CN112051598A - Vehicle-mounted GNSS/INS integrated navigation method based on double correction - Google Patents

Vehicle-mounted GNSS/INS integrated navigation method based on double correction Download PDF

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CN112051598A
CN112051598A CN202010584387.5A CN202010584387A CN112051598A CN 112051598 A CN112051598 A CN 112051598A CN 202010584387 A CN202010584387 A CN 202010584387A CN 112051598 A CN112051598 A CN 112051598A
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kalman filtering
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vehicle
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CN112051598B (en
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马俊
曹成度
夏艳军
滕焕乐
闵阳
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China Railway Siyuan Survey and Design Group Co Ltd
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
    • G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/42—Determining position
    • G01S19/45—Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement
    • G01S19/47—Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement the supplementary measurement being an inertial measurement, e.g. tightly coupled inertial
    • 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
    • G01C21/165—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 combined with non-inertial navigation instruments
    • 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
    • G01S19/40—Correcting position, velocity or attitude
    • G01S19/41—Differential correction, e.g. DGPS [differential GPS]
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/10—Internal combustion engine [ICE] based vehicles
    • Y02T10/40—Engine management systems

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Abstract

The invention belongs to the technical field of satellite navigation positioning, and particularly provides a vehicle-mounted GNSS/INS integrated navigation method based on double correction, which comprises the steps of firstly, initializing a GNSS system, obtaining a fixed solution, and estimating the GNSS position of a vehicle by utilizing extended Kalman filtering according to carrier waves of the GNSS and pseudo-range observation values; and then, calculating the difference between the GNSS position corrected by Kalman filtering and the position output by the INS, performing Kalman filtering estimation again by taking the result as an observation value, performing output correction on the position output by the INS according to the result, and finally obtaining the INS position as the final result of the system position. According to the scheme, firstly, Kalman filtering correction is carried out on the position output by the GNSS, then the corrected GNSS position is used for carrying out Kalman filtering correction on the INS position, and the precision and the reliability of the position of the integrated navigation positioning system are improved. The problem that the accuracy of the output position of the GNSS/INS combined navigation and positioning system is poor under the condition that the satellite signal is poor is solved.

Description

Vehicle-mounted GNSS/INS integrated navigation method based on double correction
Technical Field
The invention belongs to the technical field of satellite navigation and positioning, and particularly relates to a vehicle-mounted GNSS/INS integrated navigation method based on double correction.
Background
The railway is used as a main artery, a national important infrastructure and a popular traffic tool of national economy in China, has the advantages of large transportation capacity, high efficiency, long transportation distance, low energy consumption and the like, is very suitable for basic national conditions of numerous people and broad personnel in China, and plays an important role in the development of the economic society in China. The train operation control system is used as a neural center of railway transportation, is the core for guaranteeing the railway driving safety and improving the transportation efficiency, and needs to accurately grasp information such as the speed, the position and the like of a train in real time. Improving the train operation safety level, reducing the operation and maintenance cost and meeting the line traffic volume has become a research hotspot of a train control system.
With the gradual development and improvement of the GPS, GLONASS, Galileo and the autonomous Beidou satellite navigation system developed in China, the application field of the satellite navigation positioning technology is continuously expanded, and the satellite positioning system technology is utilized to quickly and accurately provide train position information, so that not only can trackside equipment be reduced, the construction and operation cost be reduced, the railway maintenance period be reduced, but also the train positioning precision can be improved, the train running interval time can be shortened, and the line transportation efficiency can be improved. For the satellite positioning system based on the differential positioning technology, the frequency of the output position information is not higher than the frequency (such as 1 HZ/second) of the differential information broadcast by the CORS station, namely, the frequency of the output position of the satellite positioning system is limited by the frequency of the broadcast information of the CORS station. However, for a train with a higher speed per hour (such as a magnetic levitation train reaching 600 km/h), the positioning system must have a higher position output frequency (at least 100HZ) to meet the requirement of real-time positioning of the train. In addition, because the train can not only encounter the problems of shielding satellite signals and the like in tunnels, bridges, hillsides, urban buildings and the like in the actual running environment, but also can be interfered and blocked by electromagnetic waves, the situations of difficult satellite signal reception, loss, errors and the like occur, and the reliability and the accuracy of train positioning are reduced. Therefore, it is difficult to continuously locate the train by using only GNSS (Global Navigation Satellite System) positioning technology. Some researches propose that an INS (inertial Navigation system) sensor is added to perform multi-source information fusion processing, so that the limitation of GNSS positioning is overcome, the advantages and the disadvantages are improved, and an effective train combined positioning function whole body is formed.
