CN110296709A - Vehicle mounted positioning navigation method based on adaptive odometer model - Google Patents
Vehicle mounted positioning navigation method based on adaptive odometer model 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
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/28—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network with correlation of data from several navigational instruments
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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
- G01S19/48—Determining position by combining or switching between position solutions derived from the satellite radio beacon positioning system and position solutions derived from a further system
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Abstract
The invention discloses a kind of vehicle mounted positioning navigation methods based on adaptive odometer model, comprising: establishes the state vector transfer matrix between multi-motion model and each model；Counted by the mileage of left and right sidesing driving wheel and geometrical constraint estimation vehicle initial motion state；In conjunction with volume Kalman filtering, each motion model parallel filtering；The motion model for judging current time vehicle, the status information of fusion output vehicle are counted according to the mileage of left and right sidesing driving wheel, the initial value as subsequent time is filtered iteration.The present invention determines optimum gain battle array according to the covariance matrix of the amount of being estimated and measurement using volume Kalman filtering, does not have extra demand to system equation and measurement equation, and superiority is had more under conditions of nonlinear system；Geometrical-restriction relation between odometer improves the positioning accuracy and real-time of vehicle driving；And odometer output information is not blocked by building to be influenced, and the continuity of positioning can be improved.
Description
Technical field
The present invention relates to vehicle mounted positioning navigations, and in particular to a kind of vehicle mounted positioning navigation based on adaptive odometer model
Method.
Background technique
Onboard navigation system has become the indispensable configuration that people drive to go on a journey, and current vehicle location and navigation system is most
Be it is nonlinear, the system equation and observational equation of mobility model are also all nonlinear.Existing spreading kalman
Filtering algorithm carries out linearization approximate to nonlinear function at single-point, fairly simple to the approximation of system state distribution, and
System mode occur cataclysm when, restrain it is slow, cannot timely strained handling, in face of emergency safety in terms of still
It is unable to satisfy.
Existing vehicle location and navigation system multi-pass crosses loading GPS receiver device, obtains speed, the position etc. of positioning target
Information.But there is also certain disadvantages for GPS positioning, when GPS signal is blocked by building, positioning continuity just by
It destroys, it is difficult to guarantee the stability of location navigation function under special circumstances.And error model of the GPS when carrying out vehicle location
It is trapped among 14m or so, setting accuracy is still to be improved.
Summary of the invention
Goal of the invention: it is an object of the present invention to provide a kind of vehicle mounted positioning navigation methods of adaptive odometer model, improve
The accuracy and continuity of vehicle mounted positioning navigation improve the stability of navigation performance under obstacle environment.
Technical solution: the present invention provides a kind of vehicle mounted positioning navigation method based on adaptive odometer model, the party
Method includes:
(1) according to vehicle m nonlinear motion model of m motion state correspondence establishment that may be present and m model
Transfer matrix T between state vectorI, j；
The state equation of nonlinear motion model is expressed as
Wherein, i, j=1,2 ... m, i, j indicate corresponding nonlinear motion model, TI, jAs from model i to model j
Transfer matrix, k be vehicle travel process in sampling instant,It is the state vector at k moment,It is k moment odometer
Output data,It is the white noise sequence that mean value is zero；
(2) data for acquiring k moment left and right vehicle wheel driving wheel odometer, according to the vehicle-state in vehicle travel process
Geometrical-restriction relation analyzes the move distance and position and attitude data of the odometer acquisition of left and right sidesing driving wheel, obtains vehicle center
The move distance and change in location data of point；
(3) volume Kalman filtering is merged, GPS observation, the output of each motion model of parallel computation is added, comprising:
