CN109375248A - A kind of Kalman's multimodality fusion location algorithm model and its method serially updated - Google Patents
A kind of Kalman's multimodality fusion location algorithm model and its method serially updated Download PDFInfo
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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
- G01S19/421—Determining position by combining or switching between position solutions or signals derived from different satellite radio beacon positioning systems; by combining or switching between position solutions or signals derived from different modes of operation in a single system
- G01S19/426—Determining position by combining or switching between position solutions or signals derived from different satellite radio beacon positioning systems; by combining or switching between position solutions or signals derived from different modes of operation in a single system by combining or switching between position solutions or signals derived from different modes of operation in a single system
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Abstract
A kind of method the present invention provides Kalman's multimodality fusion location algorithm model and its serially updated, comprising the following steps: step (1): the position at 6 moment of ephemeris computation satellite, speed, clock deviation；Step (2): design factor matrix A；Step (3): interpolation calculation t moment satellite PVT；Step (4): least square positioning calculation；Step (5): Kalman filtering resolves, this method calculates satellite spatial position coordinates boundary value by satellite ephemeris parameter, in sliding window time interval, satellite orbit estimation is carried out by fitting of a polynomial interpolation algorithm, reduce the computational complexity for solving satellite spatial position, and receiver positioning calculation is carried out by the way of least square and Kalman filtering combination, Kalman filtering is using the method serially updated, avoid big dimensional matrix inversion operation, reduce computational complexity, to can provide high-frequency positioning result, meet the application of high dynamic scene.
Description
Technical field
The present invention relates to the fields Global Satellite Navigation System (GNSS), and in particular to positions to a kind of Kalman's multimodality fusion
Algorithm model and its method serially updated.
Background technique
Global Satellite Navigation System (GNSS) mainly includes the BDS of China, the GPS in the U.S., Russia at present
GLONASS, the European big system of Galileo tetra-.GNSS is in the military and civilian field extremely important status of performers, in army
For thing on the way, the targeting rate of guided missile is can be improved in it；In civil field, GNSS can be the object of sea, land and sky every aspect
Positioned, navigated, including ship oceangoing voyage and diversion of approaching, Automobile automatic navigation and Waypoint guidance and drop of marching into the arena
It falls.In addition, the most crucial technology of various surface car tracking and municipal intelligent traffic management is exactly GNSS positioning；It can be with
For the time service of the network systems such as electric power, post and telecommunications and communication and calibrating frequency, including generate synchronization time, precision time service and calibrating frequency etc.；Greatly
Measurement, crustal movement monitoring, engineering survey, engineering project deformation monitoring and the various measurement tasks such as resource exploration and GNSS is close can not
Point.With the maturation that high precision technology is applied, the emerging fields such as intelligent robot, unmanned plane, automatic Pilot are to GNSS receiver
Demand it is more and more, the precision and stability of product is required also higher and higher.And in terms of industry development direction, GNSS is received
The development trend of machine product is multimode compatibility, high-performance, low-power consumption, miniaturization, so calculating in Project Realization data processing
More stringent requirements are proposed for method.
The basic principle that GNSS carries out receiver positioning is that satellite is calculated using satellite ephemeris parametric solution in space coordinates
In position solve following n member Nonlinear System of Equations then according to pseudorange and carrier-phase measurement information:
Wherein, (x, y, z) represents position of the receiver in WGS-84 coordinate system,Represent n satellite
Position in WGS-84 coordinate system, δ tuRepresent receiver clock-offsets,For the pseudo-range measurements after correction, updating formula are as follows:ρIt surveysFor pseudo-range measurements, δ tsFor satellite clock clock deviation correction term, δ ts=Δ
ts+Δtr-TGD, Δ ts=af0+af1(t-toc)+af2(t-toc)2, Δ trRepresent relativistic correction amount, TGDIt is delayed for group's wave,
Iono represents ionosphere delay, and Trono represents tropospheric delay.
