CN108562290A - Filtering method, device, computer equipment and the storage medium of navigation data - Google Patents
Filtering method, device, computer equipment and the storage medium of navigation data Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
- G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
- G01C21/16—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C25/00—Manufacturing, calibrating, cleaning, or repairing instruments or devices referred to in the other groups of this subclass
- G01C25/005—Manufacturing, calibrating, cleaning, or repairing instruments or devices referred to in the other groups of this subclass initial alignment, calibration or starting-up of inertial devices
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Abstract
The present invention relates to the filtering method of navigation data, device, computer equipment and storage medium, this method includes establishing and initializing inertia system parameter error model, and the time serial number designated value of next filtering cycle is arranged；Obtain state quantity prediction value, the quantity of state covariance predicted value of time serial number designated value；Obtain measuring value estimated value, measuring value covariance estimated value, quantity of state and the measuring value cross covariance of time serial number designated value；Calculate the coefficient of Gaussian kernel；Obtain the quantity of state posterior estimate of time serial number designated value；The quantity of state covariance of renewal time serial number designated value；Compensating parameter；Setting designated value is that designated value adds one, returns to state quantity prediction value, the state covariance predicted value of time serial number designated value for obtaining time serial number designated value.Method by implementing the embodiment of the present invention can realize the capture to non-Gaussian noise higher order term, improve the estimation accuracy rate of error, promote positioning accuracy.
Description
Technical field
The present invention relates to Integrated Navigation System for Vehicle, more specifically refer to the filtering method, device, calculating of navigation data
Machine equipment and storage medium.
Background technology
The theoretical foundation of inertia system is Newton mechanics law, and the operation of system only needs acceleration and angular speed information
Fusion, need not receive other auxiliary informations, will not generate export-oriented radiation, have the characteristics that shield and do not generate interference,
High-precision relative position information can be provided within a short period of time.It is based on MEMS (Micro-Electro- from the 21st century,
Mechanical System) inertial sensor of technology is successfully applied to consumption market, and MEMS is MEMS, is also cried
Microelectromechanical systems, micro-system, micromechanics etc. are done, refers to size at several millimeters or even smaller high-tech device, but used
Property sensor application needs to solve the problems, such as that error accumulates rapidly in calculating process in navigation system.
Under the background that the satellite navigation system of a variety of maturations coexists, by means of the secondary satellites location technology such as differential GPS, reason
In the case of thinking, automobile can obtain comprehensive, round-the-clock, degree of precision absolute location information, but city by satellite navigation system
City's environment Satellite signal is easily blocked by building, generates larger position error.The respective spy of inertia system and satellite system
Property enable inertia system/satellite system integrated navigation system to be that vehicle being accurately positioned in urban environment provides effective solution
Certainly scheme, two subsystems can pass through mutual redundancy and modified mode, the precision of common raised position estimation.Due to subsystem
There are the errors of itself, so exploitation integrated navigation system needs the data of two subsystems to carry out fusion and carried out to error
Compensation, the Kalman filtering algorithm and a variety of innovatory algorithms most generally used at present are all based on minimum mean square error criterion, this
Class method can realize the optimal filter performance to Gaussian noise, and can not capture the higher order term of non-Gaussian noise, furthermore it is also possible to
Data fusion is carried out using the filtering algorithm of non-Kalman filtering iteration frame and error compensates, such as particle filter and nerve
Network filtering algorithm is required for larger calculation amount, is not suitable for the higher integrated navigation system of quantity of state dimension.
Therefore, it is necessary to design a kind of new method, to realize effective capture to non-Gaussian noise higher order term, improves and miss
The estimation accuracy rate of difference promotes positioning accuracy.
Invention content
It is an object of the invention to overcome the deficiencies of existing technologies, the filtering method, device, computer of navigation data are provided
Equipment and storage medium.
To achieve the above object, in a first aspect, an embodiment of the present invention provides a kind of filtering method of navigation data, packet
It includes：
Establish inertia system parameter error model；
Inertia system parameter error model is initialized, the time serial number designated value of next filtering cycle is set；
Unscented transform is carried out to the quantity of state that time serial number designated value subtracts one, obtains the state of time serial number designated value
Measure predicted value, the quantity of state covariance predicted value of time serial number designated value；
The measuring value of time serial number designated value is carried out without mark according to the state quantity prediction value of time serial number designated value
Transformation obtains the estimation of the measuring value estimated value, the measuring value covariance of time serial number designated value of time serial number designated value
The cross covariance of the quantity of state and measuring value of value, time serial number designated value；
According to the quantity of state of the quantity of state covariance predicted value of time serial number designated value and time serial number designated value with
The pseudo- observing matrix of cross covariance construction of measuring value, linearisation rewriting is carried out using pseudo- observing matrix to non-linear measurement equation,
It obtains and rewrites formula；
According to the coefficient for rewriting formula calculating Gaussian kernel；
It is obtained using the coefficient of the quantity of state covariance predicted value of time serial number designated value, pseudo- observing matrix and Gaussian kernel
Take the quantity of state posterior estimate of time serial number designated value；
The quantity of state covariance of renewal time serial number designated value；
In feedback states amount posterior estimate to inertia system, the parameter of inertia system is compensated；
Setting designated value adds one equal to designated value, and returns to the quantity of state for subtracting one to time serial number designated value and carry out
Unscented transform, state quantity prediction value, the quantity of state covariance of time serial number designated value for obtaining time serial number designated value are pre-
The step of measured value.