The GNSS/INS integrated navigation technology and the Kalman filter form a train integrated navigation positioning system. The INS system ensures the high-frequency output of the position of the integrated navigation positioning system, and meanwhile, the error of the INS system is corrected by utilizing the position information output by the GNSS. In the real-time positioning mode, the position accuracy of the GNSS output is less than that of the static mode. Moreover, when the satellite signal is poor, the position of the GNSS output is a floating solution, and the accuracy is poor. At this time, the INS cannot be effectively corrected, which affects the overall positioning effect of the integrated navigation positioning system and has poor reliability.
Disclosure of Invention
The invention aims to solve the problem that the integral combined navigation positioning system in the prior art has low positioning accuracy and poor reliability under the condition of poor satellite signals.
Therefore, the invention provides a vehicle-mounted GNSS/INS combined navigation method based on double correction, which comprises the following steps:
s1: initializing a GNSS system, and estimating the GNSS position of the vehicle by utilizing extended Kalman filtering according to the carrier wave of the GNSS and the pseudo-range observation value after obtaining a fixed solution;
s2: and (3) calculating the difference between the GNSS position corrected by Kalman filtering and the position output by the INS, performing Kalman filtering estimation again by taking the result as an observation value, performing output correction on the position output by the INS according to the result, and finally obtaining the INS position as the final result of the system position.
Preferably, the S1 specifically includes:
the GNSS system adopts a carrier real-time dynamic differential technology for positioning and adopts extended Kalman filtering for resolving.
Preferably, for a tri-frequency GNSS receiver, the estimation parameters, i.e. the state vector, are the position and the velocity of the receiver and the single-difference carrier phase integer ambiguity at the frequencies P1, P2 and P5, and are as follows:
x＝(rr T,vr T,B1 T,B2 T,B5 T)T (1)
wherein ,rr＝(rx,r,ry,r,rz,r)TRepresenting the position, v, in x, y, z direction of the GNSS receiver outputr＝(vx,r,vy,r,vz,r)TIndicating the velocity in the x, y, z directions of the GNSS receiver output, the subscript r indicating the rover,
Figure BDA0002554079450000031
is LiThe SD ambiguity vector of the carrier wave,
Figure BDA0002554079450000032
representing the single difference carrier-phase integer ambiguity of the rover station from the reference station under observation satellite j, and subscript b representing the reference station.
Preferably, the observation of the filtering system is a double difference of the phase distance and the pseudorange observation, and is formed as follows:
y＝(Φ1, T,Φ2, T,Φ5, T,P1 T,P2 T,P5 T)T (2)
wherein ,
Figure BDA0002554079450000033
is a double difference phase distance vector of frequency i,
Figure BDA0002554079450000034
the double differences of the carrier distances of the rover station and the reference station and the observation satellites j1 and j2 at the frequency i are shown, m is the number of satellites,
Figure BDA0002554079450000035
is a double of frequency iA vector of the difference pseudoranges,
Figure BDA0002554079450000036
is the double difference between the rover and reference stations and the pseudoranges for the observed satellites j1 and j2 at the i frequency.