By satellite positioning principle it is found that needing to calculate the position, speed and clock deviation of every satellite before resolving.Traditional approach
GPS, BDS, Galileo broadcast ephemeris parameter by satellite navigation message, wherein including 6 Keplerian orbit parameters, 9 perturbations
Corrected parameter, by a series of complex operation, including solve overdetermined equation, iteration convergence, antitrigonometric function etc., it calculates accurate
Satellite spatial position coordinates and speed coordinate, operand is big, and algorithm complexity is high, is not able to satisfy the need of high-frequency location navigation
It asks.
In receiver positioning calculation, according to resolved data origin classification, usually there are Snapshot algorithm and filtering algorithm.Snapshot is calculated
Method includes least square method, weighted least-squares method etc.；Filtering algorithm includes Kalman filtering, Strong tracking filter etc..Snapshot is calculated
Method only considered each positioning epoch time information, and there is no the reception machine informations for merging last time, receive under normal conditions
It is gradual change that seat in the plane, which is set, so frequently with filtering with smooth positioning result.Kalman filtering mathematical recurrence formula is to being
System state carries out optimal estimation, and the estimated value of system mode is made to have least mean-square error (MMSE).Existing receiver positioning
Algorithm model is to carry out positioning calculation frequently with Kalman filtering, conventional Kalman updates in a manner of a kind of parallel processing
Obtain kalman gain and status maintenance positive value.In this conventional parallel processing, inverse of a matrix operation is essential.When defending
Star number mesh is n, the matrix dimensionality n x n for needing to invert, current four big system can track number of satellite up to 40 simultaneously, institute
Can bring very heavy operand when realizing in embedded system, Project Realization is highly difficult.
Summary of the invention
In order to solve above-mentioned insufficient defect, the present invention provides a kind of Kalman's multimodality fusion location algorithm model and its
The method serially updated, this method calculates satellite spatial position coordinates boundary value by satellite ephemeris parameter, in the sliding window time
In section, satellite orbit estimation is carried out by fitting of a polynomial interpolation algorithm, reduces the operation complexity for solving satellite spatial position
Degree, and receiver positioning calculation is carried out by the way of least square and Kalman filtering combination, Kalman filtering is using serial
The method of update avoids big dimensional matrix inversion operation, reduces computational complexity, thus can provide high-frequency positioning result, it is full
The application of sufficient high dynamic scene；And according to GPS positioning Satellite position, speed and clock deviation successional feature at any time and
Research to polynomial interopolation algorithm proposes and calculates satellite position, speed with polynomial interopolation algorithm in embedded systems
Degree can simplify complicated mathematical operation.It, can be by multiple observed quantities aiming at the problem that Kalman filtering updates big matrix inversion
Regard what " successively sequence " arrived as, so as to handle with a kind of serial manner, to each observed quantity y, it is assumed that its noise side
Difference is R, can calculate the updated value of kalman gain K, P and x caused by this observed quantity, without carrying out big dimension
The operation of matrix inversion.
A kind of method the present invention provides Kalman's multimodality fusion location algorithm model and its serially updated, including it is following
Step:
Step (1): the position at 6 moment of ephemeris computation satellite, speed, clock deviation；
Step (2): design factor matrix A；
Step (3): interpolation calculation t moment satellite PVT；
Step (4): least square positioning calculation；
Step (5): Kalman filtering resolves.
Above-mentioned method, wherein the step (1) specifically includes: 6 moment are respectively (t1, t2, t3, t4, t5, t6),
Each time at intervals time T takes 60s namely time span is 60s.
Above-mentioned method, wherein the step (2) specifically includes: in (t1, t2, t3, t4, t5, t6) moment satellite position
It sets, clock deviation, speed are respectively as follows:
Pi=[pix piy piz δti vix viy viz]
Then equation can be listed
Wherein τi=i*T, then:
Above-mentioned method, wherein the step (3) specifically includes: in [t1, t6] any interested between the time
The satellite position speed clock deviation coordinate for carving t can be obtained by following formula:
[px py pz δt vx vx vx]=[1 dt dt2 dt3 dt4 dt5]A
Since error can become larger when interpolation point is located at section end, so as time t > t5, renewal time window.
