CN109343095B - Vehicle-mounted navigation vehicle combined positioning device and combined positioning method thereof - Google Patents

Vehicle-mounted navigation vehicle combined positioning device and combined positioning method thereof Download PDF

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CN109343095B
CN109343095B CN201811363017.8A CN201811363017A CN109343095B CN 109343095 B CN109343095 B CN 109343095B CN 201811363017 A CN201811363017 A CN 201811363017A CN 109343095 B CN109343095 B CN 109343095B
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vehicle
wheel speed
moment
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CN109343095A (en
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周秀田
洪燕
马德仁
孔宏志
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Zotye New Energy Automobile 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

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Abstract

The invention discloses a vehicle-mounted navigation vehicle combined positioning device and a combined positioning method thereof. The positioning method comprises reading the wheel speed pulse number of the wheel to obtain the wheel speed pulse input; calculating the position by means of wheel speed pulse number recursion, judging whether GNSS data are read in, and reading in reliable GNSS data under the condition of reading in the GNSS data; obtaining positioning output and correcting errors. The use cost of the system is reduced, the positioning reliability and effectiveness are improved, the same effect as the original INS/GNSS combination can be obtained, the reliability is better, the INS modules are reduced, and the cost is effectively reduced.

Description

Vehicle-mounted navigation vehicle combined positioning device and combined positioning method thereof
Technical Field
The invention relates to a vehicle-mounted navigation positioning device and a vehicle positioning method, in particular to a vehicle-mounted navigation vehicle combined positioning device combined with a GNSS and a combined positioning method thereof.
Background
The vehicle positioning System is an indispensable part for automobile intellectualization and networking at present, most of the current positioning systems include an Inertial Navigation System (INS), a Global Navigation Satellite System (GNSS), positioning of images or laser radars and the like, and the Navigation devices are used for independently completing positioning, but the positioning systems have advantages and disadvantages, and the accuracy and the cost are not much the same. The requirement is difficult to meet by a single navigation system, the technical difficulty is high, different defects occur in practical application, and the system requirement cannot be met. Combined navigation is typically employed, such as an INS/GNSS combination, an INS/GNSS/vehicle odometer combination, an INS/GNSS/odometer/SLAM combination, and the like. These navigational positioning systems are used to intelligently drive various phases. For vehicles, in addition to accuracy, reliability and cost requirements are paramount.
The current general vehicle-mounted integrated navigation computation block diagram is that the INS module 50, the GNSS module 20 and the odometer 60 are jointly combined to perform the MCU integrated navigation computation module 10 to obtain the navigation positioning output 40 (see FIG. 3); the INS is an inertial measurement navigation system, which is composed of a triaxial acceleration sensor and a triaxial gyroscope, and generally employs a low-cost MEMS sensor and a corresponding data signal processing circuit. Parameters such as triaxial acceleration, angular velocity, position, velocity and attitude after integration can be used, and the accumulated error of the parameters obtained by integration along with time is very large due to zero drift error of the inertial sensor. But the navigation system belongs to autonomous navigation, is not influenced by weather shielding and the like, and has strong anti-interference capability; the GNSS module is a GNSS receiver module, and generally includes: GPS, GLONASS, EU GALILEO and China Beidou satellite navigation system receiver modules, a high-precision real-time carrier phase difference tapping receiver system RTK and the like; the GNSS can effectively give positioning information such as position, speed or course; however, since the positioning information is obtained through satellite signals, there is a defect that the positioning information cannot be effectively obtained because signals are easily lost under the influence of tunnels, underground garages and occasions shielded by trees and houses. The odometer is a device for measuring the speed of the vehicle, is obtained by processing the wheel speed, is obtained by calculating the ESP or ABS and is transmitted in a CAN network of the vehicle, so the odometer also belongs to an auxiliary INS. And finally outputting information such as the position, the speed, the attitude and the like of the vehicle after the integrated navigation calculation. The system considers the optimal navigation mode at present, and the advantages and disadvantages of the systems are complemented to a certain extent. However, the system is expensive to use, and the reliability is affected by the specific environment, which reduces the reliability of the positioning accuracy.
