEST_PATH_IMAGE017
is the test statistic;
Figure 700111DEST_PATH_IMAGE013
the singular value space vector is taken as the singular value space vector;
Figure 46910DEST_PATH_IMAGE018
the number of rows of the orthogonal matrix is shown; t is a transposed symbol;
Figure 100002_DEST_PATH_IMAGE019
is a weighting coefficient matrix.
In practical application, due to singular value space vector
Figure 911573DEST_PATH_IMAGE013
Observation error information is directly reflected, and test statistic can be calculated based on singular value space vectors, so that fault monitoring is performed, a fault satellite can be accurately identified, and the requirement of integrity monitoring is better met.
According to the satellite fault identification method, an observation coefficient matrix is obtained; performing singular value decomposition on the observation coefficient matrix to obtain an orthogonal matrix; obtaining a singular value space vector according to the orthogonal matrix; calculating test statistic according to the singular value space vector; and performing fault identification on the satellite according to the test statistic to identify the fault satellite. According to the method, the observation coefficient matrix in the pseudo-range observation matrix is decomposed by singular value decomposition to obtain singular value space vectors, test statistics capable of directly reflecting deviation information of a fault satellite is constructed based on the singular value space vectors, so that the fault satellite can be identified, the requirement of integrity monitoring is better met, the problems that due to the fact that each component in a residual vector has certain relevance in satellite integrity monitoring, certain inconsistent information is hidden by the existing least square residual method are effectively avoided, the algorithm implementation complexity is high, and the satellite fault identification is prevented from being inaccurate.
In a second aspect, the present application provides a satellite fault identification apparatus, including the following modules:
a first obtaining module: the system is used for acquiring an observation coefficient matrix;
a second obtaining module: the observation coefficient matrix is subjected to singular value decomposition to obtain an orthogonal matrix;
a third obtaining module: the system is used for acquiring a singular value space vector according to the orthogonal matrix;
constructing a module: for computing test statistics from the singular value space vectors;
an identification module: and the satellite fault identification module is used for carrying out fault identification on the satellite according to the test statistic so as to identify the fault satellite.
According to the satellite fault identification device, the observation coefficient matrix is obtained through the first obtaining module; the second acquisition module performs singular value decomposition on the observation coefficient matrix to acquire an orthogonal matrix; a third acquisition module acquires a singular value space vector according to the orthogonal matrix; the construction module calculates test statistic according to the singular value space vector; the identification module identifies the satellite as a failed satellite based on the test statistics to identify the failed satellite. According to the method, the observation coefficient matrix in the pseudo-range observation matrix is decomposed by singular value decomposition to obtain singular value space vectors, test statistics capable of directly reflecting deviation information of a fault satellite is constructed based on the singular value space vectors, so that the fault satellite can be identified, the requirement of integrity monitoring is better met, the problems that due to the fact that each component in a residual vector has certain relevance in satellite integrity monitoring, certain inconsistent information is hidden by the existing least square residual method are effectively avoided, the algorithm implementation complexity is high, and the satellite fault identification is prevented from being inaccurate.
In a third aspect, the present application provides an electronic device comprising a processor and a memory, wherein the memory stores computer readable instructions, and the computer readable instructions, when executed by the processor, perform the steps of the method as provided in the first aspect.
In a fourth aspect, the present application provides a storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the method as provided in the first aspect above.
In summary, according to the satellite fault identification method, the satellite fault identification device, the electronic equipment and the storage medium, the observation coefficient matrix in the pseudo-range observation matrix is decomposed by singular value decomposition to obtain the singular value space vector, and the test statistic which can directly reflect the deviation information of the fault satellite is constructed based on the singular value space vector, so that the fault satellite can be identified, the requirement of integrity monitoring is better met, the problem that certain inconsistent information is hidden due to the fact that each component in a residual vector has certain relevance in satellite integrity monitoring by the existing least square residual method is effectively solved, the problem of high algorithm implementation complexity is solved, and the problem of inaccurate satellite fault identification is prevented.
Drawings
Fig. 1 is a flowchart of a satellite fault identification method provided in the present application.
Fig. 2 is a schematic structural diagram of a satellite fault identification apparatus provided in the present application.
Fig. 3 is a schematic structural diagram of an electronic device provided in the present application.