Above-mentioned method, wherein the step (4) specifically includes:
Step (4.1): setting initial position and clock deviation, generally setting historical position, if without historical position, Ke Yichu
Beginning sets 0；
Step (4.2): pseudorange ρ is calculated using equation (2)i, calculate the poor δ ρ between pseudo-range measurements and calculated valuei
Step (4.3): by calculated value ρiIt substitutes into equation (3), finds out ai1, ai2, ai3；
Step (4.4): calculating matrix a finds out Δ x, Δ y, Δ z, Δ δ t by equation (5)u；
Step (4.5): determine whether that iteration terminates.By Δ x, Δ y, Δ z, Δ δ tuAbsolute value and equation (6) calculate
δ v, iteration terminates if being less than threshold value, continues iteration if more than threshold value；
Step (4.6): by Δ x, Δ y, Δ z, Δ δ tuWith initial x, y, z, δ tuIt is added, obtains new state, these values are made
For the initial value of next iteration；
Step (4.7): repeating step (4.1)-step (4.6), and until δ v is less than threshold value, general the number of iterations is no more than
10 times, if 10 times also not converged, resolve failure, it may be possible to which observed quantity exists abnormal.
Above-mentioned method, wherein the step (5) specifically includes:
Step (5.1): EKF model
State equation:
Wherein x=[x, y, z, bgps,df,Δbgb,Δbgr,Δbge]T,bgpsFor receiver local zone time and gps satellite system
It unites the time difference, Δ bgbIt is poor for Beidou and GPS system time, Δ bgrIt is that GLONASS and GPS system time are poor, Δ bgeFor Galileo
It is poor with GPS system time.
Observational equation:
Observed quantity is pseudorange and Doppler.
Step (5.2): state for time updates
Matrix φ is updated by the time differencek+1, by the state at 7 formulas prediction k+1 moment；
Step (5.3): calculating observation amount residual error
The state predicted according to previous stepThe observed quantity of prediction is calculated by (2) formula
And then it calculates actual observation amount and predicts the residual error of measurements between observed quantity:
Step (5.4): P and Q matrix is updated
The processing noise of each quantity of state is mutually indepedent, and is white noise, and respective noise power spectral density is respectively
diag{σp,σp,σp,σv,σv,σv,σb,σdf,σΔb,σΔb,σΔb, then
Then the update of state covariance matrix P is carried out:
Step (5.5): R matrix is updated
Calculating for R can be fitted according to the CN0 of every star and the elevation angle, and general R value and CN0 and the elevation angle are inversely；
Step (5.6): observed quantity serially updates
M observed quantity regarded to what " successively sequence " arrived as, to handle with a kind of serial manner, to each sight
Measure yi, it is assumed that its noise equation is Ri, the kalman gain k that this observed quantity obtains can be calculatedi, state error piWith
xK+1Updated value；Update specific calculating such as following formula；
Observational equation is linearized at the k+1 moment, calculates H times；
Every star serial computing formula is as follows:
Pi=[I-kihi]Pi-1 (14)
Step (5.7): system mode X is updated
By the updated error state δ x of observed quantityk+1|k+1The state for time obtained with the first step updates, and can carry out to X
Observed quantity updates:
After this step updates,So δ xk+1|k+1In include it is new breath be updated to shape
It is gone in state, it is therefore desirable to set δ xk+1|k+1=0；
Step (5.2)-step (5.4) is time update in above-mentioned steps, and e-g is observed quantity update, knot per treatment
Fruit Pk+1|k+1And Xk+1|k+1It is saved, the initial conditions as next iteration.
A kind of method the present invention provides Kalman's multimodality fusion location algorithm model and its serially updated has following
The utility model has the advantages that 1, this method carries out receiver positioning calculation, minimum two by the way of least square and Kalman filtering combination
The positioning result multiplied can help Kalman initial, fast convergence, and auxiliary Kalman's tune power improves reliability and the Shandong of system
Stick.2, multiple observed quantities can be regarded as " successively sequence " arrival using the method serially updated by Kalman filtering, thus
It can be handled with a kind of serial manner, to each observed quantity y, it is assumed that its noise variance is R, can calculate this observed quantity
The updated value of caused kalman gain K, P and x, the operation inverted without carrying out big dimensional matrix, it is multiple to reduce operation
Miscellaneous degree meets the application of high dynamic scene to can provide high-frequency positioning result.
