CN112731494A - Method and device for realizing positioning calculation - Google Patents

Method and device for realizing positioning calculation Download PDF

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CN112731494A
CN112731494A CN202011533408.7A CN202011533408A CN112731494A CN 112731494 A CN112731494 A CN 112731494A CN 202011533408 A CN202011533408 A CN 202011533408A CN 112731494 A CN112731494 A CN 112731494A
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matrix
decomposition
equation
kalman filtering
decomposing
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宋挥师
赵海龙
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Datang Semiconductor Design Co Ltd
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Datang Semiconductor Design 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
    • G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
    • G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/42—Determining position
    • 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
    • G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
    • G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/393—Trajectory determination or predictive tracking, e.g. Kalman filtering

Abstract

A method and a device for realizing positioning calculation comprise: decomposing the observed quantity measurement residual variance matrix to obtain a matrix equation with the size smaller than the observed quantity measurement residual variance matrix; calculating a Kalman filtering gain matrix according to a matrix equation obtained by decomposition; and calculating a Kalman filtering state estimation value according to the Kalman filtering gain matrix obtained by calculation. The embodiment of the invention reduces the iterative operation times and complexity of positioning calculation and improves the working efficiency of the navigation receiving chip.

Description

Method and device for realizing positioning calculation
Technical Field
The present disclosure relates to, but not limited to, satellite navigation technologies, and more particularly, to a method and an apparatus for implementing positioning solution.
Background
A Global Navigation Satellite System (GNSS) plays an increasingly irreplaceable important role in daily life of people, and is increasingly applied to the fields of Navigation, exploration, monitoring, measurement, communication time service and the like. With the rapid development of civil applications in recent years, a global satellite navigation system is gradually deepened in daily life, and the satellite navigation technology cannot be separated from mobile phones, personal computers, automobiles, civil airplanes, missiles and fighters. All major countries in the world strive to develop satellite navigation technology, a plurality of satellite navigation systems exist at present, and the countries are independent to compete to develop the satellite navigation technology and mutually compatible systems, so that a prosperous GNSS system is formed. The global satellite navigation system mainly comprises a GPS system in the United states, a Beidou (BD) system in China, a global navigation satellite positioning system (GLONASS) in Russia and a Galileo (Galileo) system in Europe; wherein, in China and Asia-Pacific region, GPS and Beidou are widely applied; in Russia, GPS and GLONASS are used more frequently.
The most concerned of the user in the navigation positioning process is the positioning, constant speed and timing results; the method is particularly important for the user positioning, constant speed and timing technology. At present, the main positioning, constant speed and timing algorithms in the navigation system include least square method positioning and kalman filtering positioning. Positioning and solving the system state at each moment in an isolated manner by a least square method; the Kalman filtering positioning utilizes a state equation to link system states at different moments, and a tracking track is smoother. The Kalman filtering positioning comprises a prediction process and a correction process; in the correction process, the Kalman filtering positioning utilizes the system measurement value to correct the predicted value of the system state, and the correction equation is as follows:
Figure BDA0002852617080000011
wherein the content of the first and second substances,
Figure BDA0002852617080000012
a Kalman filtering system state estimation value is k epochs;
Figure BDA0002852617080000013
the estimated value is a priori estimated value of the Kalman filtering system state at k epochs; kkIs a Kalman filter gain matrix; y iskMeasured values of system observations at k epochs; c is a relation matrix of the system state quantity and the observed quantity;
to obtain
Figure BDA0002852617080000021
Optimum estimate of (system state vector)
Figure BDA0002852617080000022
The sum of the mean square errors of each state variable in the matrix P) is minimized, the hypothesis matrix P is assumedkIs a mean square error matrix, matrix P, of state estimateskThe elements on the diagonal of (a) are the mean square errors of the state variables respectively, in order to obtain a Kalman filter gain matrix KkSuch that the mean square error matrix PkIs minimum, calculated by equation (2):
Figure BDA0002852617080000023
to PkPerforming Kalman filtering gain matrix KkDerivation and equalisation to zero to find the matrix Kk(ii) a Here, the related art assumes that the state prior estimation error and the measurement noise of the current epoch are not correlated with each other; p is paired by formula (3) and formula (4)kAnd (3) carrying out derivation:
Figure BDA0002852617080000024
Figure BDA0002852617080000025
wherein F, G and H are arbitrary matrixes, F, G is a square matrix, H is a symmetric matrix, and symbol "tr" represents a trace-solving operator of the matrix (i.e. the sum of diagonal elements of the calculation matrix).