Second aspect, the embodiment of the present invention additionally provide a kind of filter of navigation data comprising：
Model foundation unit, for establishing inertia system parameter error model；
The time serial number of next filtering cycle is arranged for initializing inertia system parameter error model in initialization unit
For designated value；
Predicted value acquiring unit, quantity of state for subtracting one to time serial number designated value carry out Unscented transform, when acquisition
Between serial number designated value state quantity prediction value, the quantity of state covariance predicted value of time serial number designated value；
Estimated value acquiring unit, the state quantity prediction value for subtracting one according to time serial number designated value is to time serial number
The measuring value of designated value carries out Unscented transform, and measuring value estimated value, the time serial number for obtaining time serial number designated value are specified
The cross covariance of the estimated value of the measuring value covariance of value, the quantity of state and measuring value of time serial number designated value；
Formula acquiring unit is rewritten, for the quantity of state covariance predicted value and time sequence according to time serial number designated value
Number for the quantity of state of designated value and the pseudo- observing matrix of cross covariance construction of measuring value, using pseudo- observing matrix to non-linear measurement
Equation carries out linearisation rewriting, obtains and rewrites formula；
Coefficient acquiring unit, for according to the coefficient for rewriting formula calculating Gaussian kernel；
Posterior estimate acquiring unit, for the quantity of state covariance predicted value using time serial number designated value, pseudo- sight
Survey the quantity of state posterior estimate of the coefficient acquisition time serial number designated value of matrix and Gaussian kernel；
Variance updating unit is used for the quantity of state covariance of renewal time serial number designated value；
Parameter compensating unit, in feedback states amount posterior estimate to inertia system, compensating the parameter of inertia system；
Setting unit adds one for designated value to be arranged equal to designated value.
The third aspect, the embodiment of the present invention additionally provide a kind of computer equipment comprising memory and processor, it is described
Computer program is stored on memory, the processor realizes the above method when executing the computer program.
Fourth aspect, the embodiment of the present invention additionally provide a kind of computer readable storage medium, the storage medium storage
It includes program instruction to have computer program, the computer program, and described program instruction can be realized when being executed by a processor
State method.
Compared with the prior art, the invention has the advantages that：The present invention by the quantity of state error of navigation system into
When row estimation, introduces maximal correlation entropy criterion and traditional Unscented kalman filtering algorithm is improved, can be captured using joint entropy
Non-Gaussian noise higher order term accurately estimates systematic error, and passes through feedback states amount posterior estimate, the side of being corrected
Formula compensates systematic error, realizes the high-precision output of navigational parameter, improves the estimation accuracy rate of error, promote positioning
Precision has efficient filtering performance.
The invention will be further described in the following with reference to the drawings and specific embodiments.
Description of the drawings
Fig. 1 is the schematic flow diagram of the filtering method for the navigation data that the specific embodiment of the invention provides；
Fig. 2 is the sub-step schematic flow diagram of the filtering method for the navigation data that the specific embodiment of the invention provides；
Fig. 3 is the sub-step schematic flow diagram of the filtering method for the navigation data that the specific embodiment of the invention provides；
Fig. 4 is the sub-step schematic flow diagram of the filtering method for the navigation data that the specific embodiment of the invention provides；
Fig. 5 is the schematic diagram for the position estimation error comparison that the specific embodiment of the invention provides；
Fig. 6 is the schematic block diagram of the filter for the navigation data that the specific embodiment of the invention provides；
Fig. 7 is the schematic block diagram for the model foundation unit that the specific embodiment of the invention provides；
Fig. 8 is the schematic block diagram for the initialization unit that the specific embodiment of the invention provides；
Fig. 9 is the schematic block diagram for the posterior estimate acquiring unit that the specific embodiment of the invention provides；
Figure 10 is a kind of schematic block diagram for computer equipment that the specific embodiment of the invention provides.
Specific implementation mode
In order to more fully understand the present invention technology contents, with reference to specific embodiment to technical scheme of the present invention into
One step introduction and explanation, but not limited to this.
It should be appreciated that ought use in this specification and in the appended claims, term " comprising " and "comprising" instruction
Described feature, entirety, step, operation, the presence of element and/or component, but one or more of the other feature, whole is not precluded
Body, step, operation, element, component and/or its presence or addition gathered.
It is also understood that the term used in this present specification is merely for the sake of the mesh for describing specific embodiment
And be not intended to limit the application.As present specification and it is used in the attached claims, unless on
Other situations are hereafter clearly indicated, otherwise " one " of singulative, "one" and "the" are intended to include plural form.
It will be further appreciated that the term "and/or" used in present specification and the appended claims is
Refer to any combinations and all possible combinations of one or more of associated item listed, and includes these combinations.
Referring to Fig. 1, Fig. 1 is the schematic flow diagram of the filtering method for the navigation data that the specific embodiment of the invention provides；
The filtering method is applied in server, exists in the form of filtering platform；The server carries out data interaction with navigation device,
So that the navigation data of navigation device can carry out error compensation, positioning accuracy is improved.
Fig. 1 is the schematic flow diagram of the filtering method for the navigation data that the specific embodiment of the invention provides, as shown in Figure 1,
This method includes S110~S200.
S110, inertia system parameter error model is established.
In embodiments of the present invention, inertia system parameter error model refers to by the error of the parameters in inertia system
The model to be formed is combined according to specific return.