Disclosure of Invention
The invention provides a vehicle-mounted navigation vehicle combined positioning device based on a wheel speed pulse counter and a GNSS module and a combined positioning method thereof, which can reduce the use cost of the system and improve the positioning reliability and effectiveness in order to solve the current situations that the existing vehicle-mounted navigation system has high use cost of the system and the reliability can reduce the accurate reliability of navigation positioning due to the influence of a specific environment.
The invention adopts the following specific technical scheme for solving the technical problems: the utility model provides a vehicle navigation vehicle combination positioner, includes MCU combination navigation computational module, GNSS module and the location output who obtains through MCU combination navigation computational module calculation which characterized in that: the device also comprises a wheel speed pulse counter, and the wheel speed pulse counter and the GNSS module are jointly combined and input to the MCU combined navigation calculation module. The use cost of the system is reduced, the positioning reliability and effectiveness are improved, and the positioning combination is carried out based on the wheel speed pulse counter and the GNSS module. The method has the same effect as the original INS/GNSS combination, has better reliability, reduces the INS modules and effectively reduces the cost.
Preferably, the wheel speed pulse counter is a wheel speed counter which is arranged on four wheels of the vehicle and is used for measuring the number of pulses of one rotation of the vehicle. The reliability, stability and accuracy of combined positioning output are improved.
Another object of the present invention is to provide a method for positioning a vehicle-mounted navigation vehicle in a combined manner, which is characterized in that: by adopting the combined positioning device in one of the technical schemes, the positioning output is calculated and obtained by a GNSS module combined navigation calculation module based on a wheel speed pulse counter; comprises the following combined positioning process
a, reading the wheel speed pulse number of a wheel to obtain wheel speed pulse input; namely, the pulse number increment of the left wheel speed and the right wheel speed of the vehicle is read in unit time to represent the running distance of the vehicle in unit time, and the pulse counters of the two wheel speeds in front of the vehicle are generally used as backups;
b, calculating the position of the vehicle by using wheel speed pulse recursion, wherein the calculation formula is as follows:
Figure BDA0001867337600000031
Figure BDA0001867337600000032
Figure BDA0001867337600000033
the meaning of each symbol letter in the above calculation formula is respectively:
subscript k represents the current time, k-1 is the previous time;
Figure BDA0001867337600000034
the latitude at the present time is,
Figure BDA0001867337600000035
latitude at the previous moment;
Lklongitude at the current time, Lk-1Longitude from the previous time;
ψkthe current moment course angle is the included angle with the north direction; psik-1A course angle at a previous time;
Nlkincreasing and comparing the pulse number of the left wheel at the time k and the time k-1;
Nrkincreasing the number of right wheel speed pulses at the time k and the time k-1;
αlis the nominal pulse scale of the left wheel, and the unit is: rice/pulse;
αrnominal pulse scale for the right wheel, in units of: rice/pulse;
Reis the radius of the earth;
lw is the distance between the left wheel and the right wheel behind the vehicle.
c, after the step b, judging whether to read in GNSS data, and reading in reliable GNSS data under the condition of reading in the GNSS data; if the GNSS data is read in, executing the next step;
d, during the step c, if the GNSS data is not read, continuing to read the wheel speed pulse number from the step a and executing the subsequent positioning flow process.
e after step c, obtaining a positioning output;
f, after the step c, carrying out next error calculation;
g, correcting errors;
and h, after the error is modified, continuing to return to the step a to read the wheel speed pulse number again to start to execute the subsequent positioning process step.
Because the inertial navigation system (TNS) is removed, the complexity of data sampling and algorithm is reduced, the cost can be effectively reduced, the overall positioning stability is improved, the problems of inertial navigation gyro drift and the like are avoided, and the product reliability is improved due to the reduction of devices; the positioning calculation is performed based on a combination of wheel speed pulse counters of two wheels (generally, wheel speed pulse counters of two wheels on the left and right behind the vehicle are selected) and GNSS. The GNSS module comprises: GPS, GLONASS, EU GALILEO and China Beidou satellite navigation system receiver modules, a high-precision real-time carrier phase difference receiver system RTK and the like.