Preferably, the observation model y ═ h (x) is in the following specific form:
h(x)＝(hΦ,1 T,hΦ,2 T,hΦ,5 T,hP,1 T,hP,2 T,hP,5 T)T (3)
wherein ,
Figure BDA0002554079450000037
in the formula λiIs a carrier wave LiThe wavelength of (a) of (b),
Figure BDA0002554079450000038
for double differences of pseudoranges, the first partial derivative of the estimated parameter is calculated for h (x), and the result is as follows:
Figure BDA0002554079450000041
wherein ,
Figure BDA0002554079450000042
Figure BDA0002554079450000049
is the unit vector of the receiver to satellite line of sight,
Figure BDA0002554079450000043
preferably, the specific formula for estimating the GNSS position of the vehicle using the extended kalman filter includes:
Figure BDA0002554079450000044
Pk/k-1＝FkPk-1/k-1Fk T+Qk
Kk＝Pk/k-1Hk T[Rk+HkPk/k-1Hk T]-1
Figure BDA0002554079450000045
Pk/k＝[I-KkHk]Pk/k-1
wherein
Figure RE-GDA0002739114020000047
Is a slave epoch tk-1To tkThe transformation matrix of (a) is,
Figure RE-GDA0002739114020000048
is the variance matrix of the system noise, taur＝tk-tk-1，QvIs composed ofThree directionsCovariance of velocity.
Preferably, the S2 specifically includes: and correcting the position output by the INS by using the GNSS position corrected by the classical Kalman filtering.
Preferably, the method specifically comprises the following steps:
firstly, initializing a GNSS receiver, obtaining a fixed solution, then combining a navigation positioning system and a vehicle to start running, taking position information output by the GNSS as an initial value, and inputting the initial position information to a filtering system
Figure BDA0002554079450000048
According to the estimation parameters such as the GNSS position and the like calculated after the previous epoch GNSS extended Kalman filtering, a matrix required by the epoch GNSS extended Kalman filtering estimation is constructed, the carrier distance and the pseudo-range double difference value are calculated according to the carrier signal and the pseudo-range received by the epoch GNSS receiver, and the result is constructed into the observation quantity y of the extended Kalman filtering (phi is phi)1, T,Φ2, T,Φ5, T,P1 T,P2 T,P5 T)TAccording to the formula
Figure BDA0002554079450000051
Calculating GNSS position of vehicle
Figure BDA0002554079450000052
According to the position output by the epoch INS system
Figure BDA0002554079450000053
And calculating the observation quantity of the GNSS/INS integrated navigation system Kalman filtering by the GNSS position corrected by the Kalman filtering
Figure BDA0002554079450000054
Meanwhile, the position of the INS output after the last epoch Kalman correction is utilized
Figure BDA0002554079450000055
Calculating the required matrix in classical Kalman filtering, including Ak/k-1，Bk/k-1，Pk/k-1，Kk；
According to the formula
Figure BDA0002554079450000056
Calculating the state estimation value of the system, and correcting the position output by the INS by using the estimation result:
Figure BDA0002554079450000057
finally obtain tkAnd estimating the corrected system position by using time Kalman filtering.
The invention has the beneficial effects that: the invention provides a vehicle-mounted GNSS/INS integrated navigation method based on double correction, which comprises the steps of firstly, initializing a GNSS system, obtaining a fixed solution, and estimating the GNSS position of a vehicle by utilizing extended Kalman filtering according to carrier waves of the GNSS and pseudo-range observation values; and then, calculating the difference between the GNSS position corrected by Kalman filtering and the position output by the INS, performing Kalman filtering estimation again by taking the result as an observation value, and performing output correction on the position output by the INS according to the result, wherein the finally obtained INS position is the final result of the system position. According to the scheme, firstly, Kalman filtering correction is carried out on the position output by the GNSS, and then the corrected GNSS position is used for carrying out Kalman filtering correction on the INS position, so that the precision and the reliability of the position of the integrated navigation positioning system are improved. The problem of poor accuracy of the output position of the GNSS/INS integrated navigation positioning system under the condition of poor satellite signals is solved.
The present invention will be described in further detail below with reference to the accompanying drawings.
