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

Abstract

The invention discloses a vehicle-mounted navigation vehicle combined positioning device and a combined positioning method, comprising an MCU integrated navigation calculation module, a GNSS module, and a positioning output calculated by the MCU integrated navigation calculation module, and also includes a wheel wheel speed pulse counter, which is composed of a wheel speed pulse counter. The wheel speed pulse counter and the GNSS module are combined and input to the MCU integrated navigation calculation module. The positioning method includes reading the wheel speed pulse number to obtain the wheel speed pulse input; using the wheel speed pulse number to calculate the position recursively, judging whether to read in the GNSS data, and reading in the reliable GNSS data with the read in GNSS data as the condition; Positioning output and error correction and other steps. Reduce the cost of system use, improve the reliability and effectiveness of positioning, and can obtain the same effect as the original INS/GNSS combination, with better reliability, reduce INS modules, and effectively reduce costs.

Description

A vehicle navigation vehicle combined positioning device and its combined positioning method

technical field

The invention relates to a vehicle-mounted navigation and positioning device and a vehicle positioning method, in particular to a vehicle-mounted navigation vehicle combined positioning device and a combined positioning method combined with GNSS.

Background technique

The vehicle positioning system is an indispensable part of the current car's intelligence and networking. At present, the most common positioning methods are Inertia Navigation System (INS), Global Navigation Satellite System (GNSS) positioning, image or lidar, etc. These navigation devices complete positioning independently, but each has its own advantages and disadvantages, and the accuracy and cost are not the same. It is difficult to meet the requirements by a single navigation system, which is not only technically difficult, but also has different defects in practical applications, which cannot meet the system requirements. Generally, combined navigation is used, such as INS/GNSS combination, INS/GNSS/vehicle odometer combination, INS/GNSS/odometer/SLAM combination, etc. These navigation and positioning systems are used in all phases of intelligent driving. For vehicles, in addition to accuracy, the most important are reliability and cost requirements.

The current general vehicle integrated navigation calculation block diagram is to use the INS module 50, the GNSS module 20 and the odometer 60 to combine the MCU integrated navigation calculation module 10 to obtain the navigation and positioning output 40 (see Figure 3); where the INS is an inertial measurement navigation system, It is composed of a three-axis acceleration sensor and a three-axis gyroscope. Generally, a low-cost MEMS sensor and a corresponding data signal processing circuit are used. Three-axis acceleration and angular velocity, as well as the integrated position, velocity and attitude parameters. Due to the zero drift error of the inertial sensor, the integrated parameters have a large cumulative error over time. But it belongs to autonomous navigation, which is not affected by weather, and has strong anti-interference ability; while the GNSS module is a global navigation satellite system receiving module, usually GNSS includes: GPS, GLONASS, EU GALILEO and China Beidou satellite navigation system receiver module and high precision GNSS can effectively give positioning information such as position, speed, or heading; however, because these positioning information is obtained through satellite signals, there are easy to be blocked in tunnels, underground garages, trees and houses. Under the influence of the situation, the signal is lost, and the positioning information cannot be effectively obtained. The odometer is a device for measuring the speed of the vehicle itself, which is obtained after processing the wheel speed, calculated by ESP or ABS, and transmitted in the vehicle CAN network, so the odometer also belongs to the auxiliary INS here. After the combined navigation calculation, the final output can give information such as the position, speed and attitude of the vehicle. The system considers the most ideal navigation mode at present, and the advantages and disadvantages of each system are complemented to a certain extent. However, there is also a high cost of using the system, and the reliability will reduce the reliability of positioning accuracy due to the influence of a specific environment.

SUMMARY OF THE INVENTION

In order to solve the current situation of the existing vehicle integrated navigation system that the system has high use cost, and the reliability will be affected by a specific environment, the accuracy and reliability of navigation and positioning will be reduced. , Based on wheel speed pulse counter and GNSS module combined positioning device and combined positioning method of vehicle navigation vehicle.

The specific technical solution adopted by the present invention to solve the above-mentioned technical problems is as follows: a vehicle-mounted navigation vehicle integrated positioning device, comprising an MCU integrated navigation calculation module, a GNSS module and a positioning output obtained by calculation by the MCU integrated navigation calculation module, and is characterized in that: It also includes a wheel wheel speed pulse counter, which is combined and input to the MCU integrated navigation calculation module by the wheel wheel speed pulse counter and the GNSS module. Reduce the cost of system use, improve the reliability and effectiveness of positioning, and combine positioning based on wheel speed pulse counter and GNSS module. The same effect as the original INS/GNSS combination can be obtained, the reliability is better, the INS module is reduced, and the cost is effectively reduced.