Preferably, the error calculating step includes the steps of
4-a system establishing step;
the system error state equation is established as follows:
Xk＝Fk-1Xk-1+wk
wherein:
Xkin the form of a matrix of state variables,
Figure BDA0001867337600000051
Figure BDA0001867337600000052
is latitude error;
ΔLkis the longitude error;
Δαlkleft wheel pulse scale error, unit: rice/pulse;
Δαrkright wheel pulse scale error, unit: rice/pulse;
Fk-1for the state variable transition matrix, the following:
Figure BDA0001867337600000053
here:
Figure BDA0001867337600000054
Figure BDA0001867337600000055
Figure BDA0001867337600000056
Figure BDA0001867337600000057
Figure BDA0001867337600000058
the latitude at the previous moment;
ψkthe current time course angle is the included angle between the current time course angle and the north direction; psik-1A course angle at a previous time;
Nlkincreasing the pulse number of the left wheel at the time k and the time k-1;
Nrkthe number of right wheel speed pulses is increased by the amount of comparison between the k moment and the k-1 moment;
αlis the nominal pulse scale of the left wheel, and the unit is: rice/pulse;
αrnominal pulse scale for the right wheel, in units of: rice/pulse;
Reis the radius of the earth;
lw is the distance between the left wheel and the right wheel behind the vehicle.
wkIs gaussian white noise that follows a normal distribution. Which satisfies the following conditions:
mean value E (w)k)＝0
Covariance Cov (w)i，wj)＝Qkij
QkNoise variance matrix
4-b, establishing a measurement equation in error calculation:
Zk＝HkXk+vk
wherein: zkFor observing matrix
Figure BDA0001867337600000061
Figure BDA0001867337600000062
Measuring the obtained latitude for the GNSS;
LkGlongitude measured for GNSS;
Figure BDA0001867337600000063
latitude calculated for the wheel speed at the current moment;
Lklongitude calculated for wheel speed at the current time;
Hkin order to be a transposed matrix,
Figure BDA0001867337600000064
vkis Gaussian white noise of GNSS measured values and follows normal distribution
Mean value: e (v)k)＝0
Covariance: cov (v)i，vj)＝Rij
R measures the noise matrix.
ijIs a function of kronecker
Figure BDA0001867337600000071
i, j represent the i and j times, respectively.
4-c according to the system state equation and the measurement equation, the following error calculation step is implemented
4-c-1 state variable prediction.
The state variable prediction equation is described as follows:
Xk/k-1＝Fk-1Xk-1
covariance prediction of 4-c-2 state variables
The prediction equation for covariance is described as follows:
Figure BDA0001867337600000072
4-c-3 gain calculation
The filter gain matrix is calculated as follows:
Figure BDA0001867337600000073
4-c-4 state variable parameter optimal estimation
The state variable parameter of the scheme is optimally estimated, namely the error is calculated as follows:
Xk＝Xk/k-1+Kk[Zk-HkXk/k-1]
4-c-5 the state variable covariance estimation of the scheme is as follows:
Pk＝[I-KkHk]Pk/k-1
the above 5 formula parameters are explained as follows:
subscript k is the current time, and k-1 is the previous time of k;
the superscript T is a matrix transposition mark;
Xk/k-1predicting a state variable at the k moment according to the state variable at the k-1 moment;
Fk-1a target entity motion state matrix is obtained;
Xk-1is a state variable matrix at the moment of k-1;
Hkis a transposed matrix;
Pk-1the covariance matrix of the state variable at the moment of k-1 is a 4X4 order matrix;
Pk/k-1the covariance matrix of the state variable at the k moment is estimated according to the covariance matrix of the state variable at the k-1 moment;
Pkthe covariance matrix of the state variable at the moment k is a 4X4 order matrix;
Qkis a noise variance matrix;
r is a measurement noise matrix;
i is an identity matrix of order 4X 4.
The device adopts an indirect Kalman filtering algorithm, estimates the tire calibration error at any time, improves the vehicle positioning precision, improves the error calculation precision and improves the combined positioning accuracy.