The derivation of equation (2) is zero-resolved:
Figure BDA0002852617080000026
wherein the content of the first and second substances,
Figure BDA0002852617080000027
measuring a residual variance matrix for the observation;
the Kalman filtering state estimation value can be obtained by taking the formula (5) into the formula (1)
Figure BDA0002852617080000028
As can be known from positioning calculation analysis, at least 4 visible satellites are needed to obtain the relevant data of the three-dimensional position, the speed, the time and the like of the user; in order to obtain better positioning performance, a receiver often needs to obtain and process dozens of satellites under the condition of dozens of or even multiple systems; in the calculation process, the matrix dimensions are large, and the complexity is high; meanwhile, the Kalman filtering gain matrix K is solved according to the formula (5)kIn the process, matrix inversion operation is required, the calculation amount of the positioning algorithm is further increased (namely, the calculation is more complex), the power consumption of the system is greatly increased, and meanwhile, higher requirements are provided for the running clock frequency of the system. As the recursion proceeds, an abnormal matrix is likely to occur in the process of inverting the matrix, resulting in filter divergence and failure to obtain a correlation estimation value. As can be seen from the correlation theory, the calculation amount of matrix inversion is approximately proportional to the third power of the matrix order, so the calculation amount increases rapidly as the matrix order, i.e., the number of satellites, increases. One common scheme for replacing the matrix inversion operation is: converting the parallel operation of the matrix into sequential operation, and replacing one matrix operation by multiple cycles; this approach can approximately replace the inverse operation of the matrix, but the clock frequency of the system is not mitigated, i.e. the power consumption will still be high. How to balance the iterative operation times, complexity, clock frequency and system power consumption when positioning and resolving, so that the chip area and power consumption of the satellite navigation receiving chip can be well represented is the problem existing at present.
Disclosure of Invention
The following is a summary of the subject matter described in detail herein. This summary is not intended to limit the scope of the claims.
The embodiment of the invention provides a method and a device for realizing positioning calculation, which can reduce the iterative operation times and complexity of positioning calculation and improve the working efficiency of a navigation receiving chip.
The embodiment of the invention provides a method for realizing positioning calculation, which comprises the following steps:
decomposing the observed quantity measurement residual variance matrix to obtain a matrix equation with the size smaller than the observed quantity measurement residual variance matrix;
calculating a Kalman filtering gain matrix according to a matrix equation obtained by decomposition;
and calculating a Kalman filtering state estimation value according to the Kalman filtering gain matrix obtained by calculation.
Optionally, decomposing the observation quantity measurement residual variance matrix includes:
decomposing the observation quantity measurement residual variance matrix into a product of a first matrix and a second matrix;
decomposing the first matrix and the second matrix obtained by the decomposition respectively;
obtaining the matrix equation according to the decomposition result of the first matrix and the second matrix;
wherein the second matrix is a transpose of the first matrix.
Optionally, the decomposing the decomposed first matrix and the decomposed second matrix respectively includes:
and respectively carrying out singular value decomposition on the first matrix and the second matrix.
Optionally, the obtaining the matrix equation according to the decomposition result of the first matrix and the second matrix includes:
and performing matrix multiplication on the decomposition result of the first matrix and the decomposition result of the second matrix to obtain the matrix equation.
Optionally, the calculating the kalman filter gain matrix according to the matrix equation obtained by decomposition includes:
performing sequential operation on matrices which are obtained by decomposition and are transposed and multiplied with each other in the matrix equation;
and calculating the Kalman filtering gain matrix according to the matrix equation after the sequential operation is finished.