As shown in Fig. 2, in one embodiment, above-mentioned S110 may include step S111~S113.
S111, the parameter error structural regime equation using inertia system；
S112, measurement equation is established using the difference of inertia system and the metrical information of satellite system；
S113, inertia system parameter error model is formed according to state equation and measurement equation.
Wherein, state equation refers to attitude error, velocity error, site error, the gyroscope constant value zero of inertia system
Partially and accelerometer constant value zero bias combine the equation to be formed, and measurement equation is the difference of the metrical information of inertia system and satellite system
The equation of linear relationship composition between value.It is non-linear not applicable in integrated navigation system to avoid Kalman filtering algorithm
Problem.
Quantity of state x in modelkIncluding the attitude error of inertia system, velocity error, site error, gyroscope constant value zero
It is tieed up partially for n with accelerometer constant value zero bias, quantity of state herein, system model includes state equation and measurement equation；
Inertia system parameter error model is expressed asWherein；System state amount xkIt is expressed as
xk=[φE φN φU δVE δVN δVU δL δλ δh bwx bwy bwz bfx bfy bfz]；φE、φN、φUExpression calculated
Angle calculation error in navigational coordinate system in journey；δVE、δVN、δVUIndicate that speed calculates mistake in navigational coordinate system in calculating process
Difference；δ L, δ λ, δ h indicate that the position in calculating process in geocentric inertial coordinate system calculates error；bwx、bwy、bwzIndicate carrier coordinate
Fixed drift of the gyroscope in three axial directions in system；bfx、bfy、bfzIndicate that accelerometer is in three axial directions in carrier coordinate system
Fixed drift；Fk-1And HkThe respectively measurement matrix of the state-transition matrix of 15*15 and 6*15, n=15；qk-1Indicate system noise
Sound, statistical property are Gaussian characteristics, covariance Qk；rkIt indicates measurement noise, is set as Gaussian mixed noise herein, count
Characteristic is non-Gaussian feature, and covariance is expressed as Rk；ykFor measuring value.
S120, initialization inertia system parameter error model, are arranged the time serial number designated value of next filtering cycle.
System is initialized, to complete the iteration of Kalman filtering.In the present embodiment, time serial number is indicated with k,
The time serial number k=1 of the next filtering cycle set at this time.
In one embodiment, as shown in figure 3, above-mentioned S120 may include there is S121~S122.
The covariance of S121, the quantity of state of time serial number zero and time serial number zero are initialized；
The time serial number one of S122, the next filtering cycle of setting.
System is initialized, the system mode value of time serial number k=0 is set, enables inertial navigation system acquisition used
Property data, satellite navigation system receives satellite-signal, and inertia system carries out inertial guidance data resolving, and enables the serial number k of time series
=1, the system mode value of zero moment is first obtained, as primary data, further according to primary data to inertia system parameter error mould
Type carries out parameter initialization.
The init state amount x of etching system when specifically, to k=00It is set as x0=0 and init state amount it is corresponding just
Beginning variance is set as P0=0；And it is arranged
To the estimation error of navigational parameter in integrated navigation system, accelerometer data and gyro data are being acquired
Afterwards, the navigation calculation of inertia system, including speed and location information have been carried out, navigation output data is inertial navigation resolved data
Correction value after carrying out error compensation, that is, after server carries out all calculating, result of calculation carries out navigation system
Error compensation amendment forms final result.
S130, Unscented transform is carried out to the quantity of state that time serial number designated value subtracts one, obtains time serial number designated value
State quantity prediction value, the quantity of state covariance predicted value of time serial number designated value.
State quantity prediction value refers at a time, the quantity of state obtained according to inertia system parameter error model；Shape
State amount covariance predicted value refers at a time, being worth carry out variance calculating, the numerical value of acquisition according to state quantity prediction.
In the present embodiment, specifically to the quantity of state x at k-1 momentk-1Using Unscented transform, quantity of state and its association are found out
The priori prediction value of varianceAnd Pk|k-1；For Unscented transform process, in the following ways：
Calculate 2n+1 sigma points χk-1(Kalman filtering sampled point), calculation formula is as follows：
λ is a compositely proportional factor, it is specified that λ=α in formula2(n+ε)-n；Wherein usually setting 0<α≤1, ε are a ratios
The example factor, is typically chosen in 3-n.
Nonlinear transformation is carried out to sigma points (Kalman filtering sampled point) using state transition matrix, formula is as follows：
Priori prediction value is calculated by the point after transformationAnd Pk|k-1, calculation formula is as follows：
QkIt is the covariance of inertia system noise,It is the corresponding weights of state mean value,It is the corresponding weights of association covariance matrix, calculation is as follows：
Wherein, i
=1 ..., 2n, β and xkPriori it is related, take β=2 under normal conditions.
Based on Unscented kalman filtering algorithm frame, Kalman filtering algorithm is avoided in inertia system and satellite system group
Non-linear not applicable problem in the navigation system of conjunction.
S140, the measuring value of time serial number designated value is carried out according to the state quantity prediction value of time serial number designated value
Unscented transform obtains the measuring value estimated value of time serial number designated value, the measuring value covariance of time serial number designated value
The cross covariance of the quantity of state and measuring value of estimated value, time serial number designated value.