Preferably, the error correction step is calculated as follows,
Figure BDA0001867337600000081
Figure BDA0001867337600000082
the corrected k moment latitude;
Figure BDA0001867337600000083
is corrected longitude at time k;
Figure BDA0001867337600000084
to correct the pulse scale error of the left wheel after meeting;
Figure BDA0001867337600000091
to correct the pulse scale error of the left wheel after meeting;
the corrected result is used as an initial value of the subsequent step cycle.
The error correction effectiveness is improved, and more accurate and effective initial data are provided for further circular combined positioning calculation. And outputting the corrected result, and as a final result, optimally estimating the vehicle positioning, improving the positioning accuracy of the sensor and avoiding the defect of single sensor positioning.
Preferably, the positioning output is an output of an optimal position, such as longitude and latitude. The accuracy of outputting the optimal position is improved.
The invention has the beneficial effects that: because the inertial navigation system (TNS) is removed, the cost can be effectively reduced, the integral positioning stability is improved, the problems of inertial navigation gyro drift and the like are avoided, and the product reliability is improved because devices are reduced; the use cost of the system is reduced, the positioning reliability and effectiveness are improved, and the positioning combination is carried out based on the wheel speed pulse counter and the GNSS module. The method has the same effect as the original INS/GNSS combination, has better reliability, reduces the INS modules and effectively reduces the cost. The wheel speed pulse speed counter and GNSS combination of four wheel outputs of the vehicle is adopted to replace the combination of INS/GNSS/vehicle odometer. In present vehicles, each wheel is equipped with a wheel speed pulse counter for measuring the wheel speed and the angle of rotation. These pulse counters have high accuracy and reliability and are mainly used for ABS or ESP of vehicles. According to the scheme, a combined navigation task is completed by combining a left wheel speed pulse counter, a right wheel speed pulse counter and a GNSS (global navigation satellite system), wherein the wheel speed pulse counters are four wheel speed counters which are arranged on a vehicle, and the number of pulses of one circle of rotation of the vehicle is measured. The distance traveled by each wheel is obtained. The wheel speed pulse counters of the left wheel and the right wheel behind the vehicle are used for obtaining the running distance of the two wheels, the position of the vehicle is obtained through recursion, and then the vehicle is combined with GNSS. The effect same as that of the original INS/GNSS combination is obtained, and the reliability is better. And the INS modules are reduced, and the cost is effectively reduced. Therefore, the four-wheel pulse counter has redundancy of the left and right wheel speed pulse counters at the front side of the vehicle. Because the INS is removed, the scheme greatly reduces the cost.
Description of the drawings:
the invention is described in further detail below with reference to the figures and the detailed description.
FIG. 1 is a schematic structural diagram of an integrated positioning device for a vehicle-mounted navigation vehicle according to the present invention.
FIG. 2 is a block diagram of the combined positioning calculation process of the integrated positioning method for vehicle-mounted navigation vehicles according to the present invention.
FIG. 3 is a schematic structural diagram of a combined positioning device for a vehicle-mounted navigation vehicle in the prior art.
Detailed Description
Example 1:
in the embodiment shown in fig. 1, an integrated positioning device for a vehicle-mounted navigation vehicle includes an MCU integrated navigation computing module 10, a GNSS module 20, and a positioning output 40 computed by the MCU integrated navigation computing module, and further includes a wheel speed pulse counter 30, which is jointly input to the MCU integrated navigation computing module 10 by the wheel speed pulse counter 30 and the GNSS module 20.
The wheel speed pulse counter 30 is a wheel speed counter mounted on four wheels of the vehicle to measure the number of pulses of one rotation of the vehicle.