Description of reference numerals:
201. a first acquisition module; 202. a second acquisition module; 203. a third obtaining module; 204. constructing a module; 205. an identification module; 301. a processor; 302. a memory; 303. a communication bus.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only some embodiments of the present application, and not all embodiments. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present application, as presented in the figures, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments, which can be obtained by a person skilled in the art without making creative efforts based on the embodiments of the present application, belong to the protection scope of the present application.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures. Meanwhile, in the description of the present application, the terms "first", "second", and the like are used only for distinguishing the description, and are not to be construed as indicating or implying relative importance.
Referring to fig. 1, fig. 1 is a flowchart of a satellite fault identification method according to some embodiments of the present disclosure, which includes the following steps:
s1, acquiring an observation coefficient matrix;
s2, performing singular value decomposition on the observation coefficient matrix to obtain an orthogonal matrix;
s3, acquiring a singular value space vector according to the orthogonal matrix;
s4, calculating test statistic according to the singular value space vector;
and S5, carrying out fault identification on the satellite according to the test statistic to identify the fault satellite.
According to the satellite fault identification method, an observation coefficient matrix is obtained; performing singular value decomposition on the observation coefficient matrix to obtain an orthogonal matrix; obtaining a singular value space vector according to the orthogonal matrix; calculating test statistic according to the singular value space vector; and performing fault identification on the satellite according to the test statistic to identify the fault satellite. According to the method, the observation coefficient matrix in the pseudo-range observation matrix is decomposed by singular value decomposition to obtain singular value space vectors, test statistics capable of directly reflecting deviation information of a fault satellite is constructed based on the singular value space vectors, so that the fault satellite can be identified, the requirement of integrity monitoring is better met, the problems that due to the fact that each component in a residual vector has certain relevance in satellite integrity monitoring, certain inconsistent information is hidden by the existing least square residual method are effectively avoided, the algorithm implementation complexity is high, and the satellite fault identification is prevented from being inaccurate.
In a further embodiment, in step S1, the observation coefficient matrix is obtained by solving the following equation:
Figure DEST_PATH_IMAGE021
wherein, the first and the second end of the pipe are connected with each other,
Figure 685625DEST_PATH_IMAGE003
a residual vector of the pseudo-range observed value; h is an observation coefficient matrix;
Figure 723989DEST_PATH_IMAGE004
an observed pseudo-range noise vector;
Figure 530271DEST_PATH_IMAGE005
is a user state vector.
In the practical application of the method, the air conditioner,
Figure 455632DEST_PATH_IMAGE003
a residual vector which is an nx1 dimensional pseudo-range observed value; n is the number of visible satellites and is a positive integer greater than 4;
Figure 321957DEST_PATH_IMAGE005
the user state vector with 4 x 1 dimension generally comprises 3 user receiver position vector correction quantities and 1 receiver clock correction quantity; epsilon is an nx1 dimensional observation pseudo-range noise vector; the observed pseudo-range noise vector, the residual vector of the pseudo-range observed value and the user state vector can be obtained through a receiver in the prior art.
In a further embodiment, in step S2, the observation coefficient matrix is subjected to singular value decomposition according to the following formula to obtain an orthogonal matrix:
Figure DEST_PATH_IMAGE023
wherein the content of the first and second substances,
Figure 660666DEST_PATH_IMAGE008
is an observation coefficient matrix;
Figure 270639DEST_PATH_IMAGE009
is an orthogonal matrix; d is a diagonal singular value matrix;
Figure 299775DEST_PATH_IMAGE010
characteristic vectors of the observation coefficient matrix; t is the transposed symbol.
The specific process of singular value decomposition is the prior art, and the manner of obtaining the eigenvectors of the diagonal singular value matrix and the observation coefficient matrix is the prior art, which is not described in detail here.
In a further embodiment, step S3 comprises:
A1. acquiring a singular value space matrix according to the orthogonal matrix;
A2. and calculating a singular value space vector according to the singular value space matrix and the residual error vector of the pseudo-range observed value.
In step A2, the singular value space vector is calculated according to the following formula:
Figure DEST_PATH_IMAGE025
wherein the content of the first and second substances,
Figure 621821DEST_PATH_IMAGE013
is a singular value space vector;
Figure 369197DEST_PATH_IMAGE014
is a singular value space matrix;
Figure 782861DEST_PATH_IMAGE003
as pseudo-range observationsA residual vector of values.