Measuring value estimated value is substituted into inertia system parameter error model by state quantity prediction value, the measuring value of acquisition；
The estimated value of measuring value covariance refers to carrying out variance calculating, the variance yields of acquisition to measuring value estimated value；Quantity of state and measurement
The cross covariance of value refers to the number of acquisition after the corresponding measuring value of a certain quantity of state at a certain moment carries out cross covariance calculating
Value.
In the present embodiment, the estimated value of above-mentioned measuring value covariance is calculated using following formula：Wherein, RkIt is the covariance of measurement noise,It is that time serial number refers to
The estimated value of the measuring value of definite value；PY,k|k-1It is the estimated value of the measuring value covariance of time serial number designated value；It is association
The corresponding weights of covariance matrix.
It is observing matrix that Unscented transform in the step, which is using nonlinear transformation matrix, and step is replaced with observing matrix
State-transition matrix in S130 carries out the Unscented transform similar with S130 steps.
S150, the state according to the quantity of state covariance predicted value and time serial number designated value of time serial number designated value
The pseudo- observing matrix of cross covariance construction of amount and measuring value carries out linearisation to non-linear measurement equation using pseudo- observing matrix and changes
It writes, obtains and rewrite formula.
In the present embodiment, pseudo- observing matrix is constructed using following formula：
Wherein, Pk|k-1It is the quantity of state covariance predicted value of time serial number designated value, PXY,k|k-1It is the shape of time serial number designated value
The cross covariance of state amount and measuring value.
In the present embodiment, rewriting formula isWherein, It is the state quantity prediction value of time serial number designated value,
Pk|k-1It is the quantity of state covariance predicted value of time serial number designated value；It is pseudo- observing matrix,It is that time serial number is specified
The state quantity prediction value of value, PY,k|k-1It is the estimated value of the measuring value covariance of time serial number designated value.
S160, the coefficient that Gaussian kernel is calculated according to rewriting formula；
In the present embodiment, the coefficient of the Gaussian kernelIt is counting
When calculating the coefficient of the Gaussian kernel, the process of Kalman filter equation is solved referring initially to weighted least-squares method, row write cost letter
Number, the design of cost function need to embody joint entropy, embody joint entropy using the use of gaussian kernel function, cost function indicates
For：
For calculate cost function represented by maximum value, to cost function calculation aboutDifferential, and it is zero to enable it.
Quantity of state estimated value is calculated using maximal correlation entropy criterion, due to gaussian kernel function and its work of the wide parameter of core
With so that the iterative process of Unscented kalman filtering follows maximal correlation entropy criterion, to the statistics second order term of non-Gaussian noise and
Higher order item realizes effectively capture.
S170, the quantity of state covariance predicted value of time serial number designated value, pseudo- observing matrix and Gaussian kernel are utilized
Coefficient obtains the quantity of state posterior estimate of time serial number designated value.
Quantity of state posterior estimate refers to the optimum state amount estimated value at a certain moment.
In one embodiment, as shown in figure 4, above-mentioned step S170 may include there is S171~S172.
S171, the quantity of state covariance predicted value of time serial number designated value, pseudo- observing matrix and Gaussian kernel are utilized
Coefficient obtains Kalman filtering gain；Wherein, the Kalman filtering gain
S172, the quantity of state posterior estimate that time serial number designated value is obtained according to Kalman filtering gain.The state
Measuring posterior estimate isThe data are the optimum state amount estimated values at k moment.
The quantity of state covariance of S180, renewal time serial number designated value；Specifically, by the shape of time serial number designated value
State amount covariance is updated to
In S190, feedback states amount posterior estimate to inertia system, the parameter of inertia system is compensated.
Specifically, the quantity of state posterior estimate at corresponding moment is subtracted using the data that inertia system is resolved, and is formed and is mended
Data are repaid, and using offset data as the resolving of subsequent time inertia system basis.
S200, setting designated value add one, and return to step S120 equal to designated value.
The iteration that Kalman filtering is constantly completed with this is formed and is constantly updated to navigation error estimation, ensure that group
Close the real-time and validity of navigation system filtering.
When entire navigation system powers off, which terminates, that is, does not enter back into cycle.
It gives one example, SINS/GNSS (inertia system/satellite system) integrated navigation system in a manner of pine combination is
Example, navigation initial point are Xi'an point, and longitude and latitude is separately positioned on 34.14 ° of north latitude, and 108.54 ° of east longitude, gyroscope three is axially
Constant value deviation be both configured to 0.03deg/h, random drift is set as 0.001deg/sqrt (h), and accelerometer constant value deviation is set
It is set to 0.0001g, random deviation is set as 0.0005g/sqrt (Hz).Satellite system uses GPS simulation systems, site error side
Difference is 5m；The frequency acquisition of inertia system sensor is 100Hz, and GPS renewal frequencies are 1Hz.Carry out two groups of experiments, one of which
It is to utilize filtering method provided in this embodiment in three directions (east, north, day) position estimation error amount, another group is traditional base
In least mean-square error Unscented kalman filtering algorithm (Unscented Kalman Filter, UKF) to the three of this example data
Position estimation error amount on a direction.Experimental result is as shown in figure 5, in terms of total result, and this method is in position estimation accuracy
It is upper to be higher than traditional Unscented kalman filtering algorithm.