Example 2:
in the embodiment shown in fig. 2, a vehicle-mounted navigation vehicle combined positioning method adopts the combined positioning device described in embodiment 1, and based on a wheel speed pulse counter and a positioning output calculated by a GNSS module combined navigation computation module; the method comprises the following combined positioning process:
a, reading the wheel speed pulse number of a wheel to obtain wheel speed pulse input 01; namely, the pulse number increment of the left wheel speed and the right wheel speed of the vehicle is read in unit time to represent the running distance of the vehicle in unit time, and the pulse counters of the two wheel speeds in front of the vehicle are generally used as backups;
b, calculating the position 02 of the vehicle by using wheel speed pulse recursion by adopting a calculation formula as follows:
Figure BDA0001867337600000111
Figure BDA0001867337600000112
Figure BDA0001867337600000113
the meaning of each symbol letter in the above calculation formula is respectively:
subscript k represents the current time, k-1 is the previous time;
Figure BDA0001867337600000114
the latitude at the present time is,
Figure BDA0001867337600000115
latitude at the previous moment;
Lklongitude at the current time, Lk-1Longitude from the previous time;
ψkthe current moment course angle is the included angle with the north direction; psik-1A course angle at a previous time;
Nlkincreasing and comparing the pulse number of the left wheel at the time k and the time k-1;
Nrkincreasing the number of right wheel speed pulses at the time k and the time k-1;
αlis the nominal pulse scale of the left wheel, and the unit is: rice/pulse;
αrnominal pulse scale for the right wheel, in units of: rice/pulse;
Reis the radius of the earth;
lw is the distance between the left wheel and the right wheel behind the vehicle.
c, after the step b, judging whether to read in GNSS data 03, and reading in reliable GNSS data under the condition of reading in GNSS data; if the GNSS data is read in, executing the next step;
d, during the step c, if the GNSS data is not read, continuing to read the wheel speed pulse number from the step a and executing the subsequent positioning flow process.
e after step c, obtaining a positioning output 04, the positioning output being an output optimal position, such as longitude and latitude;
f after step c, performing the next error calculation 05;
g, correcting errors 06;
and h, after the error is modified, continuing to return to the step a to read the wheel speed pulse number again to start to execute the subsequent positioning process step.
The error calculating step includes the steps of:
4-a system establishing step;
the system error state equation is established as follows:
Xk＝Fk-1Xk-1+wk
wherein:
Xkin the form of a matrix of state variables,
Figure BDA0001867337600000121
Figure BDA0001867337600000122
is latitude error;
ΔLkis the longitude error;
Δαlkleft wheel pulse scale error, unit: rice/pulse;
Δαrkright wheel pulse scale error, unit: rice/pulse;
Fk-1for the state variable transition matrix, the following:
Figure BDA0001867337600000123
here:
Figure BDA0001867337600000131
Figure BDA0001867337600000132
Figure BDA0001867337600000133
Figure BDA0001867337600000134
Figure BDA0001867337600000135
the latitude at the previous moment;
ψkthe current time course angle is the included angle between the current time course angle and the north direction; psik-1A course angle at a previous time;
Nlkincreasing the pulse number of the left wheel at the time k and the time k-1;
Nrkthe number of right wheel speed pulses is increased by the amount of comparison between the k moment and the k-1 moment;
αlis the nominal pulse scale of the left wheel, and the unit is: rice/pulse;
αrnominal pulse scale for the right wheel, in units of: rice/pulse;
Reis the radius of the earth;
lw is the distance between the left wheel and the right wheel behind the vehicle.
wkIs gaussian white noise that follows a normal distribution. Which satisfies the following conditions:
mean value E (w)k)＝0
Covariance Cov (w)i，wj)＝Qkij
QkNoise variance matrix
4-b, establishing a measurement equation in error calculation:
Zk＝HkXk+vk
wherein: zkFor observing matrix
Figure BDA0001867337600000141
Figure BDA0001867337600000142
Measuring the obtained latitude for the GNSS;
LkGlongitude measured for GNSS;
Figure BDA0001867337600000143
latitude calculated for the wheel speed at the current moment;
Lklongitude calculated for wheel speed at the current time;
Hkin order to be a transposed matrix,
Figure BDA0001867337600000144
vkis Gaussian white noise of GNSS measured values and follows normal distribution
Mean value: e (v)k)＝0
Covariance: cov (v)i，vj)＝Rij
R measures the noise matrix.
ij is the kronecker function
Figure BDA0001867337600000145
i, j represent the i and j times, respectively.