On the other hand, an embodiment of the present invention further provides a device for implementing positioning calculation, including: the device comprises a decomposition unit, a first calculation unit and a second calculation unit; wherein the content of the first and second substances,
the decomposition unit is used for: decomposing the observed quantity measurement residual variance matrix to obtain a matrix equation with the size smaller than the observed quantity measurement residual variance matrix;
the first computing unit is configured to: calculating a Kalman filtering gain matrix according to a matrix equation obtained by decomposition;
the second calculation unit is configured to: and calculating a Kalman filtering state estimation value according to the Kalman filtering gain matrix obtained by calculation.
Optionally, the decomposition unit is specifically configured to:
decomposing the observation quantity measurement residual variance matrix into a product of a first matrix and a second matrix;
decomposing the first matrix and the second matrix obtained by the decomposition respectively;
obtaining the matrix equation according to the decomposition result of the first matrix and the second matrix;
wherein the second matrix is a transpose of the first matrix.
Optionally, the decomposing unit is configured to decompose the first matrix and the second matrix obtained by decomposition respectively, and includes:
and respectively carrying out singular value decomposition on the first matrix and the second matrix.
Optionally, the decomposing unit is configured to obtain the matrix equation according to a decomposition result of the first matrix and the second matrix, and includes:
and performing matrix multiplication on the decomposition result of the first matrix and the decomposition result of the second matrix to obtain the matrix equation.
Optionally, the first computing unit is specifically configured to:
sequentially operating the parts which are obtained by decomposition and are transposed and multiplied with each other in the matrix equation;
and calculating the Kalman filtering gain matrix according to the matrix equation after the sequential operation is finished.
In still another aspect, an embodiment of the present invention further provides a computer storage medium, where computer-executable instructions are stored in the computer storage medium, and the computer-executable instructions are used to execute the above positioning calculation method.
Compared with the related art, the technical scheme of the application comprises the following steps: decomposing the observed quantity measurement residual variance matrix to obtain a matrix equation with the size smaller than the observed quantity measurement residual variance matrix; calculating a Kalman filtering gain matrix according to a matrix equation obtained by decomposition; and calculating a Kalman filtering state estimation value according to the Kalman filtering gain matrix obtained by calculation. The embodiment of the invention reduces the iterative operation times and complexity of positioning calculation and improves the working efficiency of the navigation receiving chip.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the example serve to explain the principles of the invention and not to limit the invention.
FIG. 1 is a flow chart of a method for implementing positioning solution according to an embodiment of the present invention;
fig. 2 is a block diagram of a device for implementing positioning calculation according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be noted that the embodiments and features of the embodiments in the present application may be arbitrarily combined with each other without conflict.
The steps illustrated in the flow charts of the figures may be performed in a computer system such as a set of computer-executable instructions. Also, while a logical order is shown in the flow diagrams, in some cases, the steps shown or described may be performed in an order different than here.
Fig. 1 is a flowchart of a method for implementing positioning solution according to an embodiment of the present invention, as shown in fig. 1, including:
step 101, decomposing an observed quantity measurement residual variance matrix to obtain a matrix equation with the size smaller than the observed quantity measurement residual variance matrix;
optionally, decomposing the observation quantity measurement residual variance matrix according to the embodiment of the present invention includes:
decomposing the observation quantity measurement residual variance matrix into a product of a first matrix and a second matrix;
decomposing the first matrix and the second matrix obtained by the decomposition respectively;
obtaining the matrix equation according to the decomposition result of the first matrix and the second matrix;
wherein the second matrix is a transpose of the first matrix.
Optionally, decomposing the first matrix and the second matrix obtained by decomposition respectively in the embodiment of the present invention includes:
and respectively carrying out singular value decomposition on the first matrix and the second matrix.
Optionally, obtaining the matrix equation according to the decomposition result of the first matrix and the second matrix in the embodiment of the present invention includes:
and performing matrix multiplication on the decomposition result of the first matrix and the decomposition result of the second matrix to obtain the matrix equation.
102, calculating a Kalman filtering gain matrix according to a matrix equation obtained by decomposition;
optionally, the calculating the kalman filter gain matrix according to the matrix equation obtained by decomposition in the embodiment of the present invention includes:
performing sequential operation on matrices which are obtained by decomposition and are transposed and multiplied with each other in the matrix equation;
and calculating the Kalman filtering gain matrix according to the matrix equation after the sequential operation is finished.
And 103, calculating a Kalman filtering state estimation value according to the Kalman filtering gain matrix obtained by calculation.