The filtering method of above-mentioned navigation data, by when the quantity of state error to navigation system is estimated, introducing
Maximal correlation entropy criterion is improved traditional Unscented kalman filtering algorithm, and non-Gaussian noise high-order can be captured using joint entropy
, systematic error is accurately estimated, and by feedback states amount posterior estimate, mode is corrected, to systematic error
It compensates, realizes the high-precision output of navigational parameter, improve the estimation accuracy rate of error, promote positioning accuracy, have efficiently
Filtering performance.
Referring to Fig. 6, Fig. 6 is the schematic frame of the filter 300 for the navigation data that the specific embodiment of the invention provides
Figure；As shown in fig. 6, the device includes：
Model foundation unit 310, for establishing inertia system parameter error model.
The time sequence of next filtering cycle is arranged for initializing inertia system parameter error model in initialization unit 320
Number be designated value.
Predicted value acquiring unit 330, the quantity of state for subtracting one to time serial number designated value carry out Unscented transform, obtain
The state quantity prediction value of time serial number designated value, the quantity of state covariance predicted value of time serial number designated value.
Estimated value acquiring unit 340 is used for the state quantity prediction value according to time serial number designated value to time serial number
The measuring value of designated value carries out Unscented transform, and measuring value estimated value, the time serial number for obtaining time serial number designated value are specified
The cross covariance of the estimated value of the measuring value covariance of value, the quantity of state and measuring value of time serial number designated value.
Rewrite formula acquiring unit 350, for according to the quantity of state covariance predicted value of time serial number designated value and when
Between the quantity of state of serial number designated value and the pseudo- observing matrix of cross covariance construction of measuring value, using pseudo- observing matrix to non-linear
Measurement equation carries out linearisation rewriting, obtains and rewrites formula.
Coefficient acquiring unit 360, for according to the coefficient for rewriting formula calculating Gaussian kernel.
Posterior estimate acquiring unit 370, for quantity of state covariance predicted value, the puppet using time serial number designated value
Observing matrix and the coefficient of Gaussian kernel obtain the quantity of state posterior estimate of time serial number designated value.
Variance updating unit 380 is used for the quantity of state covariance of renewal time serial number designated value.
Parameter compensating unit 390, in feedback states amount posterior estimate to inertia system, compensating the ginseng of inertia system
Number.
Setting unit 391 adds one for designated value to be arranged equal to designated value.
In one embodiment, as shown in fig. 7, above-mentioned model foundation unit 310 includes：
State equation builds module 311, for the parameter error structural regime equation using inertia system.
Measurement equation builds module 312, for the difference foundation amount using inertia system and the metrical information of satellite system
Survey equation.
Composite module 313, for forming inertia system parameter error model according to state equation and measurement equation.
In one embodiment, as shown in figure 8, above-mentioned initialization unit 320 includes：
Data initialization module 321 is carried out for the quantity of state of time serial number zero and the covariance of time serial number zero
Initialization；
Serial number setup module 322, the time serial number one for next filtering cycle to be arranged.
In one embodiment, as shown in figure 9, above-mentioned posterior estimate acquiring unit 370 includes：
Gain acquisition module 371, for the quantity of state covariance predicted value using time serial number designated value, pseudo- observation square
The coefficient of battle array and Gaussian kernel obtains Kalman filtering gain；
Numerical value acquisition module 372, after the quantity of state according to Kalman filtering gain acquisition time serial number designated value
Test estimated value.
It should be noted that it is apparent to those skilled in the art that, the filtering dress of above-mentioned navigation data
The specific implementation process of 300 and each unit is set, the corresponding description in preceding method embodiment can be referred to, for convenience of description
With it is succinct, details are not described herein.
The filter 300 of above-mentioned navigation data can be implemented as a kind of form of computer program, the computer program
It can be run on computer equipment as shown in Figure 10.
Referring to Fig. 10, Figure 10 is a kind of schematic block diagram of computer equipment provided by the embodiments of the present application.The calculating
Machine equipment 500 is server, specifically, this refering to fig. 10, which includes being connected by system bus 501
Processor 502, memory and network interface 505, wherein memory may include non-volatile memory medium 503 and interior storage
Device 504.
The non-volatile memory medium 503 can storage program area 5031 and computer program 5032.The computer program
5032 include program instruction, which is performed, and processor 502 may make to execute a kind of filtering side of navigation data
Method.
The processor 502 is for providing calculating and control ability, to support the operation of entire computer equipment 500.
The built-in storage 504 provides environment for the operation of the computer program 5032 in non-volatile memory medium 503, should
When computer program 5032 is executed by processor 502, processor 502 may make to execute a kind of filtering method of navigation data.
The network interface 505 is used to carry out network communication with miscellaneous equipment.It will be understood by those skilled in the art that in Fig. 8
The structure shown is not constituted and is applied to application scheme only with the block diagram of the relevant part-structure of application scheme
The restriction of computer equipment 500 thereon, specific computer equipment 500 may include more more or fewer than as shown in the figure
Component either combines certain components or is arranged with different components.