Detailed description of the invention
Upon reading the detailed description of non-limiting embodiments with reference to the following drawings, the present invention and its feature, outer
Shape and advantage will become more apparent upon.Identical label indicates identical part in all the attached drawings.Not deliberately proportionally
Draw attached drawing, it is preferred that emphasis is show the gist of the present invention.
Fig. 1 is multimode rake receiver positioning calculation program flow diagram provided by the invention.
Fig. 2 a, Fig. 2 b, Fig. 2 c are respectively interpolation calculation satellite position, speed, clock deviation accuracy comparison figure.
Specific embodiment
In the following description, a large amount of concrete details are given so as to provide a more thorough understanding of the present invention.So
And it is obvious to the skilled person that the present invention may not need one or more of these details and be able to
Implement.In other examples, in order to avoid confusion with the present invention, for some technical characteristics well known in the art not into
Row description.
In order to thoroughly understand the present invention, detailed step and detailed structure will be proposed in following description, so as to
Illustrate technical solution of the present invention.Presently preferred embodiments of the present invention is described in detail as follows, however other than these detailed descriptions, this
Invention can also have other embodiments.
Referring to Fig.1 shown in-Fig. 2 c, a kind of Kalman's multimodality fusion location algorithm model provided by the invention and its it is serial more
New method, this method include following implementation steps:
The position at 6 moment of ephemeris computation satellite, speed, clock deviation；Specially 6 moment be respectively (t1, t2, t3, t4,
T5, t6), each time at intervals time T takes 60s namely time span is 60s.
Design factor matrix A
It is respectively as follows: in (t1, t2, t3, t4, t5, t6) moment satellite position, clock deviation, speed
Pi=[pix piy pizδti vix viy viz]
Then equation can be listed
Wherein τi=i*T.Then:
Interpolation calculation t moment satellite PVT
It can be obtained in the satellite position speed clock deviation coordinate of any interested moment t of [t1, t6] between the time by following formula:
[px py pz δt vx vx vx]=[1 dt dt2 dt3 dt4 dt5]A
Since error can become larger when interpolation point is located at section end, so as time t > t5, renewal time window.
Compare with the conventional method with ephemeris computation satellite position speed, operand can be greatly reduced.
Least square positioning calculation
Initial position and clock deviation are set, generally setting historical position, if can initially set 0 without historical position.
Pseudorange ρ is calculated using equation (2)i, calculate the poor δ ρ between pseudo-range measurements and calculated valuei
By calculated value ρiIt substitutes into equation (3), finds out ai1, ai2, ai3。
Calculating matrix a finds out Δ x, Δ y, Δ z, Δ δ t by equation (5)u。
Determine whether that iteration terminates.By Δ x, Δ y, Δ z, Δ δ tuAbsolute value and equation (6) calculate δ v, if being less than
Then iteration terminates threshold value, continues iteration if more than threshold value.
By Δ x, Δ y, Δ z, Δ δ tuWith initial x, y, z, δ tuIt is added, obtains new state, these values are as next iteration
Initial value.
It repeats the above steps, until δ v is less than threshold value.General the number of iterations is no more than 10 times, if 10 times are also not converged,
Then resolve failure, it may be possible to which observed quantity exists abnormal.
Kalman filtering resolves
EKF model
State equation:
Wherein x=[x, y, z, bgps,df,Δbgb,Δbgr,Δbge]T,bgpsFor receiver local zone time and gps satellite system
It unites the time difference, Δ bgbIt is poor for Beidou and GPS system time, Δ bgrIt is that GLONASS and GPS system time are poor, Δ bgeFor Galileo
Poor with GPS system time, df is the drift of receiver clock.
Observational equation:
Observed quantity is pseudorange and Doppler.
State for time updates
Matrix φ is updated by the time differencek+1, by the state at 7 formulas prediction k+1 moment.
Calculating observation amount residual error
The state predicted according to previous stepThe observed quantity of prediction is calculated by (2) formula
And then it calculates actual observation amount and predicts the residual error of measurements between observed quantity:
Update P and Q matrix
The processing noise of each quantity of state is mutually indepedent, and is white noise, and respective noise power spectral density is respectively
diag{σp,σp,σp,σv,σv,σv,σb,σdf,σΔb,σΔb,σΔb, then
Then the update of state covariance matrix P is carried out:
Update R matrix
Calculating for R can be fitted according to the CN0 of every star and the elevation angle, and general R value and CN0 and the elevation angle are inversely.