Wherein the content of the first and second substances,
Figure 869766DEST_PATH_IMAGE009
is an orthogonal matrix of n x n dimensions, such that
Figure 343473DEST_PATH_IMAGE026
，
Figure DEST_PATH_IMAGE027
Is composed of
Figure 594456DEST_PATH_IMAGE028
The first 4 lines of the first three (u),
Figure DEST_PATH_IMAGE029
is composed of
Figure 77390DEST_PATH_IMAGE028
The remaining (n-4) rows. Thus, step A1 comprises: and extracting the last n-4 rows of data of the orthogonal matrix to obtain a singular value space matrix.
In other embodiments, step S3 comprises:
B1. acquiring a singular value space matrix according to the orthogonal matrix (in the concrete process, referring to the step A1);
B2. and calculating a singular value space vector according to the singular value space matrix and the observed pseudo range noise vector.
In step B2, the singular value space vector is calculated according to the following formula:
Figure 831851DEST_PATH_IMAGE030
wherein, the first and the second end of the pipe are connected with each other,
Figure 210880DEST_PATH_IMAGE013
is a singular value space vector;
Figure 932848DEST_PATH_IMAGE014
is a singular value space matrix;
Figure 422735DEST_PATH_IMAGE004
to observe the pseudorange noise vector.
In practical application, the singular value space vector is a projection vector of an observation pseudo-range noise vector on a singular value space matrix, and can directly reflect the deviation information of a fault satellite.
Since the observation error is based on the singular value space matrix
Figure 297281DEST_PATH_IMAGE029
Each column of the singular value space vector is reflected to the singular value space vector, so that the singular value space vector has necessary connection with the column of the singular value space matrix, and the fault satellite can be identified by the geometrical property between the singular value space vector and the column of the singular value space matrix, and the test statistic can be constructed on the basis of the singular value space vector and the column of the singular value space matrix for fault detection and identification.
In a further embodiment, in step S4, the test statistic is calculated as follows:
Figure 112791DEST_PATH_IMAGE032
wherein, the first and the second end of the pipe are connected with each other,
Figure 56476DEST_PATH_IMAGE017
is a test statistic;
Figure 350054DEST_PATH_IMAGE013
is a singular value space vector;
Figure 810598DEST_PATH_IMAGE018
the number of rows of the orthogonal matrix; t is a transposed symbol;
Figure 797008DEST_PATH_IMAGE019
is a weighting coefficient matrix.
The obtaining mode of the weighting coefficient matrix is the prior art, specifically:
Figure 227990DEST_PATH_IMAGE034
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE035
representing the observation noise variance of the nth satellite, wherein the acquisition mode is the prior art; c represents an observation noise covariance matrix of the nth satellite; and then
Figure 341570DEST_PATH_IMAGE036
。
In practical application, due to singular value space vector
Figure 439976DEST_PATH_IMAGE013
Observation error information is directly reflected, and the test statistic can be calculated based on the singular value space vector, so that fault monitoring is performed, the accuracy of fault satellite identification can be improved, and the requirement of integrity monitoring can be better met.
In step S5, the fault identification may determine that the second satellite has a fault by using the existing barda gross error detection method.
According to the satellite fault identification method, the observation coefficient matrix is obtained; performing singular value decomposition on the observation coefficient matrix to obtain an orthogonal matrix; obtaining a singular value space vector according to the orthogonal matrix; calculating test statistic according to the singular value space vector; and performing fault identification on the satellite according to the test statistic so as to identify the fault satellite. According to the method, the observation coefficient matrix in the pseudo-range observation matrix is decomposed by singular value decomposition to obtain singular value space vectors, test statistics capable of directly reflecting deviation information of a fault satellite is constructed based on the singular value space vectors, so that the fault satellite can be identified, the requirement of integrity monitoring is better met, the problems that due to the fact that each component in a residual vector has certain relevance in satellite integrity monitoring, certain inconsistent information is hidden by the existing least square residual method are effectively avoided, the algorithm implementation complexity is high, and the satellite fault identification is prevented from being inaccurate.
Referring to fig. 2, fig. 2 is a satellite fault identification apparatus according to some embodiments of the present disclosure, which includes the following modules:
the first obtaining module 201: acquiring an observation coefficient matrix;
the second obtaining module 202: performing singular value decomposition on the observation coefficient matrix to obtain an orthogonal matrix;
the third obtaining module 203: obtaining a singular value space vector according to the orthogonal matrix;
a construction module 204: calculating test statistic according to the singular value space vector;
the recognition module 205: for fault identification of the satellite based on the test statistics.