Wherein, the processor 502 is for running computer program 5032 stored in memory, to realize following step
Suddenly：
Establish inertia system parameter error model；
Inertia system parameter error model is initialized, the time serial number designated value of next filtering cycle is set；
Unscented transform is carried out to the quantity of state that time serial number designated value subtracts one, obtains the state of time serial number designated value
Measure predicted value, the quantity of state covariance predicted value of time serial number designated value；
The measuring value of time serial number designated value is carried out without mark according to the state quantity prediction value of time serial number designated value
Transformation obtains the estimation of the measuring value estimated value, the measuring value covariance of time serial number designated value of time serial number designated value
The cross covariance of the quantity of state and measuring value of value, time serial number designated value；
The shape of quantity of state covariance predicted value and time serial number designated value at the time of according to time serial number designated value
The pseudo- observing matrix of the cross covariance of state amount and measuring value construction, linearizes non-linear measurement equation using pseudo- observing matrix
It rewrites, obtains and rewrite formula；
According to the coefficient for rewriting formula calculating Gaussian kernel；
It is obtained using the coefficient of the quantity of state covariance predicted value of time serial number designated value, pseudo- observing matrix and Gaussian kernel
Take the quantity of state posterior estimate of time serial number designated value；
The quantity of state covariance of renewal time serial number designated value；
In feedback states amount posterior estimate to inertia system, the parameter of inertia system is compensated；
Setting designated value adds one equal to designated value, and returns to the quantity of state for subtracting one to time serial number designated value and carry out
Unscented transform, state quantity prediction value, the quantity of state covariance of time serial number designated value for obtaining time serial number designated value are pre-
The step of measured value.
In one embodiment, processor 502 realize it is described establish inertia system parameter error model step when, it is specific real
Existing following steps：
Utilize the parameter error structural regime equation of inertia system；
Measurement equation is established using the difference of inertia system and the metrical information of satellite system；
Inertia system parameter error model is formed according to state equation and measurement equation.
In one embodiment, processor 502 is realizing the initialization inertia system parameter error model, and next filter is arranged
When the time serial number designated value step of wave period, it is implemented as follows step：
The quantity of state of time serial number zero and the covariance of time serial number zero are initialized；
The time serial number one of next filtering cycle is set.
The estimated value of the above-mentioned measuring value covariance is calculated using following formulaWherein, RkIt is the covariance of measurement noise,It is that time serial number refers to
The measuring value estimated value of definite value；PY,k|k-1It is the estimated value of the measuring value covariance of time serial number designated value；It is association association
The corresponding weights of variance matrix.
Above-mentioned pseudo- observing matrix is constructed using following formula：Wherein,
Pk|k-1It is the quantity of state covariance predicted value of time serial number designated value, PXY,k|k-1It is the quantity of state of time serial number designated value
With the cross covariance of measuring value.
Above-mentioned rewriting formula isWherein, It is the state quantity prediction value of time serial number designated value, Pk|k-1It is
The quantity of state covariance predicted value of time serial number designated value；It is pseudo- observing matrix,It is the shape of time serial number designated value
State amount predicted value, PY,k|k-1It is the estimated value of the measuring value covariance of time serial number designated value.
In one embodiment, processor 502 is realizing that the quantity of state covariance using time serial number designated value is pre-
When the coefficient of measured value, pseudo- observing matrix and Gaussian kernel obtains the quantity of state posterior estimate step of time serial number designated value,
It is implemented as follows step：
It is obtained using the coefficient of the quantity of state covariance predicted value of time serial number designated value, pseudo- observing matrix and Gaussian kernel
Card taking Kalman Filtering gain；
The quantity of state posterior estimate of time serial number designated value is obtained according to Kalman filtering gain.
It should be appreciated that in the embodiment of the present application, processor 502 can be central processing unit (Central
Processing Unit, CPU), which can also be other general processors, digital signal processor (Digital
Signal Processor, DSP), application-specific integrated circuit (Application Specific Integrated Circuit,
ASIC), ready-made programmable gate array (Field-Programmable Gate Array, FPGA) or other programmable logic
Device, discrete gate or transistor logic, discrete hardware components etc..Wherein, general processor can be microprocessor or
Person's processor can also be any conventional processor etc..
One of ordinary skill in the art will appreciate that be realize above-described embodiment method in all or part of flow,
It is that relevant hardware can be instructed to complete by computer program.The computer program includes program instruction, computer journey
Sequence can be stored in a storage medium, which is computer readable storage medium.The program instruction is by the department of computer science
At least one of system processor executes, to realize the process step of the embodiment of the above method.
Therefore, the present invention also provides a kind of storage mediums.The storage medium can be computer readable storage medium.This is deposited
Storage media is stored with computer program, and wherein computer program includes program instruction.The program instruction makes when being executed by processor
Processor executes following steps：
Establish inertia system parameter error model；
Inertia system parameter error model is initialized, the time serial number designated value of next filtering cycle is set；
Unscented transform is carried out to the quantity of state that time serial number designated value subtracts one, obtains the state of time serial number designated value
Measure predicted value, the quantity of state covariance predicted value of time serial number designated value；
The measuring value of time serial number designated value is carried out without mark according to the state quantity prediction value of time serial number designated value
Transformation obtains the estimation of the measuring value estimated value, the measuring value covariance of time serial number designated value of time serial number designated value
The cross covariance of the quantity of state and measuring value of value, time serial number designated value；
The shape of quantity of state covariance predicted value and time serial number designated value at the time of according to time serial number designated value
The pseudo- observing matrix of the cross covariance of state amount and measuring value construction, linearizes non-linear measurement equation using pseudo- observing matrix
It rewrites, obtains and rewrite formula；
According to the coefficient for rewriting formula calculating Gaussian kernel；
It is obtained using the coefficient of the quantity of state covariance predicted value of time serial number designated value, pseudo- observing matrix and Gaussian kernel
Take the quantity of state posterior estimate of time serial number designated value；
The quantity of state covariance of renewal time serial number designated value；
In feedback states amount posterior estimate to inertia system, the parameter of inertia system is compensated；
Setting designated value adds one equal to designated value, and returns to the quantity of state for subtracting one to time serial number designated value and carry out
Unscented transform, state quantity prediction value, the quantity of state covariance of time serial number designated value for obtaining time serial number designated value are pre-
The step of measured value.