(31) the non-linear observational equation Z of volume Kalman filtering is establishedk=h (Xk, Vk), XkIt is the state vector of system,
VkFor systematic observation noise sequence；
(32) by the error co-variance matrix P at k-1 momentJ, k-1 | k-1Chol is carried out to decompose to obtain lower triangular matrix Sk-1|k-1；
(33) admixture at k-1 moment is estimated2n are determined according to spherical surface-radius rule
Cubature point, according to cubature pointCubature point X is calculatedI, k-1 | k-1And its corresponding weight wi, n
For the dimension of model state vector；
(34) by cubature point XI, k-1 | k-1By the state equation of model j, the one-step prediction to do well is calculated
Cubature point value X* I, k | k-1, and calculate the one-step prediction value to do wellWith one-step prediction covariance matrix PJ, k | k-1；
(35) by one-step prediction covariance matrix PJ, k | k-1Chol decomposition is carried out, lower triangular matrix S is obtainedk|k-1；
(36) lower triangular matrix S is utilizedk|k-12n cubature point X is calculatedI, k | k-1And its corresponding weight wi；
(37) by cubature point XI, k | k-1By systematic observation equation, observation predicted value is found outAnd utilize 2n
A observation predicted value is estimated to calculate observation whole at this time
(38) the observation covariance matrix P at k moment is calculatedZz, k | k-1And state observation Cross-covariance PXz, k | k-1；
(39) it measures and updates, calculate volume Kalman filtering gain Kj, state estimation of the computation model j at the k momentAnd error co-variance matrix PJ, k | k；
(4) displacement data in left and right vehicle wheel driving wheel odometer is analyzed, judges motion state locating for current vehicle, and
Match corresponding motion model；Model probability is updated according to matched motion model；
(5) fusion output interaction, obtains k moment total state estimationP is estimated with total covariancek|k；Judge vehicle
Whether traveling stops, if stopping, terminating to filter；Otherwise, return step (2).
Further, in step (1), geometrical-restriction relation are as follows:
ΔdA=(Δ dB+ΔdC)/2
γA=(- AdB+ΔdC)/L
Wherein, Δ dB、ΔdCWith Δ dAIt is the position of left and right two-wheeled odometer and vehicle center point within the sampling interval respectively
It moves, L is the width of vehicle, γAIt is position and attitude deviation angle of the vehicle center within the sampling interval.
Further, in step (33), the specific determining method of cubature point is as follows:
Wherein, vector set P is made of the unit vector for being only 1,0, -1 in each dimension, and n is state vector dimension.
Further, in step (34), one-step prediction valueWith one-step prediction covariance matrix PJ, k | k-1It is specific
Method is as follows:
Wherein, Γk|k-1It is the process noise driving battle array of system, Qk-1It is systematic procedure noise wk-1Variance matrix.
Further, in step (38) (39), observation predicted value isObservation covariance matrix is PZz, k | k-1And shape
It is P that state, which observes Cross-covariance,Xz, k | k-1The specific method is as follows:
Wherein, RkFor observation noise covariance matrix.
Further, in step (5), total state estimationP is estimated with total covariancek|kThe specific method is as follows:
Wherein, μj(k) probability for being k moment motion model j.
The utility model has the advantages that compared with prior art, positioning navigation method of the invention is pacified on the left and right sidesing driving wheel of vehicle
Fill odometer, and the relative position of the two equipment and vehicle remains constant, thus each moment always remain it is fixed
Geometrical relationship.Using the constraint relationship, GPS can be reduced to the error of observation of vehicle location coordinate survey data.By to a left side
The analysis that right wheel mileage counts, as the foundation that rate pattern, Fast track surgery, Turn Models switch, instead of the prior art
In covariance matrix analysis probabilistic method, can more accurate switching model, thus improve the precision of Kalman filtering,
Stability.
Detailed description of the invention
Fig. 1 is positioning navigation method flow chart of the invention；
Fig. 2 is the geometrical relationship figure of odometer on left and right vehicle wheel driving wheel；
Fig. 3-1 is vehicle two dimensional motion of the present invention track estimation figure；
Fig. 3-2 is the partial enlarged view of vehicle two dimensional motion of the present invention track estimation figure；
Fig. 4 is vehicle of the present invention velocity estimation figure in the Y direction；
Fig. 5 is comparison diagram of the present invention with the vehicle using CKF-IMM algorithm in whole position error；
Fig. 6 is the present invention and the partial enlarged view that positioning result is integrally emulated using the vehicle of CKF-IMM algorithm.