Observed quantity serially updates
M observed quantity regarded to what " successively sequence " arrived as, to handle with a kind of serial manner, to each sight
Measure yi, it is assumed that its noise equation is Ri, the kalman gain k that this observed quantity obtains can be calculatedi, state error piWith
xK+1Updated value.Update specific calculating such as following formula.
Observational equation is linearized at the k+1 moment, calculates H times.
Every star serial computing formula is as follows:
Pi=[I-kihi]Pi-1(14)
Update system mode X
By the updated error state δ x of observed quantityk+1|k+1The state for time obtained with the first step updates, and can carry out to X
Observed quantity updates:
After this step updates,So δ xk+1|k+1In include it is new breath be updated to
It is gone in state, it is therefore desirable to set δ xk+1|k+1=0.
B-d is time update in above-mentioned steps, and e-g is observed quantity update, result P per treatmentk+1|k+1And Xk+1|k+1Quilt
It preserves, the initial conditions as next iteration.
In the present invention, realize the calculating using interpolation method to satellite position, speed, clock deviation, and with least square and
The method of Extended Kalman filter combination carries out Combined Calculation to multimode data.The invention has according to navigation satellite running track
The characteristic of flatness carries out satellite orbit Interpolate estimation with fitting of a polynomial interpolation method, and in-orbit channel estimation error is 10-6Meter Qian
Put, and avoid iteration when calculating satellite orbital position in conventional method, antitrigonometric function reconciliation overdetermined equation as far as possible etc.
Operation.The method that positioning calculation is combined using least square and Kalman filtering, improves the robustness of system；Kalman filtering shape
In state update by the way of serially updating, big matrix inversion operation is avoided, greatly reduces computational complexity, meets and uses
Requirement of the family to receiver location accuracy, real-time and robustness.This method has been successfully applied to the multimode of arctic nebula
In multifrequency baseband board card product, positioning performance is excellent.
In the present invention, receiver signal processing part carries out satellite signal acquisition first, then track loop realization pair
Code phase and carrier phase tracking after synchronizing text Bit, realize frame synchronization, and extract ephemeris parameter information；Positioning calculation part
Determine ephemeris validity, and initial calculation satellite orbital position, speed and clock deviation component, chooses sliding window time interval boundary
Point carries out satellite position, speed, clock deviation interpolative prediction with polynomial interpolation, and it is fixed to carry out least square using pseudo range observed quantity
Position resolves, and carries out least square velocity calculated using Doppler measurements, and system mode vector is arranged and process noise, observation are made an uproar
Sound covariance matrix, using the smooth positioning calculation of Kalman filter as a result, final updating sliding window time interval, re-starts
Receiver quick location technique details.
Presently preferred embodiments of the present invention is described above.It is to be appreciated that the invention is not limited to above-mentioned
Particular implementation, devices and structures not described in detail herein should be understood as gives reality with the common mode in this field
It applies；Anyone skilled in the art, without departing from the scope of the technical proposal of the invention, all using the disclosure above
Methods and technical content many possible changes and modifications are made to technical solution of the present invention, or be revised as equivalent variations etc.
Embodiment is imitated, this is not affected the essence of the present invention.Therefore, anything that does not depart from the technical scheme of the invention, foundation
Technical spirit of the invention any simple modifications, equivalents, and modifications made to the above embodiment, still fall within the present invention
In the range of technical solution protection.