Preferably, the wheel wheel speed pulse counter is a wheel wheel speed counter installed on the four wheels of the vehicle to measure the number of pulses in one revolution of the vehicle. Improve the reliable stability and accuracy of combined positioning output.

Another object of the present invention is to provide an on-board navigation vehicle combined positioning method, which is characterized in that: using the combined positioning device described in one of the above technical solutions, based on the wheel wheel speed pulse counter and the GNSS module combined navigation calculation module to calculate and obtain The positioning output of ; including the following combined positioning process

a Read the number of wheel speed pulses, and obtain the wheel speed pulse input; that is, read the increment of the number of wheel speed pulses of the left and right wheels behind the vehicle within a unit time, indicating the distance traveled by the vehicle per unit time, and the two wheels in front of the vehicle. Speed pulse counter, generally used as backup;

b Use the wheel speed pulse number to calculate the position of the vehicle recursively. The calculation formula is as follows:

The meaning of each symbol letter in the above calculation formula is:

The subscript k represents the current moment, and k-1 is the previous moment;

当前时刻纬度，

前一时刻纬度；

Latitude of the current time,

the latitude of the previous moment;

The longitude of the current moment of L _k , the longitude of the previous moment of L _k-1 ;

ψ _k is the heading angle at the current moment, that is, the angle with the north direction; ψ _k-1 is the heading angle at the previous moment;

Comparing the increase in the number of pulses of the left wheel speed at time N _lk k and time k-1;

Comparing the number of right wheel speed pulses at time N _rk k and time k-1;

α _l is the nominal pulse scale of the left wheel, the unit is: m/pulse;

α _r is the nominal pulse scale of the right wheel, the unit is: m/pulse;

_Re is the radius of the earth;

lw is the distance between the left and right wheels behind the vehicle.

c After step b, judge whether to read in GNSS data, take the read in GNSS data as a condition, read in reliable GNSS data; If read in GNSS data, then execute the next step;

d In step c, if the GNSS data is not read in, continue to read the number of wheel speed pulses from step a to execute the subsequent positioning process steps.

e After step c, obtain the positioning output;

f After step c, perform the next error calculation;

g for error correction;

After the h error is modified, continue to return to step a to re-read the number of wheel speed pulses and start the subsequent positioning process steps.

Since the inertial navigation system (TNS) is removed, the complexity of data sampling and algorithms is reduced, which can effectively reduce the cost, improve the overall positioning stability, and avoid problems such as inertial navigation gyro drift. Product reliability; based on the wheel speed pulse counters of the two wheels (usually select the wheel speed pulse counters of the left and right wheels behind the vehicle) and the GNSS combination for positioning calculation. GNSS modules include: GPS, GLONASS, EU GALILEO and China Beidou satellite navigation system receiver modules and high-precision time-to-time carrier-phase differential receiver system RTK, etc.

Preferably, the error calculation step includes the following steps

4-a System establishment steps;

The system error state equation is established as follows:

X _k =F _k-1 X _k-1 +w _k

in:

X _k is the state variable matrix,

为纬度误差；

is the latitude error;

ΔL _k is the longitude error;

Δα _lk is the left wheel pulse scale error, unit: m/pulse;

Δα _rk is the right wheel pulse scale error, unit: m/pulse;

F _k-1 is the state variable transition matrix, as follows:

here:

为前一时刻纬度；

is the latitude of the previous moment;

ψ _k is the heading angle at the current moment, that is, the angle with the north direction; ψ _k-1 is the heading angle at the previous moment;

N _lk is the increase in the number of left wheel speed pulses at time k and time k-1;

N _rk is the increase in the number of wheel speed pulses of the right wheel at time k and time k-1;

α _l is the nominal pulse scale of the left wheel, the unit is: m/pulse;

α _r is the nominal pulse scale of the right wheel, the unit is: m/pulse;

_Re is the radius of the earth;

lw is the distance between the left and right wheels behind the vehicle.

w _k is Gaussian white noise that obeys a normal distribution. It satisfies:

Mean E(w _k )=0

Covariance Cov( _wi , w _j )=Q _k δij

Q _k noise variance matrix

4-b Establish the measurement equation in the error calculation:

Z _k =H _k X _k +v _k

Where: Z _k is the observation matrix

为GNSS测得到的纬度；

is the latitude measured by GNSS;

L _kG is the longitude measured by GNSS;

为当前时刻轮速计算的纬度；

The latitude calculated for the wheel speed at the current moment;

L _k is the longitude calculated by the wheel speed at the current moment;

H _k is the transposed matrix,

v _k is the Gaussian white noise of the GNSS measured value, which is normally distributed

Mean: E(v _k )=0

Covariance: Cov(v _i , v _j )=Rδij

R measures the noise matrix.