Specific embodiment
The present invention is described further with reference to the accompanying drawings and examples:
The present invention provides a kind of vehicle mounted positioning navigation methods based on adaptive odometer model, as shown in Figure 1, the party
Method includes:
(1) according to vehicle m nonlinear motion model of m motion state correspondence establishment that may be present and m model
Transfer matrix T between state vectorI, j；
Nonlinear motion model is expressed as
Wherein, i, j=1,2 ... m, j indicate corresponding nonlinear motion model, TI, jAs from model i to model j's
Transfer matrix, k are the sampling instant in vehicle travel process,It is the state vector at k moment,It is the defeated of k moment odometer
Data out,It is the white noise sequence that mean value is zero；
(2) data for acquiring k moment left and right vehicle wheel driving wheel odometer, according to the vehicle-state in vehicle travel process
Geometrical-restriction relation analyzes the move distance and position and attitude data of the odometer acquisition of left and right sidesing driving wheel, obtains vehicle center
The move distance and change in location data of point；Wherein, as shown in Fig. 2, the geometrical-restriction relation of vehicle-state are as follows:
ΔdA=(Δ dB+ΔdC)/2 (2)
γA=(- Δ dB+ΔdC)/L (3)
In formula, Δ dB、ΔdCWith Δ dAIt is the position of left and right two-wheeled odometer and vehicle center point within the sampling interval respectively
It moves, L is the width of vehicle, γAIt is position and attitude deviation angle of the vehicle center within the sampling interval.
(3) fusion volume Kalman filtering, addition GPS observation, the output of each motion model of parallel computation, including it is following
Step:
Establish the non-linear observational equation Z of volume Kalman filteringk=h (Xk, Vk), XkIt is the state vector of system, VkFor
Systematic observation noise sequence；
By the error co-variance matrix P at k-1 momentJ, k-1 | k-1Chol is carried out to decompose to obtain lower triangular matrix Sk-1|k-1；
The admixture at k-1 moment is estimated2n cubature is determined according to spherical surface-radius rule
Point, according to cubature pointCubature point X is calculatedI, k-1 | k-1And its corresponding weight wi, n is model shape
The dimension of state vector；The specific determining method of cubature point is as follows:
Wherein, vector set P is made of the unit vector for being only 1,0, -1 in each dimension, and n is state vector dimension.
By cubature point XI, k-1 | k-1By the state equation of model j, the one-step prediction cubature point to do well is calculated
Value X* I, k | k-1, and calculate the one-step prediction value to do wellWith one-step prediction covariance matrix PJ, k | k-1；The two by with
Lower method calculates:
Wherein, Γk|k-1It is the process noise driving battle array of system, Qk-1It is systematic procedure noise wk-1Variance matrix.
By one-step prediction covariance matrix PJ, k | k-1Chol decomposition is carried out, lower triangular matrix S is obtainedk|k-1；
Utilize lower triangular matrix Sk|k-12n cubature point X is calculatedI, k | k-1And its corresponding weight wi；
By cubature point XI, k | k-1By systematic observation equation, observation predicted value is found outAnd utilize 2n sight
It is estimated to calculate observation whole at this time to examine predicted valueCalculate observation covariance matrix P at this timeZz, k | k-1And state
Observe Cross-covariance PXz, k | k-1；Its calculation method such as formula (9)~(12):
Wherein, RkFor observation noise covariance matrix.
It measures and updates, calculate volume Kalman filtering gain Kj, state estimation of the computation model j at the k momentWith
And error co-variance matrix PJ, k | k；Its calculation method such as formula (13)~(14)
(4) displacement data in left and right vehicle wheel driving wheel odometer is analyzed, judges motion state locating for current vehicle, and
Match corresponding motion model:
Judge whether the displacement of two odometers respectively current time and previous moment is identical, i.e., whether meets simultaneously following public
Formula:
ΔdB, k-ΔdB, k-1==0 (15)
ΔdC, k-ΔdC, k-1==0 (16)
Wherein, Δ dB、ΔdCIt is the displacement data of left and right odometer respectively；
If not satisfied, being then determined as even acceleration model；
If mileage, which counts, meets formula (15) (16), i.e. current time is identical as the displacement of previous moment；Continue according to public affairs
Formula (17) compares left and right odometer current value:
|ΔdB, k-ΔdC, k| >=a, a are constant (17)
Wherein, a indicates motor vehicle under turn condition, the displacement difference in the left and right wheels sampling interval, in a particular embodiment,
A is according to the vehicle width of vehicle come value.
