CN115494527B - Satellite system fault removal method based on correlation coefficient - Google Patents
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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
- G01S19/23—Testing, monitoring, correcting or calibrating of receiver elements
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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
- G01S19/35—Constructional details or hardware or software details of the signal processing chain
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Abstract
The application discloses a satellite system fault elimination method based on a correlation coefficient, which comprises the steps of obtaining a current GDOP value through calculating an omega vector and an S matrix, judging whether the GDOP value is within a threshold value, updating the omega vector by adopting different updating rules, further calculating the correlation coefficient of the omega vector and the S matrix, eliminating the satellite with the largest correlation coefficient according to the correlation coefficient, judging whether the fault is eliminated, and if not, carrying out parameter calculation and judgment again on the rest satellites according to the steps. According to the fault elimination method, the satellite with the fault can be eliminated through one-time calculation of the correlation coefficient, and compared with the traditional least square method, the least square calculation is carried out for how many times by how many satellites are involved in positioning. Under the condition of the same execution effect, the operation time is greatly saved, and the method has a certain positive effect on saving the resources of the navigation positioning chip.
Description
Technical Field
The application relates to the field of Beidou satellite navigation, in particular to a satellite system fault removal method based on a correlation coefficient.
Background
The Beidou satellite navigation system (hereinafter referred to as Beidou system) is a global satellite navigation system which is autonomously built and operated in China and is a national important space-time infrastructure for providing all-weather, all-day-time and high-precision positioning, navigation and time service for global users.
The Beidou system provides services, has been widely applied in the fields of transportation, agriculture, forestry, fishery, hydrologic monitoring, weather forecast, communication time service, power dispatching, disaster relief, disaster reduction, public safety and the like, serves important national infrastructure, and generates remarkable economic benefit and social benefit. Navigation service based on Beidou system is adopted by manufacturers such as electronic commerce, mobile intelligent terminal manufacturing and location service, and widely enters the fields of Chinese mass consumption, shared economy and folk life, and new modes, new business states and new economy of application are continuously emerging, so that the production and life modes of people are deeply changed. China will continue to advance Beidou application and industrialization development, serve modern construction of China and daily life of common people, and contribute to global science and technology, economy and social development.
Just as the Beidou system plays a great role in various fields, the requirements on the completeness of the Beidou system are more strict. The system perfection detection means that when the Beidou receiver is in the task execution process, various reasons such as a faulty star or long-time false lock and the like may cause problems on the system reliability, for example, the system cannot be used for navigation service or the navigation precision exceeds a given range, at this time, the navigation system should have the capability of timely finding faults and notifying users so that the Beidou user can eliminate the influence of fault sources and ensure the normal running of Beidou user navigation.
In the prior art, a fault elimination method, namely a least square method, selects a fault satellite through a traversing method, and comprises the following steps: one satellite involved in the positioning is selected. The least squares calculation is re-performed for other satellites that do not include the selected one. And (3) carrying out fault detection on the result of the least square calculation again through the above formula, if the system is detected to be faulty, traversing the next satellite, and when the system is not faulty due to the fact that the next satellite is traversed, judging that the satellite with the fault in the system is the currently traversed satellite.
The method in the prior art has the defects that the calculation is complex, when the number of faulty satellites is large, the time for fault elimination is long, and other errors are easy to generate in the navigation positioning software.
Disclosure of Invention
(one) solving the technical problems
In order to solve the technical problems and achieve the aim of the application, the application is realized by the following technical scheme: and obtaining the current GDOP value by calculating the omega vector and the S matrix, judging whether the GDOP value is in the threshold value, updating the omega vector by adopting different updating rules, further calculating the correlation coefficient of the omega vector and the S matrix, eliminating the satellite with the largest correlation coefficient according to the correlation coefficient, judging whether the fault is eliminated, and if not, carrying out parameter calculation and judgment on the rest satellites again according to the steps.
(II) technical scheme
S1: calculating omega vector and S matrix
Assuming that N satellites are visible at a certain moment, considering the weight matrix, the linearized GNSS observation equation is:
y＝Hx+εW
wherein y is the difference between the observed pseudo-range and the calculated pseudo-range, H is the observation matrix, x is the parameter to be solved, W is the weight matrix of the observed pseudo-range, and the general weight matrix W is selected as follows:
wherein ,for each observed pseudorange noise vector epsilon variance.
The least square solution of the user state can be obtained by the least square principle as follows:
from the following componentsThe pseudo-range residual vector ω is obtained as:
wherein s=i-H (H T WH) -1 H T W
S2: calculating the current GDOP value and judging whether the current GDOP value is larger than a threshold value or not, specifically comprising the following steps:
by linearizing the nonlinear positioning observation equation, a geometric effect matrix can be defined by using a directional cosine matrix G of "user-visible star":
Q＝(G T G) -1
taking four satellites in view as an example, wherein:
wherein ,(x0 ,y 0 ,z 0 )(t)，(x i ,y i ,z i ) (t) the user position at time t and the position of the ith visible satellite, ρ i0 (t) is the distance between the ith visible satellite at time t and the user, and thus the matrix Q is related to the geometry between the visible satellites.