4-c according to the system state equation and the measurement equation, the following error calculation step is implemented
4-c-1 state variable prediction.
The state variable prediction equation is described as follows:
Xk/k-1＝Fk-1Xk-1
covariance prediction of 4-c-2 state variables
The prediction equation for covariance is described as follows:
Figure BDA0001867337600000146
4-c-3 gain calculation
The filter gain matrix is calculated as follows:
Figure BDA0001867337600000151
4-c-4 state weight change parameter optimal estimation
The state variable parameter of the scheme is optimally estimated, namely the error is calculated as follows:
Xk＝Xk/k-1+Kk[Zk-HkXk/k-1]
4-c-5 the state variable covariance estimation of the scheme is as follows:
Pk＝[I-KkHk]Pk/k-1
the above 5 formula parameters are explained as follows:
subscript k is the current time, and k-1 is the previous time of k;
the superscript T is a matrix transposition mark;
Xk/k-1predicting a state variable at the k moment according to the state variable at the k-1 moment;
Fk-1a target entity motion state matrix is obtained;
Xk-1is a state variable matrix at the moment of k-1;
Hkis a transposed matrix;
Pk-1the covariance matrix of the state variable at the moment of k-1 is a 4X4 order matrix;
Pk/k-1the covariance matrix of the state variable at the k moment is estimated according to the covariance matrix of the state variable at the k-1 moment;
Pkthe covariance matrix of the state variable at the moment k is a 4X4 order matrix;
Qkis a noise variance matrix;
r is a measurement noise matrix;
i is an identity matrix of order 4X 4.
The error correction procedure is calculated as follows:
Figure BDA0001867337600000161
Figure BDA0001867337600000162
the corrected k moment latitude;
Figure BDA0001867337600000163
is corrected longitude at time k;
Figure BDA0001867337600000164
to correct the pulse scale error of the left wheel after meeting;
Figure BDA0001867337600000165
to correct the pulse scale error of the left wheel after meeting;
the corrected result is used as an initial value of the subsequent step cycle.
The foregoing summary and structure are provided to explain the principles, general features, and advantages of the product and to enable others skilled in the art to understand the invention. The foregoing examples and description have been presented to illustrate the principles of the invention and are intended to provide various changes and modifications within the spirit and scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.

Claims (3)

1. A vehicle-mounted navigation vehicle combined positioning method is characterized in that:
the following combined positioning device is adopted: the device comprises an MCU combined navigation computing module, a GNSS module, a positioning output obtained by the calculation of the MCU combined navigation computing module, a wheel speed pulse counter, a control module and a control module, wherein the wheel speed pulse counter and the GNSS module are jointly combined and input to the MCU combined navigation computing module; the wheel speed pulse counter adopts a wheel speed counter which is arranged on four wheels of the vehicle and is used for measuring the number of pulses of one circle of rotation of the vehicle;
positioning output calculated by a combined navigation calculation module based on a wheel speed pulse counter and a GNSS module; the method comprises the following combined positioning process:
a, reading the wheel speed pulse number of a wheel to obtain wheel speed pulse input; namely, the pulse number increment of the left wheel speed and the right wheel speed of the vehicle is read in unit time to represent the running distance of the vehicle in unit time, and the pulse counters of the two wheel speeds in front of the vehicle are generally used as backups;
b, calculating the position of the vehicle by using wheel speed pulse recursion, wherein the calculation formula is as follows:
Figure FDA0002514718100000011
Figure FDA0002514718100000012
Figure FDA0002514718100000013
the meaning of each symbol letter in the above calculation formula is respectively:
subscript k represents the current time, k-1 is the previous time;
Figure FDA0002514718100000014
the latitude at the present time is,
Figure FDA0002514718100000015
latitude at the previous moment;
Lklongitude at the current time, Lk-1Longitude from the previous time;
ψkthe current moment course angle is the included angle with the north direction; psik-1A course angle at a previous time;
Nlkincreasing and comparing the pulse number of the left wheel at the time k and the time k-1;
Nrkincreasing the number of right wheel speed pulses at the time k and the time k-1;
αlthe unit is the nominal pulse scale of the left wheel, namely meter/pulse;