Drawings
FIG. 1 is a schematic diagram illustrating the principle of the dual calibration based on-board GNSS/INS integrated navigation method of the present invention;
FIG. 2 is a flow chart of the method for vehicle-mounted GNSS/INS integrated navigation based on dual calibration according to the present invention;
FIG. 3 is a schematic diagram of a combined navigation trajectory of the vehicle-mounted GNSS/INS combined navigation method based on dual calibration according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the description of the present invention, it is to be understood that the terms "center", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", and the like indicate orientations or positional relationships based on those shown in the drawings, and are only for convenience of description and simplicity of description, and do not indicate or imply that the referenced devices or elements must have a particular orientation, be constructed and operated in a particular orientation, and thus, are not to be construed as limiting the present invention.
The terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature; in the description of the present invention, "a plurality" means two or more unless otherwise specified.
The embodiment of the invention provides a vehicle-mounted GNSS/INS integrated navigation method based on double correction, which comprises the following steps:
s1: initializing a GNSS system, and estimating the GNSS position of the vehicle by utilizing extended Kalman filtering according to the carrier wave of the GNSS and the pseudo-range observation value after obtaining a fixed solution;
s2: and (3) calculating the difference between the GNSS position corrected by Kalman filtering and the position output by the INS, performing Kalman filtering estimation again by taking the result as an observation value, performing output correction on the position output by the INS according to the result, and finally obtaining the INS position as the final result of the system position.
As shown in FIG. 1, the correction principle is mixed for the GNSS/INS integrated navigation system. The brief contents of this figure are as follows: firstly, initializing a GNSS system, and estimating the GNSS position of the vehicle by utilizing extended Kalman filtering according to a carrier wave of the GNSS and a pseudo-range observation value after obtaining a fixed solution; and secondly, calculating the difference between the GNSS position corrected by Kalman filtering and the position output by the INS, performing Kalman filtering estimation again by taking the result as an observation value, and performing output correction on the position output by the INS according to the result, wherein the finally obtained INS position is the final result of the system position.
The invention researches a method for doubly correcting the position of a train by using Kalman filtering estimation in a vehicle-mounted GNSS/INS integrated navigation positioning system. When the GNSS system and the INS system work simultaneously, firstly, carrier phase and pseudo range received by the GNSS are utilized to carry out extended Kalman filtering estimation to obtain position information of the GNSS. And on the basis, performing Kalman filtering estimation again by using the corrected train GNSS position and other navigation parameters of the INS system, outputting and correcting the train position provided by the INS according to a filtering result, and finally outputting the train position.
Compared with the traditional train GNSS/INS combined positioning method, the method corrects the position of the combined navigation system twice. According to the invention, firstly, the position output by the GNSS is corrected by using the extended Kalman filtering, and on the basis, the output of the integrated navigation positioning system is corrected by using the classical Kalman filtering, so that the position precision and the reliability of the integrated navigation system are improved.
The principle of the scheme is shown in fig. 1 and fig. 2, and the scheme specifically comprises the following steps:
(1) GNSS real-time positioning Kalman filtering correction
The GNSS system adopts a carrier real-time dynamic differential technology for positioning and adopts extended Kalman filtering for resolving. Assume for a tri-band GNSS receiver that the estimated parameters, i.e., the state vector, are the position and velocity of the receiver and the single-differenced (SD) carrier-phase integer ambiguity at the P1, P2, and P5 frequencies. The specific form is as follows:
x＝(rr T,vr T,B1 T,B2 T,B5 T)T (1)
wherein ,rr＝(rx,r,ry,r,rz,r)TRepresenting the position, v, in x, y, z direction of the GNSS receiver outputr＝(vx,r,vy,r,vz,r)TIndicating the velocity in the x, y, z directions of the GNSS receiver output, the subscript r indicating the rover,
Figure BDA0002554079450000081
is LiThe SD ambiguity vector of the carrier wave,
Figure BDA0002554079450000082
representing the single difference carrier-phase integer ambiguity of the rover station from the reference station under observation satellite j, and subscript b representing the reference station.