Compared with the related art, the technical scheme of the application comprises the following steps: decomposing the observed quantity measurement residual variance matrix to obtain a matrix equation with the size smaller than the observed quantity measurement residual variance matrix; calculating a Kalman filtering gain matrix according to a matrix equation obtained by decomposition; and calculating a Kalman filtering state estimation value according to the Kalman filtering gain matrix obtained by calculation. The embodiment of the invention reduces the iterative operation times and complexity of positioning calculation and improves the working efficiency of the navigation receiving chip.
Fig. 2 is a block diagram of a device for implementing positioning calculation according to an embodiment of the present invention, as shown in fig. 2, including: the device comprises a decomposition unit, a first calculation unit and a second calculation unit; wherein the content of the first and second substances,
the decomposition unit is used for: decomposing the observed quantity measurement residual variance matrix to obtain a matrix equation with the size smaller than the observed quantity measurement residual variance matrix;
optionally, the decomposition unit in the embodiment of the present invention is specifically configured to:
decomposing the observation quantity measurement residual variance matrix into a product of a first matrix and a second matrix;
decomposing the first matrix and the second matrix obtained by the decomposition respectively;
obtaining the matrix equation according to the decomposition result of the first matrix and the second matrix;
wherein the second matrix is a transpose of the first matrix.
Optionally, the decomposing unit is configured to decompose the first matrix and the second matrix obtained by decomposition respectively, and includes:
and respectively carrying out singular value decomposition on the first matrix and the second matrix.
Optionally, the obtaining the matrix equation according to the decomposition result of the first matrix and the second matrix by the decomposition unit according to an embodiment of the present invention includes:
and performing matrix multiplication on the decomposition result of the first matrix and the decomposition result of the second matrix to obtain the matrix equation.
The first computing unit is configured to: calculating a Kalman filtering gain matrix according to a matrix equation obtained by decomposition;
optionally, the first calculating unit in the embodiment of the present invention is specifically configured to:
sequentially operating the parts which are obtained by decomposition and are transposed and multiplied with each other in the matrix equation;
and calculating the Kalman filtering gain matrix according to the matrix equation after the sequential operation is finished.
The second calculation unit is configured to: and calculating a Kalman filtering state estimation value according to the Kalman filtering gain matrix obtained by calculation.
Compared with the related art, the technical scheme of the application comprises the following steps: decomposing the observed quantity measurement residual variance matrix to obtain a matrix equation with the size smaller than the observed quantity measurement residual variance matrix; calculating a Kalman filtering gain matrix according to a matrix equation obtained by decomposition; and calculating a Kalman filtering state estimation value according to the Kalman filtering gain matrix obtained by calculation. The embodiment of the invention reduces the iterative operation times and complexity of positioning calculation and improves the working efficiency of the navigation receiving chip.
The embodiment of the invention also provides a computer storage medium, wherein the computer storage medium stores computer executable instructions, and the computer executable instructions are used for executing the positioning calculation method.
The method of the embodiment of the present invention is described in detail below by using application examples, which are only used for illustrating the present invention and are not used for limiting the protection scope of the present invention.