αrthe unit is the nominal pulse scale of the right wheel and is meter/pulse;
Reis the radius of the earth;
lw is the distance between the left wheel and the right wheel behind the vehicle;
c, after the step b, judging whether to read in GNSS data, and reading in reliable GNSS data under the condition of reading in the GNSS data; if the GNSS data is read in, executing the next step;
d, during the step c, if the GNSS data is not read, continuing to read the wheel speed pulse number of the wheel from the step a and starting to execute the subsequent positioning flow step;
e after step c, obtaining a positioning output;
f, after the step c, carrying out next error calculation;
g, correcting errors;
h, after the error is modified, continuing to return to the step a to read the wheel speed pulse number again and starting to execute the subsequent positioning process step;
the error calculating step comprises the following steps:
4-a system establishing step;
the system error state equation is established as follows:
Xk＝Fk-1Xk-1+Wk
wherein:
Xkin the form of a matrix of state variables,
Figure FDA0002514718100000021
Figure FDA0002514718100000031
is latitude error;
ΔLkis the longitude error;
Δαlkthe unit is meter/pulse, which is the pulse scale error of the left wheel;
Δαrkthe unit is meter/pulse, which is the pulse scale error of the right wheel;
Fk-1for the state variable transition matrix, the following:
Figure FDA0002514718100000032
here:
Figure FDA0002514718100000033
Figure FDA0002514718100000034
Figure FDA0002514718100000035
Figure FDA0002514718100000036
Figure FDA0002514718100000037
the latitude at the previous moment;
ψkthe current time course angle is the included angle between the current time course angle and the north direction; psik-1A course angle at a previous time;
Nlkincreasing the pulse number of the left wheel at the time k and the time k-1;
Nrkthe number of right wheel speed pulses is increased by the amount of comparison between the k moment and the k-1 moment;
αlthe unit is the nominal pulse scale of the left wheel, namely meter/pulse;
αrthe unit is the nominal pulse scale of the right wheel and is meter/pulse;
Reis the radius of the earth;
lw is the distance between the left wheel and the right wheel behind the vehicle;
wkis gaussian white noise that follows a normal distribution; which satisfies the following conditions:
mean value E (w)k)＝0
Covariance Cov (w)i，wj)＝Qkij
QkNoise variance matrix
4-b, establishing a measurement equation in error calculation:
Zk＝HkXk+Vk
wherein Z iskFor observing matrix
Figure FDA0002514718100000041
Figure FDA0002514718100000042
Measuring the obtained latitude for the GNSS;
Figure FDA0002514718100000043
longitude measured for GNSS;
Figure FDA0002514718100000044
latitude calculated for the wheel speed at the current moment;
Lklongitude calculated for wheel speed at the current time;
Hkin order to be a transposed matrix,
Figure FDA0002514718100000045
vkis gaussian white noise of GNSS measured values, obeying normal distribution mean: e (v)k)＝0
Covariance: cov (v)i，vj)＝Rij
R measures a noise matrix;
ij is the kronecker function
Figure FDA0002514718100000051
i and j represent the time of i and j respectively;
4-c, according to a system state equation and a measurement equation, implementing the following error calculation step 4-c-1 state variable prediction;
the state variable prediction equation is described as follows:
Xk/k-1＝Fk-1Xk-1
covariance prediction of 4-c-2 state variables
The prediction equation for covariance is described as follows:
Figure FDA0002514718100000052
4-c-3 gain calculation
The filter gain matrix is calculated as follows:
Figure FDA0002514718100000053
4-c-4 state variable parameter optimal estimation
The state variable parameter of the scheme is optimally estimated, namely the error is calculated as follows:
Xk＝Xk/k-1+Kk[Zk-HkXk/k-1]
4-c-5 the state variable covariance estimation of the scheme is as follows:
Pk＝[I-KkHk]Pk/k-1
the above 5 formula parameters are explained as follows:
subscript k is the current time, and k-1 is the previous time of k;
the superscript T is a matrix transposition mark;
Xk/k-1predicting a state variable at the k moment according to the state variable at the k-1 moment;
Fk-1a target entity motion state matrix is obtained;
Xk-1is a state variable matrix at the moment of k-1;
Hkis a transposed matrix;
Pk-1the covariance matrix of the state variable at the moment of k-1 is a 4X4 order matrix;
Pk/k-1the covariance matrix of the state variable at the k moment is estimated according to the covariance matrix of the state variable at the k-1 moment;
Pkthe covariance matrix of the state variable at the moment k is a 4X4 order matrix;
Qkis a noise variance matrix;
r is a measurement noise matrix;
i is an identity matrix of order 4X 4.