The observed quantity of the filtering system is double differences of phase distance and pseudo-range observed values, and the specific form is as follows:
y＝(Φ1, T,Φ2, T,Φ5, T,P1 T,P2 T,P5 T)T (2)
wherein ,
Figure BDA0002554079450000083
is a double difference phase distance vector of frequency i,
Figure BDA0002554079450000084
the double differences of the carrier distances of the rover station and the reference station and the observation satellites j1 and j2 at the frequency i are shown, m is the number of satellites,
Figure BDA0002554079450000085
is a double-differenced pseudorange vector for frequency i,
Figure BDA0002554079450000086
is the double difference between the rover and reference stations and the pseudoranges for the observed satellites j1 and j2 at the i frequency.
The observed quantity model y is h (x), and the specific form is as follows:
h(x)＝(hΦ,1 T,hΦ,2 T,hΦ,5 T,hP,1 T,hP,2 T,hP,5 T)T (3)
wherein ,
Figure BDA0002554079450000087
in the formula λiIs a carrier wave LiThe wavelength of (a) of (b),
Figure BDA0002554079450000088
double differences in pseudoranges. The first partial derivative of the estimated parameter for h (x) is calculated as follows:
Figure BDA0002554079450000091
wherein ,
Figure BDA0002554079450000092
Figure BDA00025540794500000913
is the unit vector of the receiver to satellite line of sight,
Figure BDA0002554079450000093
the covariance matrix of the observations is as follows:
Figure BDA0002554079450000094
wherein
Figure BDA0002554079450000095
And
Figure BDA0002554079450000096
GNSS observation errors for the carrier and pseudoranges,
Figure BDA0002554079450000097
is the standard deviation of the Li carrier phase distance,
Figure BDA0002554079450000098
is LiStandard deviation of carrier pseudorange measurements.
By using extended kalman filtering, more accurate position information can be obtained, and the specific formula is as follows:
Figure BDA0002554079450000099
Pk/k-1＝FkPk-1/k-1Fk T+Qk
Kk＝Pk/k-1Hk T[Rk+HkPk/k-1Hk T]-1
Figure BDA00025540794500000910
Pk/k＝[I-KkHk]Pk/k-1
wherein
Figure RE-GDA00027391140200000912
Is a slave epoch tk-1To tkThe transformation matrix of (a) is,
Figure RE-GDA00027391140200000913
is the variance matrix of the system noise, taur＝tk-tk-1，QvIs composed ofThree directionsCovariance of velocity.
(2) And correcting the GNSS/INS integrated navigation Kalman filtering.
And correcting the position output by the INS by using the GNSS position corrected by the Kalman filtering. This time, output correction is performed by adopting classic Kalman filtering.
The coordinate system of the inertial navigation system is a northeast coordinate system, and the system state variables are as follows:
Figure BDA0002554079450000101
wherein phi is [ phi ]x φy φz]For the angle of misalignment of the platform, v ═ vx vy vz]For speed error, p ═ L λ h]Is a position error;b＝[bx by bz]is a random constant of the drift of the gyro,r＝[rx ry rz]in order to shift the first order markov process,
Figure BDA0002554079450000102
for accelerometer null errors, the subscripts x, y, z now represent the east, north, and sky axes, respectively, and L, λ, and h represent the latitude, longitude, and altitude of the carrier, respectively. The system error state equation is:
Figure BDA0002554079450000103
in the above formula, x (t) has an initial value of 0, f (t) is a state transition matrix, w (t) is a system noise matrix, and g (t) is a noise driving matrix. The system adopts a loose combination mode, and uses the position difference provided by the GNSS and the INS as the measurement information of Kalman filtering. Since the GNSS system outputs the position in the WGS84 coordinate system, it must be converted into the coordinates in the world coordinate system
Figure BDA0002554079450000104
And then differenced with the position provided by the INS.