Application example
The present application example sets the state equation and the measurement equation of kalman filter with reference to the related art as equation (6) and equation (7), respectively:
xk＝Axk-1+Buk-1+wk-1 (6)
yk＝Cxk+vk (7)
suppose that,
Figure BDA0002852617080000081
Kalman filter pair System State x for k-1 epochk-1Is the optimum estimate of uk-1The input quantity of the epoch; k epoch State x from the State equation of Kalman FilteringkPredicted value of (2)
Figure BDA0002852617080000082
Here, the process noise is treated as zero to obtain equation (8):
Figure BDA0002852617080000083
due to the fact that
Figure BDA0002852617080000084
Has not undergone measurement value ykThe check of (1) is also called prior estimation value, and the symbol "-" at the upper right corner represents prior; a priori estimate error
Figure BDA0002852617080000085
Is the true value x of the statekWith its a priori estimate
Figure BDA0002852617080000086
The difference between them, the prior estimated value error
Figure BDA0002852617080000087
Comprises the following steps:
Figure BDA0002852617080000088
by definition, the covariance matrix of the a priori estimation error
Figure BDA0002852617080000089
Covariance matrix
Figure BDA00028526170800000810
The mean square error matrix, called the state, is expressed as:
Figure BDA00028526170800000811
obtaining a predicted value of an observed value from a measurement equation of Kalman filtering
Figure BDA00028526170800000812
ykFor observing the value of the actual value, the residual expression of the observed quantity is
Figure BDA00028526170800000813
The state estimation value of the Kalman filtering system is the state prediction value
Figure BDA00028526170800000814
And the measured value ykMaking a balance between them to achieve the optimum, the estimated value of the system state is:
Figure BDA00028526170800000815
wherein, KkIs the kalman filter gain. Due to this fact
Figure BDA00028526170800000816
The verification of the measured values has already been carried out,
Figure BDA00028526170800000817
also known as the a posteriori estimate of the state, the difference between this and the true value of the epoch state is called the a posteriori estimation error ekI.e. by
Figure BDA00028526170800000818
By definition, itMean square error matrix Pk：
Figure BDA0002852617080000091
The third part of analysis shows that the optimal estimation value of the system state is obtained, namely, the Kalman gain K is solvedkThe optimum of (c) minimizes the sum of diagonal elements of the mean square error matrix of the system estimate. From the formulae (6), (10), (11), (1), (12), (13)
Figure BDA0002852617080000092
Figure BDA0002852617080000093
To PkFor making a derivation
Figure BDA0002852617080000094
To obtain
Figure BDA0002852617080000095
Wherein, R is a covariance matrix of a measurement error vector, is a symmetrical square matrix with the same number of rows and the same dimension of a measurement value vector, and is known for a Kalman filtering system; this application example assumes that the observed quantity measures the residual variance matrix
Figure BDA0002852617080000096
Is Mk，MkIs a square matrix with dimension same as R, and the hypothesis M in the Kalman filtering systemkFor a symmetric positive definite matrix, the following is the process of decomposing the observation quantity measurement residual variance matrix, including:
will MkDecomposition into LLTWherein L is the number of rows and the momentMatrix MkThe number of rows is the same, and the number of columns is greater than or equal to the matrix MkA matrix of column numbers. Assume matrix MkHaving i positive singular values dj，j＝1,2,3…i。
Optionally, in this application example, the matrix L is decomposed to obtain an orthogonal matrix U ∈ Rn×n，V∈Rm×mWhere n is the number of rows of L, m is the number of columns of L, m ≧ n, and the diagonal matrix D ═ diag ([ D ]1,d2,…di]) Then satisfy
Figure BDA0002852617080000097
Bringing formula (16) into MkAfter decomposing the obtained matrix, one can obtain:
Figure BDA0002852617080000098
where U and V are orthogonal matrices, in which case U isT＝U-1，VT＝V-1；
Matrix M is solvedkThe inverse matrix of (d) is given by:
Figure BDA0002852617080000101
from the analysis of equation (18), the matrix M is determinedkInversion, only to the matrix DDTInversion is carried out; example of this application, due to the matrix DDTIs greater than M in sizekThe operation complexity is greatly reduced.
Bringing formula (18) into formula (1)
Figure BDA0002852617080000102
Optionally, the present application example may be applied to the matrix DDTThe inversion operation is carried out by adopting sequential operation, so that the iterative computation times of positioning calculation are reduced, and the effective reduction of the iterative computation times is realizedThe clock frequency is set. In the application example, the matrix operation involved in the scheme is a high-order nonlinear relation, so that the dimension of the matrix can be reduced, the complexity of positioning calculation and the reduction of the iterative calculation times can be realized, and the balance point of the chip area and the power consumption can be realized; meanwhile, the matrix divergence risk in the system operation process is also reduced.
It will be understood by those skilled in the art that all or part of the steps of the above methods may be implemented by a program instructing associated hardware (e.g., a processor) to perform the steps, and the program may be stored in a computer readable storage medium, such as a read only memory, a magnetic or optical disk, and the like. Alternatively, all or part of the steps of the above embodiments may be implemented using one or more integrated circuits. Accordingly, each module/unit in the above embodiments may be implemented in hardware, for example, by an integrated circuit to implement its corresponding function, or in software, for example, by a processor executing a program/instruction stored in a memory to implement its corresponding function. The present invention is not limited to any specific form of combination of hardware and software.