In one embodiment, processor described in processor 502 is realized and described establishes inertia executing described program instruction
When systematic parameter error model step, it is implemented as follows step：
Utilize the parameter error structural regime equation of inertia system；
Measurement equation is established using the difference of inertia system and the metrical information of satellite system；
Inertia system parameter error model is formed according to state equation and measurement equation.
In one embodiment, processor described in processor 502 realizes that the initialization is used executing described program instruction
Sexual system parameter error model is implemented as follows step when the time serial number designated value step of next filtering cycle is arranged：
The quantity of state of time serial number zero and the covariance of time serial number zero are initialized；
The time serial number one of next filtering cycle is set.
The estimated value of above-mentioned measuring value covariance is calculated using following formula：Wherein, RkIt is the covariance of measurement noise,It is that time serial number refers to
The measuring value estimated value of definite value；PY,k|k-1It is the estimated value of the measuring value covariance of time serial number designated value；It is association association
The corresponding weights of variance matrix.
Above-mentioned pseudo- observing matrix is constructed using following formula：Wherein,
Pk|k-1It is the quantity of state covariance predicted value of time serial number designated value, PXY,k|k-1It is the quantity of state of time serial number designated value
With the cross covariance of measuring value.
Above-mentioned rewriting formula isWherein, It is the state quantity prediction value of time serial number designated value, Pk|k-1It is
The quantity of state covariance predicted value of time serial number designated value；It is pseudo- observing matrix,It is the shape of time serial number designated value
State amount predicted value, PY,k|k-1It is the estimated value of the measuring value covariance of time serial number designated value.
In one embodiment, processor described in processor 502 execute described program instruction and realize it is described utilize the time
It is specified that the coefficient of the quantity of state covariance predicted value of serial number designated value, pseudo- observing matrix and Gaussian kernel obtains time serial number
When the quantity of state posterior estimate step of value, it is implemented as follows step：
It is obtained using the coefficient of the quantity of state covariance predicted value of time serial number designated value, pseudo- observing matrix and Gaussian kernel
Card taking Kalman Filtering gain；
The quantity of state posterior estimate of time serial number designated value is obtained according to Kalman filtering gain.
The storage medium can be USB flash disk, mobile hard disk, read-only memory (Read-Only Memory, ROM), magnetic disc
Or the various computer readable storage mediums that can store program code such as CD.
Those of ordinary skill in the art may realize that lists described in conjunction with the examples disclosed in the embodiments of the present disclosure
Member and algorithm steps, can be realized with electronic hardware, computer software, or a combination of the two, in order to clearly demonstrate hardware
With the interchangeability of software, each exemplary composition and step are generally described according to function in the above description.This
A little functions are implemented in hardware or software actually, depend on the specific application and design constraint of technical solution.Specially
Industry technical staff can use different methods to achieve the described function each specific application, but this realization is not
It is considered as beyond the scope of this invention.
In several embodiments provided by the present invention, it should be understood that disclosed device and method can pass through it
Its mode is realized.For example, the apparatus embodiments described above are merely exemplary.For example, the division of each unit, only
Only a kind of division of logic function, formula that in actual implementation, there may be another division manner.Such as multiple units or component can be tied
Another system is closed or is desirably integrated into, or some features can be ignored or not executed.
The steps in the embodiment of the present invention can be sequentially adjusted, merged and deleted according to actual needs.This hair
Unit in bright embodiment device can be combined, divided and deleted according to actual needs.In addition, in each implementation of the present invention
Each functional unit in example can be integrated in a processing unit, can also be that each unit physically exists alone, can also
It is during two or more units are integrated in one unit.
If the integrated unit is realized in the form of SFU software functional unit and when sold or used as an independent product,
It can be stored in a storage medium.Based on this understanding, technical scheme of the present invention is substantially in other words to existing skill
The all or part of part or the technical solution that art contributes can be expressed in the form of software products, the meter
Calculation machine software product is stored in a storage medium, including some instructions are used so that a computer equipment (can be
People's computer, terminal or network equipment etc.) it performs all or part of the steps of the method described in the various embodiments of the present invention.
It is above-mentioned only with embodiment come the technology contents that further illustrate the present invention, in order to which reader is easier to understand, but not
It represents embodiments of the present invention and is only limitted to this, any technology done according to the present invention extends or recreation, by the present invention's
Protection.Protection scope of the present invention is subject to claims.