Claims (6)
1. a kind of Kalman's multimodality fusion location algorithm model and its method serially updated, which is characterized in that including following step
It is rapid:
Step (1): the position at 6 moment of ephemeris computation satellite, speed, clock deviation；
Step (2): design factor matrix A；
Step (3): interpolation calculation t moment satellite PVT；
Step (4): least square positioning calculation；
Step (5): Kalman filtering resolves；It specifically includes: step (5.1): EKF model
State equation:
Wherein x=[x, y, z, bgps,df,Δbgb,Δbgr,Δbge]T,bgpsWhen for receiver local zone time and gps satellite system
Between poor, Δ bgbIt is poor for Beidou and GPS system time, Δ bgrIt is that GLONASS and GPS system time are poor, Δ bgeFor Galileo and
GPS system time is poor；
Observational equation:
Observed quantity is pseudorange and Doppler；
Step (5.2): state for time updates
Matrix φ is updated by the time differencek+1, by the state at 7 formulas prediction k+1 moment；
Step (5.3): calculating observation amount residual error
The state predicted according to previous stepThe observed quantity of prediction is calculated by (2) formula
And then it calculates actual observation amount and predicts the residual error of measurements between observed quantity:
Step (5.4): P and Q matrix is updated
The processing noise of each quantity of state is mutually indepedent, and is white noise, and respective noise power spectral density is respectively diag { σp,
σp,σp,σv,σv,σv,σb,σdf,σΔb,σΔb,σΔb, then
Then the update of state covariance matrix P is carried out:
Step (5.5): R matrix is updated
Calculating for R can be fitted according to the CN0 of every star and the elevation angle, and general R value and CN0 and the elevation angle are inversely；
Step (5.6): observed quantity serially updates
M observed quantity regarded to what " successively sequence " arrived as, to handle with a kind of serial manner, to each observed quantity
yi, it is assumed that its noise equation is Ri, the kalman gain k that this observed quantity obtains can be calculatedi, state error piAnd xK+1's
Updated value；Update specific calculating such as following formula；
Observational equation is linearized at the k+1 moment, calculates H times；
Every star serial computing formula is as follows:
Pi=[I-kihi]Pi-1 (14)。
2. a kind of Kalman's multimodality fusion location algorithm model as described in claim 1 and its method serially updated, special
Sign is that the step (1) specifically includes: 6 moment are respectively (t1, t2, t3, t4, t5, t6), each time at intervals time T
It takes 60s namely time span is 60s.
3. a kind of Kalman's multimodality fusion location algorithm model as described in claim 1 and its method serially updated, special
Sign is that the step (2) specifically includes: being respectively as follows: in (t1, t2, t3, t4, t5, t6) moment satellite position, clock deviation, speed
Pi=[pix piy piz δti vix viy viz]
Then equation can be listed
Wherein τi=i*T, then:
4. a kind of Kalman's multimodality fusion location algorithm model as claimed in claim 3 and its method serially updated, special
Sign is that the step (3) specifically includes: in the satellite position speed of any interested moment t of [t1, t6] between the time
Clock deviation coordinate can be obtained by following formula:
[px py pz δt vx vx vx]=[1 dt dt2 dt3 dt4 dt5]A
Since error can become larger when interpolation point is located at section end, so as time t > t5, renewal time window.
5. a kind of Kalman's multimodality fusion location algorithm model as claimed in claim 4 and its method serially updated, special
Sign is that the step (4) specifically includes:
Step (4.1): setting initial position and clock deviation, generally setting historical position, if can initially be set without historical position
0；
Step (4.2): pseudorange is calculated using equation (2)Calculate the poor δ ρ between pseudo-range measurements and calculated valuei
Step (4.3): by calculated valueIt substitutes into equation (3), finds out ai1, ai2, ai3；
Step (4.4): calculating matrix a finds out Δ x, Δ y, Δ z, Δ δ t by equation (5)u；
Δ x=[aTa]-1aTδρ (5)
Step (4.5): determining whether that iteration terminates, by Δ x, Δ y, Δ z, Δ δ tuAbsolute value and equation (6) calculate δ v, if
Less than threshold value, then iteration terminates, and continues iteration if more than threshold value；
Step (4.6): by Δ x, Δ y, Δ z, Δ δ tuWith initial x, y, z, δ tuIt is added, obtains new state, under these values are used as
The initial value of secondary iteration；
Step (4.7): repeating step (4.1)-step (4.6), and until δ v is less than threshold value, general the number of iterations is no more than 10
It is secondary, if 10 times are also not converged, resolve failure, it may be possible to which observed quantity exists abnormal.