Although the embodiments of the present invention have been described above, the above description is only for the convenience of understanding the present invention, and is not intended to limit the present invention. It will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.

Claims (11)

1. A method of implementing positioning solutions, comprising:
decomposing the observed quantity measurement residual variance matrix to obtain a matrix equation with the size smaller than the observed quantity measurement residual variance matrix;
calculating a Kalman filtering gain matrix according to a matrix equation obtained by decomposition;
and calculating a Kalman filtering state estimation value according to the Kalman filtering gain matrix obtained by calculation.
2. The method of claim 1, wherein decomposing the observation quantity measurement residual variance matrix comprises:
decomposing the observation quantity measurement residual variance matrix into a product of a first matrix and a second matrix;
decomposing the first matrix and the second matrix obtained by the decomposition respectively;
obtaining the matrix equation according to the decomposition result of the first matrix and the second matrix;
wherein the second matrix is a transpose of the first matrix.
3. The method of claim 2, wherein the separately decomposing the decomposed first and second matrices comprises:
and respectively carrying out singular value decomposition on the first matrix and the second matrix.
4. The method of claim 2 or 3, wherein the obtaining the matrix equation from the decomposition of the first matrix and the second matrix comprises:
and performing matrix multiplication on the decomposition result of the first matrix and the decomposition result of the second matrix to obtain the matrix equation.
5. The method according to any one of claims 1 to 3, wherein the calculating of the Kalman filter gain matrix according to the matrix equation obtained by decomposition comprises:
performing sequential operation on matrices which are obtained by decomposition and are transposed and multiplied with each other in the matrix equation;
and calculating the Kalman filtering gain matrix according to the matrix equation after the sequential operation is finished.
6. An apparatus for implementing positioning solutions, comprising: the device comprises a decomposition unit, a first calculation unit and a second calculation unit; wherein the content of the first and second substances,
the decomposition unit is used for: decomposing the observed quantity measurement residual variance matrix to obtain a matrix equation with the size smaller than the observed quantity measurement residual variance matrix;
the first computing unit is configured to: calculating a Kalman filtering gain matrix according to a matrix equation obtained by decomposition;
the second calculation unit is configured to: and calculating a Kalman filtering state estimation value according to the Kalman filtering gain matrix obtained by calculation.
7. The apparatus according to claim 6, wherein the decomposition unit is specifically configured to:
decomposing the observation quantity measurement residual variance matrix into a product of a first matrix and a second matrix;
decomposing the first matrix and the second matrix obtained by the decomposition respectively;
obtaining the matrix equation according to the decomposition result of the first matrix and the second matrix;
wherein the second matrix is a transpose of the first matrix.
8. The apparatus of claim 7, wherein the decomposition unit is configured to decompose the decomposed first matrix and second matrix respectively comprises:
and respectively carrying out singular value decomposition on the first matrix and the second matrix.
9. The apparatus of claim 7 or 8, wherein the decomposition unit is configured to obtain the matrix equation according to a decomposition result of the first matrix and the second matrix, and comprises:
and performing matrix multiplication on the decomposition result of the first matrix and the decomposition result of the second matrix to obtain the matrix equation.
10. The apparatus according to any one of claims 6 to 8, wherein the first computing unit is specifically configured to:
sequentially operating the parts which are obtained by decomposition and are transposed and multiplied with each other in the matrix equation;
and calculating the Kalman filtering gain matrix according to the matrix equation after the sequential operation is finished.
11. A computer storage medium having computer-executable instructions stored therein for performing the method of any one of claims 1-5.
CN202011533408.7A 2020-12-23 2020-12-23 Method and device for realizing positioning calculation Pending CN112731494A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113466904A (en) * 2021-06-11 2021-10-01 西安交通大学 Dynamic interference source tracking method and system

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113466904A (en) * 2021-06-11 2021-10-01 西安交通大学 Dynamic interference source tracking method and system
CN113466904B (en) * 2021-06-11 2022-12-09 西安交通大学 Dynamic interference source tracking method and system

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