If left and right mileage count value meets formula (17), Turn Models are determined as, are otherwise determined as uniform rectilinear's model.
After the completion of Model checking, model probability is updated according to the motion model of differentiation.
(5) fusion output interaction, obtains k moment total state estimationP is estimated with total covariancek|k；Judge vehicle
Whether traveling stops, if stopping, terminating to filter；Otherwise, return step (2).Total state estimationWith total covariance
Estimate Pk|kThe specific method is as follows:
Wherein, μj(k) probability for being k moment motion model j.
The method performance and positioning accuracy improvement effect of this method are by being implemented as follows further explanation:
The state vector being arranged in the embodiment of the present invention includes the displacement of vehicle, velocity and acceleration information, dimension 6
Dimension, observation vector include the displacement in the direction x and the displacement in the direction y, and dimension is 2 dimensions.Vehicle width is set in emulation experiment as 3m, constant
A value 0.03.Vehicle is located at (10,50) coordinate at 45 degree of two-dimensional space inceptive direction angle, initial position in emulation, initial speed
Degree is (20,20).0-100 filter point does linear uniform motion；100-200 filter point does retarded motion；200-700 point
Do at the uniform velocity turning motion；700-800 point makees uniformly accelrated rectilinear motion.The motion profile estimation figure of vehicle is as shown in Figure 3.From figure
3 as can be seen that the GPS observation data got there are biggish noise error, it is aobvious using the auditory localization cues after odometer model
Write the fitting degree improved with actual motion track.
Velocity tracking scenario when state of motion of vehicle suddenly change is tested, experimental result is as shown in Figure 4.It is independent compared to rising
Volume Kalman filtering algorithm, be added odometer model after, the precision of velocity estimation is significantly improved and reduces time delay.
Comparison of the vehicle of the measurement present invention and CKF-IMM algorithm in whole position error, experimental data unit is rice
(m), experimental result is as shown in Figure 5.It can be seen that vehicle is when carrying out uniform rectilinear or speed change straight line or turning motion, use is inner
The position error of journey meter model is respectively less than CKF-IMM algorithm.
The measurement present invention is as shown in Figure 6 with the location simulation result partial enlarged view of the vehicle of CKF-IMM algorithm.Experiment pair
The performance for having compared this method Yu CKF-IMM algorithm, in the case where encountering the scene that the mobilities states such as turning persistently change, the present invention
The position error of the positioning navigation method of offer is significantly less than CKF-IMM algorithm.
Present invention introduces the analyses of the data of multiple odometer equipment, and different movement shapes is corresponded to using different motion models
State merges volume Kalman filtering, solves the problems, such as that locating effect is bad when vehicular motion state suddenly change, can be realized certainly
Adaptive filtering.And the probability analysis method of original covariance matrix is substituted, also there is biggish mention in the speed of service of algorithm
It rises, and can be used in the road conditions that GPS signal lacks.