The measurement equation is as follows:
Z(t)＝H(t)X(t)+V(t) (7)
in the above formula, quantity is measured
Figure BDA0002554079450000105
The difference between latitude, longitude and height is respectively, and subscripts I and G respectively represent INS and GNSS system;
Figure BDA0002554079450000106
v (t) is the noise of the measurement signal.
According to tk-1System position [ L ] of epoch after Kalman filtering output correctionk-1 λk-1 hk-1]TAnd attitude quaternion, updating matrix F in equation of state (6)k,k-1And Gk,k-1. Converting the equation of continuous state equation (1) into a discrete equation of state: xk＝Ak,k-1Xk-1+Bk,k-1Wk-1Wherein A isk,k-1＝I+Fk,k-1Ts， Bk,k-1＝[I+Fk,k-1Ts/2]×Gk,k-1×Ts，TsThe period of filtering.
Constructing a matrix necessary for a Kalman filtering process, comprising:
one-step prediction variance matrix
Figure BDA0002554079450000111
Estimating covariance matrix Pk＝[I-KkHk]×Pk,k-1
Filter gain matrix
Figure BDA0002554079450000112
Wherein Qk-1Is the variance matrix, R, of the INS system noisekFor the variance matrix of the measured values, it is worth to be noted that the prediction variance matrix, the estimation covariance matrix, and the filter gain matrix in the kalman filtering process are different from the correlation matrix in the GNSS extended kalman filtering. According to tkDifference between system position output by time INS and GNSS
Figure BDA0002554079450000113
Obtaining a filtered estimation result
Figure BDA0002554079450000114
Wherein
Figure BDA0002554079450000115
And outputting a corrected estimation result for the previous epoch INS Kalman filtering. Where the superscript 0 represents the state (including position and velocity) of the INS output prior to correction by the kalman filter. Correcting the position output by the INS according to the filtering estimation result to obtain the final Kalman filtering result of the integrated navigation system
Figure BDA0002554079450000116
Wherein
Figure BDA0002554079450000117
Are respectively as
Figure BDA0002554079450000118
The 7 th, 8 th and 9 th elements of the INS system, i.e. the estimation result of the INS system position error. The specific principle of the above process is shown in fig. 1.
In a specific implementation scenario, by taking data acquired by a vehicle-mounted GNSS/INS system in a friendship square of Wuhan university as an example, the position of the vehicle in operation is obtained in real time by adopting Kalman filtering double correction. Fig. 2 is a flow chart of the method, and the specific steps are as follows:
(1) firstly, initializing a GNSS receiver, obtaining a fixed solution, then combining a navigation positioning system and a vehicle to start running, taking position information output by the GNSS as an initial value, and inputting the initial position information to a filtering system
Figure BDA0002554079450000121
Estimation covariance matrices P belonging to GNSS systems and INS systems respectively0System noise variance matrix Q0Variance matrix R of measurement information0And navigation parameters of the INS system (including speed, attitude angle, attitude quaternion, specific force in the train geographic coordinate system).
(2) Constructing a matrix required by the current epoch GNSS extended Kalman filtering estimation according to the estimation parameters such as the GNSS position calculated after the previous epoch GNSS extended Kalman filtering, such as Fk，Pk/k-1，Pk/k，Kk. Calculating a carrier distance and a pseudo range double difference value according to the carrier signal and the pseudo range received by the epoch GNSS receiver, and forming an observation quantity y of the extended Kalman filtering as (phi)1, T,Φ2, T,Φ5, T,P1 T,P2 T,P5 T)T. According to the formula
Figure BDA0002554079450000122
Calculating GNSS position of vehicle
Figure BDA0002554079450000123
(3) According to the position output by the epoch INS system
Figure BDA0002554079450000124
And GNSS position calculation after Kalman filtering correctionObserved quantity of GNSS/INS integrated navigation system Kalman filtering
Figure BDA0002554079450000125
Meanwhile, the position of the INS output after the last epoch Kalman correction is utilized
Figure BDA0002554079450000126
Calculating the required matrix in the classic Kalman Filter, including Ak/k-1，Bk/k-1，Pk/k-1， Kk。
According to the formula
Figure BDA0002554079450000127
Calculating the state estimation value of the system, and correcting the position output by the INS by using the estimation result:
Figure BDA0002554079450000128
finally obtain tkAnd estimating the corrected system position by using time Kalman filtering. Because absolute coordinates are not available, only travel route without reference is shown in fig. 3, and the accuracy of the output covariance matrix is about 3cm, which proves the effectiveness of the invention in improving the positioning accuracy of the GNSS/INS combined navigation.