And judging whether the GDOP value is larger than a threshold value R, and updating the omega vector according to a judging result.
Further, R is 3.5.
S3: updating omega vector
Updating omega vector according to GDOP value, concretely:
where ω' is the updated ω vector.
S4: calculating the correlation coefficient of omega vector and S matrix
Decomposing the S matrix into n vectors S 1 ,，S 2 …S n Each decomposition vector S i And calculating the correlation coefficient between the two vectors respectively with the omega vector, wherein the calculation formula is as follows:
where i=1, 2 … n.
Correlation coefficient r i Corresponding to the parameters corresponding to the ith satellite.
S5: searching the maximum value of the correlation coefficient in the calculated correlation coefficient vector
Based on n correlation coefficients r calculated in step S4 1 ,r 2 …r n And obtaining the maximum value of the correlation coefficient by selecting a sorting algorithm.
S6: the satellite with the largest correlation coefficient is excluded, and the S matrix and omega vector are recalculated.
S7: and (5) judging whether the current system has a fault again, if so, returning to the step (S1), if not, finishing judgment, and outputting a result.
(III) beneficial effects
According to the fault elimination method, the satellite with the fault can be eliminated through one-time calculation of the correlation coefficient, and compared with the traditional least square method, the least square calculation is carried out for how many times by how many satellites are involved in positioning. Under the condition of the same execution effect, the operation time is greatly saved, and the method has a certain positive effect on saving the resources of the navigation positioning chip.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute a limitation on the application. In the drawings:
FIG. 1 is a flow chart of a correlation coefficient based system troubleshooting method in accordance with an embodiment of the present application;
FIG. 2 is a positioning result of a prior art satellite system troubleshooting method;
FIG. 3 is a prior art method of troubleshooting a satellite system with time consuming fault detection and troubleshooting;
FIG. 4 is a localization result based on a correlation coefficient system troubleshooting method in accordance with an embodiment of the present application;
FIG. 5 illustrates the time-consuming detection and removal of faults based on the correlation coefficient system fault removal method according to an embodiment of the present application.
Detailed Description
Embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.
Other advantages and effects of the present disclosure will become readily apparent to those skilled in the art from the following disclosure, which describes embodiments of the present disclosure by way of specific examples. It will be apparent that the described embodiments are merely some, but not all embodiments of the present disclosure. The disclosure may be embodied or practiced in other different specific embodiments, and details within the subject specification may be modified or changed from various points of view and applications without departing from the spirit of the disclosure. It should be noted that the following embodiments and features in the embodiments may be combined with each other without conflict. All other embodiments, which can be made by one of ordinary skill in the art without inventive effort, based on the embodiments in this disclosure are intended to be within the scope of this disclosure.
It should also be noted that the illustrations provided in the following embodiments merely illustrate the basic concepts of the disclosure by way of illustration, and only the components related to the disclosure are shown in the drawings and are not drawn according to the number, shape and size of the components in actual implementation, and the form, number and proportion of the components in actual implementation may be arbitrarily changed, and the layout of the components may be more complicated.
Referring to fig. 1, the method comprises the steps of:
s1: calculating omega vector and S matrix
Assuming that N satellites are visible at a certain moment, considering the weight matrix, the linearized GNSS observation equation is:
y＝Hx+εW
wherein y is the difference between the observed pseudo-range and the calculated pseudo-range, H is the observation matrix, x is the parameter to be solved, W is the weight matrix of the observed pseudo-range, and the general weight matrix W is selected as follows:
wherein ,for each observed pseudorange noise vector epsilon variance.
The least square solution of the user state can be obtained by the least square principle as follows:
from the following componentsThe pseudo-range residual vector ω is obtained as:
wherein s=i-H (H T WH) -1 H T W
S2: calculating the current GDOP value and judging whether the current GDOP value is larger than a threshold value or not, specifically comprising the following steps:
by linearizing the nonlinear positioning observation equation, a geometric effect matrix can be defined by using a directional cosine matrix G of "user-visible star":
Q＝(G T G) -1
taking four satellites in view as an example, wherein:
wherein ,(x0 ,y 0 ,z 0 )(t)，(x i ,y i ,z i ) (t) the user position at time t and the position of the ith visible satellite, ρ i0 (t) is the distance between the ith visible satellite at time t and the user, and thus the matrix Q is related to the geometry between the visible satellites.
And judging whether the GDOP value is larger than a threshold value R, and updating the omega vector according to a judging result.
Further, R is 3.5.