In a further embodiment, in the first obtaining module 201, the observation coefficient matrix is obtained by solving the following equation:
Figure 597288DEST_PATH_IMAGE038
wherein the content of the first and second substances,
Figure 266298DEST_PATH_IMAGE003
a residual vector of the pseudo-range observed value; h is an observation coefficient matrix;
Figure 901679DEST_PATH_IMAGE004
an observed pseudo-range noise vector;
Figure 589012DEST_PATH_IMAGE005
is a user state vector.
In the practical application of the method, the air conditioner,
Figure 464695DEST_PATH_IMAGE003
a residual vector of the nx1 dimensional pseudo-range observed value; n is the number of visible satellites and n is a positive integer greater than 4;
Figure 604690DEST_PATH_IMAGE005
the user state vector with 4 x 1 dimension generally comprises 3 user receiver position vector correction quantities and 1 receiver clock correction quantity; epsilon is an nx1 dimensional observation pseudo-range noise vector; observed pseudo-range noise vector, pseudo-range observationThe residual vector of values and the user state vector can be obtained by a receiver in the prior art.
In a further embodiment, in the second obtaining module 202, the observation coefficient matrix is subjected to singular value decomposition according to the following formula to obtain an orthogonal matrix:
Figure 309340DEST_PATH_IMAGE040
wherein, the first and the second end of the pipe are connected with each other,
Figure 851180DEST_PATH_IMAGE008
is an observation coefficient matrix;
Figure 826659DEST_PATH_IMAGE009
is an orthogonal matrix; d is a diagonal singular value matrix;
Figure 453949DEST_PATH_IMAGE010
characteristic vectors of the observation coefficient matrix; t is the transposed symbol.
In a further embodiment, the third obtaining module 203 performs the following steps when obtaining the singular value space vector according to the orthogonal matrix:
A1. acquiring a singular value space matrix according to the orthogonal matrix;
A2. and calculating a singular value space vector according to the singular value space matrix and the residual error vector of the pseudo-range observed value.
In step A2, a singular value space vector is calculated according to the following formula:
Figure 431133DEST_PATH_IMAGE042
wherein, the first and the second end of the pipe are connected with each other,
Figure 93058DEST_PATH_IMAGE013
is a singular value space vector;
Figure 513806DEST_PATH_IMAGE014
as singular valuesA spatial matrix;
Figure 628393DEST_PATH_IMAGE003
a residual vector that is a pseudorange observation.
Wherein, the first and the second end of the pipe are connected with each other,
Figure 206005DEST_PATH_IMAGE009
is an orthogonal matrix of n x n dimensions, such that
Figure 473169DEST_PATH_IMAGE026
，
Figure 314086DEST_PATH_IMAGE027
Is composed of
Figure 915969DEST_PATH_IMAGE028
The first 4 rows of the first row(s),
Figure 234955DEST_PATH_IMAGE029
is composed of
Figure 356625DEST_PATH_IMAGE028
The remaining (n-4) rows. Thus, step A1 comprises: and extracting the last n-4 rows of data of the orthogonal matrix to obtain a singular value space matrix.
In other embodiments, the third obtaining module 203 performs the following steps when obtaining the singular value space vector according to the orthogonal matrix:
B1. acquiring a singular value space matrix according to the orthogonal matrix (in the concrete process, referring to the step A1);
B2. and calculating a singular value space vector according to the singular value space matrix and the observed pseudo range noise vector.
In step B2, the singular value space vector is calculated according to the following formula:
Figure 368444DEST_PATH_IMAGE030
wherein the content of the first and second substances,
Figure 457623DEST_PATH_IMAGE013
is a singular value space vector;
Figure 845879DEST_PATH_IMAGE014
is a singular value space matrix;
Figure 819126DEST_PATH_IMAGE004
is the observed pseudorange noise vector.
In practical application, the singular value space vector is a projection vector of an observation pseudo-range noise vector on a singular value space matrix, and can directly reflect the deviation information of a fault satellite.