The invention has the beneficial effects that: the invention provides a vehicle-mounted GNSS/INS integrated navigation method based on double correction, which comprises the steps of firstly, initializing a GNSS system, obtaining a fixed solution, and estimating the GNSS position of a vehicle by utilizing extended Kalman filtering according to carrier waves of the GNSS and pseudo-range observation values; and then, calculating the difference between the GNSS position corrected by Kalman filtering and the position output by the INS, performing Kalman filtering estimation again by taking the result as an observation value, and performing output correction on the position output by the INS according to the result, wherein the finally obtained INS position is the final result of the system position. According to the scheme, firstly, Kalman filtering correction is carried out on the position output by the GNSS, and then the corrected GNSS position is used for carrying out Kalman filtering correction on the INS position, so that the precision and the reliability of the position of the integrated navigation positioning system are improved. The problem of poor accuracy of the output position of the GNSS/INS integrated navigation positioning system under the condition of poor satellite signals is solved.
The above examples are merely illustrative of the present invention and should not be construed as limiting the scope of the invention, which is intended to be covered by the claims and any design similar or equivalent to the scope of the invention.

Claims (8)

1. A vehicle-mounted GNSS/INS combined navigation method based on double correction is characterized by comprising the following steps:
s1: initializing a GNSS system, and estimating the GNSS position of the vehicle by utilizing extended Kalman filtering according to the carrier wave of the GNSS and the pseudo-range observation value after obtaining a fixed solution;
s2: and (3) calculating the difference between the GNSS position corrected by Kalman filtering and the position output by the INS, performing Kalman filtering estimation again by taking the result as an observation value, performing output correction on the position output by the INS according to the result, and finally obtaining the INS position as the final result of the system position.
2. The dual calibration based on-board GNSS/INS combined navigation method of claim 1, wherein the S1 specifically comprises:
the GNSS system adopts a carrier real-time dynamic differential technology for positioning and adopts extended Kalman filtering for resolving.
3. The vehicle-mounted GNSS/INS combined navigation method based on dual calibration as claimed in claim 2, wherein: for a tri-band GNSS receiver, the estimation parameters, i.e., the state vector, are the position and the velocity of the receiver and the single-difference carrier phase integer ambiguity at the frequencies P1, P2, and P5, and the specific form is as follows:
x＝(rr T,vr T,B1 T,B2 T,B5 T)T (1)
wherein r isr＝(rx,r,ry,r,rz,r)TRepresenting the position, v, in x, y, z direction of the GNSS receiver outputr＝(vx,r,vy,r,vz,r)TIndicating the velocity in the x, y, z directions of the GNSS receiver output, the subscript r indicating the rover,
Figure FDA0002554079440000011
is LiThe SD ambiguity vector of the carrier wave,
Figure FDA0002554079440000012
representing the single difference carrier-phase integer ambiguity of the rover station from the reference station under observation satellite j, and subscript b representing the reference station.