6. a kind of Kalman's multimodality fusion location algorithm model as claimed in claim 5 and its method serially updated, special
Sign is that the step (5) further includes step (5.7): updating system mode X by the updated error state δ of observed quantity
xk+1|k+1The state for time obtained with the first step updates, and can update to the X amount of being observed:
After this step updates,So δ xk+1|k+1In include it is new breath be updated in state
It goes, it is therefore desirable to set δ xk+1|k+1=0；
Step (5.2)-step (5.4) is time update in above-mentioned steps, and e-g is observed quantity update, result per treatment
Pk+1|k+1And Xk+1|k+1It is saved, the initial conditions as next iteration.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110109162A (en) * | 2019-03-26 | 2019-08-09 | 西安开阳微电子有限公司 | A kind of Kalman filtering positioning calculation method that GNSS receiver is adaptive |
| CN111537950A (en) * | 2020-04-14 | 2020-08-14 | 哈尔滨工业大学 | Satellite position prediction tracking method based on position fingerprint and two-step polynomial fitting |
| CN111856531A (en) * | 2020-07-10 | 2020-10-30 | 暨南大学 | Agricultural Internet of things method based on satellite positioning |
| CN113219506A (en) * | 2021-05-07 | 2021-08-06 | 江苏俱为科技有限公司 | Positioning method for multimode fusion seamless switching |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1361431A (en) * | 2000-12-23 | 2002-07-31 | 林清芳 | Complete integral navigation positioning method and system |
| CN103197326A (en) * | 2013-03-25 | 2013-07-10 | 东南大学 | Multi-constellation single base station receiver clock difference estimation method |
| CN103529461A (en) * | 2013-10-14 | 2014-01-22 | 北京大学 | Receiver quick positioning method based on strong tracking filtering and Hermite interpolation method |
| CN106569232A (en) * | 2016-10-31 | 2017-04-19 | 航天恒星科技有限公司 | Space signal accuracy evaluation method and system |
| CN108196267A (en) * | 2017-12-20 | 2018-06-22 | 中国科学院国家授时中心 | A kind of uninterrupted time delivering method based on GNSS CP technologies |
-
2018
- 2018-10-15 CN CN201811195227.0A patent/CN109375248A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1361431A (en) * | 2000-12-23 | 2002-07-31 | 林清芳 | Complete integral navigation positioning method and system |
| CN103197326A (en) * | 2013-03-25 | 2013-07-10 | 东南大学 | Multi-constellation single base station receiver clock difference estimation method |
| CN103529461A (en) * | 2013-10-14 | 2014-01-22 | 北京大学 | Receiver quick positioning method based on strong tracking filtering and Hermite interpolation method |
| CN106569232A (en) * | 2016-10-31 | 2017-04-19 | 航天恒星科技有限公司 | Space signal accuracy evaluation method and system |
| CN108196267A (en) * | 2017-12-20 | 2018-06-22 | 中国科学院国家授时中心 | A kind of uninterrupted time delivering method based on GNSS CP technologies |
Non-Patent Citations (2)
| Title |
|---|
| 吴鹏等: ""基于定点/插值算法的卫星PVT实时计算的优化设计"", 《舰船电子工程》 * |
| 陈杨毅: ""GPS与BD双模GNSS接收机定位解算技术研究"", 《中国优秀硕士学位论文全文数据库信息科技辑》 * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110109162A (en) * | 2019-03-26 | 2019-08-09 | 西安开阳微电子有限公司 | A kind of Kalman filtering positioning calculation method that GNSS receiver is adaptive |
| CN110109162B (en) * | 2019-03-26 | 2021-06-11 | 西安开阳微电子有限公司 | GNSS receiver self-adaptive Kalman filtering positioning resolving method |
| CN111537950A (en) * | 2020-04-14 | 2020-08-14 | 哈尔滨工业大学 | Satellite position prediction tracking method based on position fingerprint and two-step polynomial fitting |
| CN111856531A (en) * | 2020-07-10 | 2020-10-30 | 暨南大学 | Agricultural Internet of things method based on satellite positioning |
| CN113219506A (en) * | 2021-05-07 | 2021-08-06 | 江苏俱为科技有限公司 | Positioning method for multimode fusion seamless switching |
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