S3: updating omega vector
Updating omega vector according to GDOP value, concretely:
where ω' is the updated ω vector.
S4: calculating the correlation coefficient of omega vector and S matrix
Decomposing the S matrix into n vectors S 1 ,，S 2 …S n Each decomposition vector S i And calculating the correlation coefficient between the two vectors respectively with the omega vector, wherein the calculation formula is as follows:
where i=1, 2 … n.
Correlation coefficient r i Corresponding to the parameters corresponding to the ith satellite.
S5: searching the maximum value of the correlation coefficient in the calculated correlation coefficient vector
Based on n correlation coefficients r calculated in step S4 1 ,r 2 …r n And sorting the correlation coefficients by selecting a sorting algorithm to obtain the maximum value of the correlation coefficients.
S6: the satellite with the largest correlation coefficient is excluded, and the S matrix and omega vector are recalculated.
S7: and (5) judging whether the current system has a fault again, if so, returning to the step (S1), if not, finishing judgment, and outputting a result. The correlation coefficient method can eliminate the satellite with fault through one-time calculation of the correlation coefficient, and compared with the traditional least square method, the least square calculation is carried out for how many times by how many satellites are involved in positioning. The operation time is greatly saved under the condition of the same execution effect. Has certain effect on saving the resources of the navigation positioning chip.
The inventors respectively test the results of the prior art fault removal method and the inventive fault removal method and count the time consumption of fault removal, analyze and draw images by using matlab, and as shown in fig. 2-5, respectively, the positioning result of the prior art method, the time consumption of fault detection and removal of the prior art method, the positioning result of the inventive method and the time consumption of fault detection and removal of the inventive method, it can be seen that the inventive system fault removal method achieves the same effect as the prior art system fault removal method, but has obvious reduction in time consumption and superiority in time complexity.
The above examples are only illustrative of the preferred embodiments of the present application and are not intended to limit the scope of the present application, and various modifications and improvements made by those skilled in the art to the technical solution of the present application should fall within the scope of protection defined by the claims of the present application without departing from the spirit of the present application.
Claims (6)
1. A method for satellite system troubleshooting based on correlation coefficients, comprising the steps of:
s1: calculating omega vector and S matrix;
the ω vector calculation method in step S1 is as follows:
calculating a linearized GNSS observation equation:
y＝Hx+εW
wherein y is the difference between the observed pseudo-range and the calculated pseudo-range, H is the observation matrix, x is the parameter to be solved, W is the weight matrix of the observed pseudo-range, and W is selected as follows:
wherein ,for the variance of each observed pseudo-range noise vector epsilon, N is the number of visible satellites;
the least squares solution for the user state is:
from the following componentsThe pseudo-range residual vector ω is obtained as:
the S matrix calculation method is as follows:
S＝I-H(H T WH) -1 H T W；
s2: calculating a current GDOP value;
s3: updating the omega vector, in particular according to the value of the GDOP;
the step S3 includes: judging whether the GDOP value is larger than a threshold value R, and updating the omega vector according to a judging result;
wherein ω' is the updated ω vector;
s4: calculating the correlation coefficient of the omega vector and the S matrix;
s5: searching a value with the maximum correlation coefficient in the calculated correlation coefficient vector;
s6: excluding the satellite with the largest correlation coefficient, and recalculating an S matrix and omega vectors;
s7: and (5) judging whether the current system has a fault again, if so, returning to the step (S1), if not, finishing judgment, and outputting a result.
2. The method for satellite system fault removal based on correlation coefficients according to claim 1, wherein the method for calculating the GDOP value in step S2 is as follows:
the geometric effect matrix can be defined by the directional cosine matrix G of "user-visible star":
Q＝(G T G) -1
wherein ,(x0 ，y 0 ，z 0 )(t)，(x i ，y i ，z i ) (t) the user position at time t and the position of the ith visible satellite, ρ i0 (t) is the distance between the ith visible satellite at time t and the user;
3. the correlation coefficient based satellite system troubleshooting method of claim 2 wherein the threshold R is 3.5.
4. The method for satellite system fault removal based on correlation coefficient according to claim 1, wherein the step S4 comprises:
decomposing the S matrix into n vectors S 1 ，S 2 ...S n Each decomposition vector S i And calculating the correlation coefficient between the two vectors respectively with the omega vector, wherein the calculation formula is as follows:
wherein i=1, 2..n;
correlation coefficient r i Corresponding to the parameters corresponding to the ith satellite.
5. The method for satellite system fault removal based on correlation coefficient according to claim 1, wherein the step S5 comprises:
and ordering the correlation coefficients through an ordering algorithm, and searching for the value with the maximum correlation coefficient in the calculated correlation coefficient vector.
6. The method for satellite system troubleshooting based on correlation coefficients of claim 5, wherein said step ordering algorithm is specifically: a selection ordering algorithm is employed.
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