Since the observation error is obtained by singular value space matrix
Figure 736267DEST_PATH_IMAGE029
Each column of the singular value space vector is reflected to the singular value space vector, so that the singular value space vector and the columns of the singular value space matrix have necessary connection, and the fault satellite can be identified according to the geometrical property between the singular value space vector and the columns of the singular value space matrix, and the test statistic can be constructed on the basis of the singular value space vector and the columns of the singular value space matrix for fault detection and identification.
In a further embodiment, in the construction module 204, the test statistic is calculated as follows:
Figure 578321DEST_PATH_IMAGE044
wherein the content of the first and second substances,
Figure 504689DEST_PATH_IMAGE017
is a test statistic;
Figure 335372DEST_PATH_IMAGE013
is a singular value space vector;
Figure 954573DEST_PATH_IMAGE018
the number of rows of the orthogonal matrix; t is a transposed symbol;
Figure 752764DEST_PATH_IMAGE019
is a weighting coefficient matrix.
The obtaining mode of the weighting coefficient matrix is the prior art, specifically:
Figure 499135DEST_PATH_IMAGE034
wherein the content of the first and second substances,
Figure 761489DEST_PATH_IMAGE035
representing the observation noise variance of the nth satellite, wherein the acquisition mode is the prior art; c represents an observation noise covariance matrix of the nth satellite; while
Figure 302323DEST_PATH_IMAGE036
。
In practical application, space vector is generated due to singular value
Figure 587810DEST_PATH_IMAGE013
Observation error information is directly reflected, and test statistic can be calculated based on singular value space vectors, so that fault monitoring is performed, the accuracy of fault satellite identification can be improved, and the requirement of integrity monitoring can be better met.
In the identification module 205, the fault identification can determine that the satellite has a fault by using the existing barda gross error detection method.
According to the satellite fault identification device, an observation coefficient matrix is obtained through a first obtaining module 201; the second obtaining module 202 performs singular value decomposition on the observation coefficient matrix to obtain an orthogonal matrix; the third obtaining module 203 obtains a singular value space vector according to the orthogonal matrix; the construction module 204 calculates test statistics from the singular value space vectors; the identification module 205 performs fault identification on the satellites based on the test statistics to identify the faulty satellite. According to the method, the observation coefficient matrix in the pseudo-range observation matrix is decomposed by singular value decomposition to obtain singular value space vectors, test statistics capable of directly reflecting deviation information of a fault satellite is constructed based on the singular value space vectors, so that the fault satellite can be identified, the requirement of integrity monitoring is better met, the problems that due to the fact that each component in a residual vector has certain relevance in satellite integrity monitoring, certain inconsistent information is hidden by the existing least square residual method are effectively avoided, the algorithm implementation complexity is high, and the satellite fault identification is prevented from being inaccurate.
Referring to fig. 3, fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure, where the electronic device includes: the processor 301 and the memory 302, the processor 301 and the memory 302 being interconnected and communicating with each other via a communication bus 303 and/or other form of connection mechanism (not shown), the memory 302 storing computer readable instructions executable by the processor 301, the processor 301 executing the computer readable instructions when the electronic device is running to perform the method in any of the alternative implementations of the above embodiments when executing to implement the following functions: acquiring an observation coefficient matrix; performing singular value decomposition on the observation coefficient matrix to obtain an orthogonal matrix; obtaining a singular value space vector according to the orthogonal matrix; calculating test statistic according to the singular value space vector; and performing fault identification on the satellite according to the test statistic so as to identify the fault satellite.
The present application provides a storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the method in any optional implementation manner of the foregoing implementation manner is executed, so as to implement the following functions: acquiring an observation coefficient matrix; performing singular value decomposition on the observation coefficient matrix to obtain an orthogonal matrix; obtaining a singular value space vector according to the orthogonal matrix; calculating test statistic according to the singular value space vector; and performing fault identification on the satellite according to the test statistic so as to identify the fault satellite. The storage medium may be implemented by any type of volatile or nonvolatile storage device or combination thereof, such as a Static Random Access Memory (SRAM), an Electrically Erasable Programmable Read-Only Memory (EEPROM), an Erasable Programmable Read-Only Memory (EPROM), a Programmable Read-Only Memory (PROM), a Read-Only Memory (ROM), a magnetic Memory, a flash Memory, a magnetic disk, or an optical disk.
In the embodiments provided in the present application, it should be understood that the disclosed system and method may be implemented in other ways. The above-described system embodiments are merely illustrative, and for example, the division of the units is merely a logical division, and there may be other divisions in actual implementation, and for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of systems or units through some communication interfaces, and may be in an electrical, mechanical or other form.