Claims (6)
1. a kind of vehicle mounted positioning navigation method based on adaptive odometer model characterized by comprising
(1) according to vehicle m nonlinear motion model of m motion state correspondence establishment that may be present and m model state
Transfer matrix T between vectori,j；
The state equation of the nonlinear motion model is expressed as
Wherein, i, j=1,2 ... m, i, j indicate corresponding nonlinear motion model, Ti,jAs turn from model i to model j
Matrix is moved, k is the sampling instant in vehicle travel process,It is the state vector at k moment,It is the output of k moment odometer
Data,It is the white noise sequence that mean value is zero；
(2) data for acquiring k moment left and right vehicle wheel driving wheel odometer, according to the geometry of the vehicle-state in vehicle travel process
The constraint relationship analyzes the move distance and position and attitude data of the odometer acquisition of left and right sidesing driving wheel, obtains vehicle center point
Move distance and change in location data；
(3) volume Kalman filtering is merged, GPS observation, the output of each motion model of parallel computation is added, comprising:
(31) the non-linear observational equation Z of volume Kalman filtering is establishedk=h (Xk,Vk), XkIt is the state vector of system, VkFor
Systematic observation noise sequence；
(32) by the error co-variance matrix P at k-1 momentj,k-1|k-1Chol is carried out to decompose to obtain lower triangular matrix Sk-1|k-1；
(33) admixture at k-1 moment is estimated2n cubature is determined according to spherical surface-radius rule
Point, according to cubature pointCubature point X is calculatedi,k-1|k-1And its corresponding weight wi, n is model shape
The dimension of state vector；
(34) by the cubature point Xi,k-1|k-1By the state equation of model j, the one-step prediction to do well is calculated
Cubature point value X* i,k|k-1, and calculate the one-step prediction value to do wellWith one-step prediction covariance matrix Pj,k|k-1；
(35) by the one-step prediction covariance matrix Pj,k|k-1Chol decomposition is carried out, lower triangular matrix S is obtainedk|k-1；
(36) the lower triangular matrix S is utilizedk|k-12n cubature point X is calculatedi,k|k-1And its corresponding weight wi；
(37) by the cubature point Xi,k|k-1By systematic observation equation, observation predicted value is found outAnd utilize 2n
A observation predicted value is estimated to calculate observation whole at this time
(38) the observation covariance matrix P at k moment is calculatedzz,k|k-1And state observation Cross-covariance Pxz,k|k-1；
(39) it measures and updates, calculate volume Kalman filtering gain Kj, state estimation of the computation model j at the k momentWith
And error co-variance matrix Pj,k|k；
(4) displacement data in left and right vehicle wheel driving wheel odometer is analyzed, judges motion state locating for current vehicle, and match
Corresponding motion model；Model probability is updated according to matched motion model；
(5) fusion output interaction, obtains k moment total state estimationP is estimated with total covariancek|k；Judge vehicle driving
Whether stop, if stopping, terminating to filter；Otherwise, return step (2).
2. the vehicle mounted positioning navigation method according to claim 1 based on adaptive odometer model, which is characterized in that step
Suddenly in (1), the geometrical-restriction relation are as follows:
ΔdA=(Δ dB+ΔdC)/2
γA=(- Δ dB+ΔdC)/L
Wherein, Δ dB、ΔdCWith Δ dAIt is the displacement of left and right two-wheeled odometer and vehicle center point within the sampling interval respectively, L is
The width of vehicle, γAIt is position and attitude deviation angle of the vehicle center within the sampling interval.
3. the vehicle mounted positioning navigation method according to claim 1 based on adaptive odometer model, which is characterized in that institute
It states in step (33), the specific determining method of cubature point is as follows:
Wherein, vector set P is made of the unit vector for being only 1,0, -1 in each dimension, and n is state vector dimension.
4. the vehicle mounted positioning navigation method according to claim 1 based on adaptive odometer model, which is characterized in that step
Suddenly in (34), the one-step prediction valueWith one-step prediction covariance matrix Pj,k|k-1The specific method is as follows:
Wherein, Γk|k-1It is the process noise driving battle array of system, Qk-1It is systematic procedure noise wk-1Variance matrix.
5. the vehicle mounted positioning navigation method according to claim 1 based on adaptive odometer model, which is characterized in that step
Suddenly in (38) (39), the observation predicted value isObservation covariance matrix is Pzz,k|k-1And state observation cross covariance
Matrix is Pxz,k|k-1The specific method is as follows:
Wherein, RkFor observation noise covariance matrix.
6. the vehicle mounted positioning navigation method according to claim 1 based on adaptive odometer model, which is characterized in that step
Suddenly in (5), total state estimationP is estimated with total covariancek|kThe specific method is as follows:
Wherein, μj(k) probability for being k moment motion model j.
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| CN112683268A (en) * | 2020-12-08 | 2021-04-20 | 中国铁建重工集团股份有限公司 | Roadway real-time positioning navigation method and system based on extended Kalman filtering |
| CN113504558A (en) * | 2021-07-14 | 2021-10-15 | 北京理工大学 | Ground unmanned vehicle positioning method considering road geometric constraint |
| CN113504558B (en) * | 2021-07-14 | 2024-02-27 | 北京理工大学 | Ground unmanned vehicle positioning method considering road geometric constraint |
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