Claims (10)
1. the filtering method of navigation data, which is characterized in that including：
Establish inertia system parameter error model；
Inertia system parameter error model is initialized, the time serial number designated value of next filtering cycle is set；
Unscented transform is carried out to the quantity of state that time serial number designated value subtracts one, the quantity of state for obtaining time serial number designated value is pre-
The quantity of state covariance predicted value of measured value, time serial number designated value；
Unscented transform is carried out to the measuring value of time serial number designated value according to the state quantity prediction value of time serial number designated value,
Obtain the measuring value estimated value of time serial number designated value, the estimated value of the measuring value covariance of time serial number designated value, when
Between the quantity of state of serial number designated value and the cross covariance of measuring value；
According to the quantity of state and measurement of the quantity of state covariance predicted value of time serial number designated value and time serial number designated value
The pseudo- observing matrix of cross covariance construction of value, carries out linearisation rewriting to non-linear measurement equation using pseudo- observing matrix, obtains
Rewrite formula；
According to the coefficient for rewriting formula calculating Gaussian kernel；
When being obtained using the coefficient of the quantity of state covariance predicted value of time serial number designated value, pseudo- observing matrix and Gaussian kernel
Between serial number designated value quantity of state posterior estimate；
The quantity of state covariance of renewal time serial number designated value；
In feedback states amount posterior estimate to inertia system, the parameter of inertia system is compensated；
Setting designated value adds one equal to designated value, and returns to the quantity of state for subtracting one to time serial number designated value and carry out without mark
Transformation obtains state quantity prediction value, the quantity of state covariance predicted value of time serial number designated value of time serial number designated value
The step of.
2. the filtering method of navigation data according to claim 1, which is characterized in that described to establish inertia system parameter mistake
The step of differential mode type, including step in detail below：
Utilize the parameter error structural regime equation of inertia system；
Measurement equation is established using the difference of inertia system and the metrical information of satellite system；
Inertia system parameter error model is formed according to state equation and measurement equation.
3. the filtering method of navigation data according to claim 1, which is characterized in that the initialization inertia system parameter
The time serial number designated value step of next filtering cycle, including step in detail below is arranged in error model：
The quantity of state of time serial number zero and the covariance of time serial number zero are initialized；
The time serial number one of next filtering cycle is set.
4. the filtering method of navigation data according to claim 1, which is characterized in that the estimation of the measuring value covariance
Value is calculated using following formula：Wherein, RkIt is the covariance of measurement noise,It is the estimated value of the measuring value of time serial number designated value；PY,k|k-1It is the measuring value covariance of time serial number designated value
Estimated value；It is the corresponding weights of association covariance matrix.
5. the filtering method of navigation data according to claim 1, which is characterized in that the puppet observing matrix is using following
Formula construction：Wherein, Pk|k-1It is the quantity of state association side of time serial number designated value
Poor predicted value, PXY,k|k-1It is the cross covariance of the quantity of state and measuring value of time serial number designated value.
6. the filtering method of navigation data according to claim 4, which is characterized in that the rewriting formula isWherein, It is the state quantity prediction value of time serial number designated value, Pk|k-1Be time serial number designated value quantity of state covariance it is pre-
Measured value；It is pseudo- observing matrix,It is the state quantity prediction value of time serial number designated value, PY,k|k-1It is that time serial number is specified
The estimated value of the measuring value covariance of value.
7. the filtering method of navigation data according to claim 1, which is characterized in that utilize time serial number designated value
The coefficient of quantity of state covariance predicted value, pseudo- observing matrix and Gaussian kernel obtains the quantity of state posteriority of time serial number designated value
The step of estimated value, including step in detail below：
Card is obtained using the coefficient of the quantity of state covariance predicted value of time serial number designated value, pseudo- observing matrix and Gaussian kernel
Kalman Filtering gain；
The quantity of state posterior estimate of time serial number designated value is obtained according to Kalman filtering gain.
8. the filter of navigation data, which is characterized in that including：
Model foundation unit, for establishing inertia system parameter error model；
Initialization unit, for initializing inertia system parameter error model, the time serial number that next filtering cycle is arranged refers to
Definite value；
Predicted value acquiring unit, the quantity of state for subtracting one to time serial number designated value carry out Unscented transform, obtain time sequence
Number be designated value state quantity prediction value, the quantity of state covariance predicted value of time serial number designated value；
Estimated value acquiring unit is used for the state quantity prediction value according to time serial number designated value to time serial number designated value
Measuring value carries out Unscented transform, obtains the measurement of the measuring value estimated value, time serial number designated value of time serial number designated value
The cross covariance of the estimated value of value covariance, the quantity of state and measuring value of time serial number designated value；
Formula acquiring unit is rewritten, for the quantity of state covariance predicted value and time serial number according to time serial number designated value
The pseudo- observing matrix of cross covariance construction of the quantity of state and measuring value of designated value, using pseudo- observing matrix to non-linear measurement equation
Linearisation rewriting is carried out, obtains and rewrites formula；
Coefficient acquiring unit, for according to the coefficient for rewriting formula calculating Gaussian kernel；
Posterior estimate acquiring unit, for the quantity of state covariance predicted value using time serial number designated value, pseudo- observation square
The coefficient of battle array and Gaussian kernel obtains the quantity of state posterior estimate of time serial number designated value；
Variance updating unit is used for the quantity of state covariance of renewal time serial number designated value；
Parameter compensating unit, in feedback states amount posterior estimate to inertia system, compensating the parameter of inertia system；
Setting unit adds one for designated value to be arranged equal to designated value.
9. a kind of computer equipment, which is characterized in that the computer equipment includes memory and processor, on the memory
It is stored with computer program, the processor is realized when executing the computer program as described in any one of claim 1 to 7
Method.
10. a kind of storage medium, which is characterized in that the storage medium is stored with computer program, the computer program packet
Program instruction is included, described program instruction can realize the side as described in any one of claim 1 to 7 when being executed by a processor
Method.
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