In addition, units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.
In addition, the functional modules in the embodiments of the present application may be integrated together to form an independent part, or each module may exist alone, or two or more modules may be integrated to form an independent part.
In this document, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions.
The above description is only an embodiment of the present application and is not intended to limit the scope of the present application, and various modifications and changes may be made to the present application by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims (10)

1. A satellite fault identification method is characterized by comprising the following steps:
s1, acquiring an observation coefficient matrix;
s2, performing singular value decomposition on the observation coefficient matrix to obtain an orthogonal matrix;
s3, acquiring a singular value space vector according to the orthogonal matrix;
s4, calculating test statistic according to the singular value space vector;
and S5, carrying out fault identification on the satellite according to the test statistic to identify the fault satellite.
2. The method according to claim 1, wherein in step S1, the observation coefficient matrix is obtained by solving the following equation:
Figure 927022DEST_PATH_IMAGE002
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE003
a residual vector of the pseudo-range observed value; h is the observation coefficient matrix;
Figure 135281DEST_PATH_IMAGE004
an observed pseudo-range noise vector;
Figure DEST_PATH_IMAGE005
is a user state vector.
3. The method according to claim 2, wherein in step S2, the observation coefficient matrix is subjected to singular value decomposition according to the following formula to obtain the orthogonal matrix:
Figure DEST_PATH_IMAGE007
wherein the content of the first and second substances,
Figure 808708DEST_PATH_IMAGE008
is an observation coefficient matrix;
Figure DEST_PATH_IMAGE009
is an orthogonal matrix; d is a diagonal singular value matrix;
Figure 444219DEST_PATH_IMAGE010
is a feature vector; t is the transposed symbol.
4. The satellite fault identification method according to claim 3, wherein step S3 comprises:
A1. acquiring a singular value space matrix according to the orthogonal matrix;
A2. and calculating the singular value space vector according to the singular value space matrix and the residual vector of the pseudo-range observation value.
5. The satellite fault identification method according to claim 4, wherein in step A2, the singular value space vector is calculated according to the following formula:
Figure 242411DEST_PATH_IMAGE012
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE013
is the singular value space vector;
Figure 254361DEST_PATH_IMAGE014
is a singular value space matrix;
Figure 454398DEST_PATH_IMAGE003
a residual vector that is the pseudorange observation.
6. The satellite fault identification method according to claim 3, wherein step S3 comprises:
B1. acquiring a singular value space matrix according to the orthogonal matrix;
B2. and calculating the singular value space vector according to the singular value space matrix and the observed pseudo range noise vector.
7. The satellite fault identification method according to any one of claims 4 to 6, wherein in step S4, the test statistic is calculated as follows:
Figure 978920DEST_PATH_IMAGE016
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE017
is the test statistic;
Figure 537510DEST_PATH_IMAGE013
is the singular value space vector;
Figure 805680DEST_PATH_IMAGE018
the number of rows of the orthogonal matrix; t is a transposed symbol;
Figure DEST_PATH_IMAGE019
is a weighting coefficient matrix.
8. A satellite fault identification device, comprising the following modules:
a first obtaining module: the method comprises the steps of obtaining an observation coefficient matrix;
a second obtaining module: the system comprises an observation coefficient matrix, a singular value decomposition module and a data processing module, wherein the observation coefficient matrix is used for carrying out singular value decomposition on the observation coefficient matrix to obtain an orthogonal matrix;
a third obtaining module: the system is used for acquiring a singular value space vector according to the orthogonal matrix;
constructing a module: for calculating test statistics from the singular value space vectors;
an identification module: and the satellite fault identification module is used for carrying out fault identification on the satellite according to the test statistic so as to identify the fault satellite.
9. An electronic device comprising a processor and a memory, the memory storing computer readable instructions which, when executed by the processor, perform the steps of the method of satellite fault identification according to any one of claims 1-7.
10. A storage medium having stored thereon a computer program, wherein the computer program, when being executed by a processor, performs the steps of the method for identifying a satellite fault as claimed in any one of claims 1 to 7.
CN202211227991.8A 2022-10-09 2022-10-09 Satellite fault identification method and device, electronic equipment and storage medium Pending CN115629398A (en)

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