4. The vehicle-mounted GNSS/INS combined navigation method based on dual calibration as claimed in claim 2, wherein: the observed quantity of the filtering system is double differences of phase distance and pseudo-range observed values, and the specific form is as follows:
y＝(Φ1,T,Φ2,T,Φ5,T,P1 T,P2 T,P5 T)T (2)
wherein,
Figure FDA0002554079440000021
is a double-difference phase distance vector for frequency i,
Figure FDA0002554079440000022
the double differences of the carrier distances of the rover station and the reference station and the observation satellites j1 and j2 at the frequency i are shown, m is the number of satellites,
Figure FDA0002554079440000023
is a double-differenced pseudorange vector for frequency i,
Figure FDA0002554079440000024
is the double difference between the rover and reference stations and the pseudoranges for the observed satellites j1 and j2 at the i frequency.
5. The dual calibration based combined GNSS/INS navigation method of claim 4, wherein:
the observed quantity model y is h (x), and the specific form is as follows:
h(x)＝(hΦ,1 T,hΦ,2 T,hΦ,5 T,hP,1 T,hP,2 T,hP,5 T)T (3)
wherein,
Figure FDA0002554079440000025
in the formula ofiIs a carrier wave LiThe wavelength of (a) of (b),
Figure FDA0002554079440000026
for double differences of pseudoranges, the first partial derivative of the estimated parameter is calculated for h (x), and the result is as follows:
Figure FDA0002554079440000027
wherein,
Figure FDA0002554079440000028
Figure FDA0002554079440000029
is the unit vector of the receiver to satellite line of sight,
Figure FDA00025540794400000210
6. the vehicle-mounted GNSS/INS combined navigation method based on dual calibration as claimed in claim 2, wherein the specific formula for estimating the GNSS position of the vehicle using the extended Kalman filter comprises:
Figure RE-FDA00027391140100000211
Pk/k-1＝FkPk-1/k-1Fk T+Qk
Kk＝Pk/k-1Hk T[Rk+HkPk/k-1Hk T]-1
Figure RE-FDA0002739114010000031
Pk/k＝[I-KkHk]Pk/k-1
wherein
Figure RE-FDA0002739114010000032
Is a slave epoch tk-1To tkThe transformation matrix of (a) is,
Figure RE-FDA0002739114010000033
is the variance matrix of the system noise, taur＝tk-tk-1，QvIs composed ofThree directionsCovariance of velocity.
7. The dual calibration based on-board GNSS/INS combined navigation method of claim 1, wherein the S2 specifically comprises: and correcting the position output by the INS by using the GNSS position corrected by the classical Kalman filtering.
8. The dual calibration based vehicle-mounted GNSS/INS integrated navigation method according to claim 1, further comprising:
firstly, initializing a GNSS receiver, obtaining a fixed solution, then combining a navigation positioning system and a vehicle to start running, taking position information output by the GNSS as an initial value, and inputting the initial position information to a filtering system
Figure FDA0002554079440000034
According to the previous oneThe method comprises the steps of constructing a matrix required by the epoch GNSS extended Kalman filtering estimation by using estimation parameters such as GNSS positions and the like calculated after epoch extended Kalman filtering, calculating a carrier distance and a pseudo-range double difference value according to a carrier signal received by an epoch GNSS receiver and a pseudo-range, and forming an observation quantity y of the extended Kalman filtering by using the result (phi is phi)1,T,Φ2,T,Φ5,T,P1 T,P2 T,P5 T)TAccording to the formula
Figure FDA0002554079440000035
Calculating GNSS position of vehicle
Figure FDA0002554079440000036
According to the position output by the epoch INS system
Figure FDA0002554079440000037
And calculating the observation quantity of the GNSS/INS integrated navigation system Kalman filtering by the GNSS position corrected by the Kalman filtering
Figure FDA0002554079440000041
Meanwhile, the position of the INS output after the last epoch Kalman correction is utilized
Figure FDA0002554079440000042
Calculating the required matrix in the classic Kalman Filter, including Ak/k-1，Bk/k-1，Pk/k-1，Kk；
According to the formula
Figure FDA0002554079440000043
Calculating the state estimation value of the system, and correcting the position output by the INS by using the estimation result:
Figure FDA0002554079440000044
finally obtain tkTime cardThe kalman filtering estimates the corrected system position.
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