2. The integrated positioning method for vehicle-mounted navigation vehicles according to claim 1, characterized in that: the error correction procedure described is calculated as follows,
Figure FDA0002514718100000061
Figure FDA0002514718100000062
the corrected k moment latitude;
Figure FDA0002514718100000063
is corrected longitude at time k;
Figure FDA0002514718100000064
to correct the pulse scale error of the left wheel after meeting;
Figure FDA0002514718100000065
to correct the pulse scale error of the left wheel after meeting;
the corrected result is used as an initial value of the subsequent step cycle.
3. The integrated positioning method for vehicle-mounted navigation vehicles according to claim 1, characterized in that: the positioning output is the output of the best position, such as longitude and latitude.
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CN110095793B (en) * 2019-04-10 2021-11-09 同济大学 Automatic driving low-speed sweeper positioning method based on tire radius self-adaption
CN110133694B (en) * 2019-04-18 2023-11-03 同济大学 Vehicle positioning method and system based on dual-antenna GNSS heading and wheel speed assistance
CN112097758A (en) * 2019-06-18 2020-12-18 阿里巴巴集团控股有限公司 Positioning method and device, robot positioning method and robot
CN110307836B (en) * 2019-07-10 2021-05-07 北京智行者科技有限公司 Accurate positioning method for welt cleaning of unmanned cleaning vehicle
CN110926483B (en) * 2019-11-25 2020-12-25 奥特酷智能科技(南京)有限公司 Low-cost sensor combination positioning system and method for automatic driving
CN111380516B (en) * 2020-02-27 2022-04-08 上海交通大学 Inertial navigation/odometer vehicle combined navigation method and system based on odometer measurement information
CN111504309B (en) * 2020-04-28 2021-09-10 东风汽车集团有限公司 Method for calculating pose of automobile in low-speed motion
CN112577516B (en) * 2020-11-11 2022-07-08 上汽大众汽车有限公司 Method and system for identifying and compensating wheel speed error of vehicle
CN113376670A (en) * 2021-04-26 2021-09-10 安徽域驰智能科技有限公司 Vehicle self-positioning online calibration method

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102928816B (en) * 2012-11-07 2014-03-12 东南大学 High-reliably integrated positioning method for vehicles in tunnel environment
CN207832202U (en) * 2017-12-20 2018-09-07 上海北寻信息科技有限公司 A kind of low cost integrated navigation system
CN108508471A (en) * 2018-06-05 2018-09-07 广东纵行科技有限公司 A kind of automatic driving vehicle localization method and device

Family Cites Families (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1235017C (en) * 2002-10-08 2006-01-04 曲声波 Vehicle position detection apparatus and treatment method
JP4630327B2 (en) * 2007-05-03 2011-02-09 日本ビクター株式会社 Navigation device
US20110257882A1 (en) * 2010-04-15 2011-10-20 Mcburney Paul W Road map feedback server for tightly coupled gps and dead reckoning vehicle navigation

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102928816B (en) * 2012-11-07 2014-03-12 东南大学 High-reliably integrated positioning method for vehicles in tunnel environment
CN207832202U (en) * 2017-12-20 2018-09-07 上海北寻信息科技有限公司 A kind of low cost integrated navigation system
CN108508471A (en) * 2018-06-05 2018-09-07 广东纵行科技有限公司 A kind of automatic driving vehicle localization method and device

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