_δij is the Kronecker function

i, j represent time i and j, respectively.

4-c According to the system state equation and measurement equation, implement the following error calculation steps

4-c-1 State variable prediction prediction.

The state variable prediction equation is described as follows:

X _k/k-1 =F _k-1 X _k-1

4-c-2 Covariance Prediction of State Variables

The prediction equation for covariance is described as follows:

4-c-3 Gain Calculation

The filter gain matrix is calculated as follows:

4-c-4 Optimal estimation of state variable parameters

The optimal estimation of the state variable parameters in this case, that is, the error calculation is as follows:

X _k =X _k/k-1 +K _k [Z _k -H _k X _k/k-1 ]

4-c-5 The state variable covariance estimation in this case is as follows:

P _k =[IK _k H _k ]P _k/k-1

The above five formula parameters are explained as follows:

The subscript k is the current moment, and k-1 is the previous moment of k;

The superscript T is the matrix transpose mark;

X _k/k-1 is to predict the state variable at time k according to the state variable at time k-1;

F _k-1 is the motion state matrix of the target entity;

X _k-1 is the state variable matrix at time k-1;

H _k is the transposed matrix;

P _k-1 is the covariance matrix of the state variable at time k-1, which is a 4X4 order matrix;

P _k/k-1 is the covariance matrix of the state variable at time k estimated according to the covariance matrix of the state variable at time k-1;

P _k is the covariance matrix of the state variable at time k, which is a 4X4 order matrix;

Q _k is the noise variance matrix;

R is the measurement noise matrix;

I is an identity matrix of order 4X4.

In this case, the indirect Kalman filter algorithm is used to estimate the tire calibration error at any time, improve the vehicle positioning accuracy, improve the error calculation accuracy, and improve the combined positioning accuracy.

Preferably, the error correction step is calculated as follows,

为修正后的k时刻纬度；

is the corrected latitude at time k;

为修正后的k时刻经度；

is the corrected longitude at time k;

为修正会后的左车轮脉冲刻度误差；

In order to correct the left wheel pulse scale error after the meeting;

为修正会后的左车轮脉冲刻度误差；

In order to correct the left wheel pulse scale error after the meeting;

The correction result is used as the initial value for the next step loop.

Improve the effectiveness of error correction and provide more accurate and effective initial data for further cyclic combined positioning calculations. The corrected output, as the final result, is the best estimate for the vehicle positioning, improving the positioning accuracy of the sensor, avoiding the shortcomings of single sensor positioning.

Preferably, the positioning output is to output the best position, such as longitude and latitude. Improve the accuracy of outputting the best position.

The beneficial effects of the present invention are: because the inertial navigation system (TNS) is removed, the cost can be effectively reduced, the overall positioning stability is also improved, the problems such as inertial navigation gyro drift are avoided, and the product reliability is also improved due to the reduction of components. ; Reduce the cost of system use, improve the reliability and effectiveness of positioning, and perform positioning combination based on wheel speed pulse counter and GNSS module. The same effect as the original INS/GNSS combination can be obtained, the reliability is better, the INS module is reduced, and the cost is effectively reduced. The wheel speed pulse tachometer and GNSS output from the four wheels on the vehicle are used to replace the combination of INS/GNSS/vehicle odometer. In today's vehicles, each wheel is equipped with a wheel speed pulse counter, which measures the wheel speed and turning angle. These pulse counters are highly accurate and reliable and are mainly used in vehicle ABS or ESP. This solution uses the combination of the left and right wheel speed pulse counters at the rear of the vehicle and GNSS to complete the integrated navigation task. The wheel speed pulse counters are four wheel speed counters installed on the vehicle, which measure the number of pulses in one revolution of the vehicle. Get the distance traveled by each wheel. Wheel speed pulse counters are installed on all four wheels of the vehicle, which have been used in ABS, ESP and other applications. In this case, the driving distance of the two wheels is obtained through the wheel speed pulse counters of the left and right wheels at the rear of the vehicle, and the vehicle position is obtained recursively. Then combine with GNSS. Get the same effect as the original INS/GNSS combination, with better reliability. Reduced INS modules, effectively reducing costs. Therefore, the four-wheel pulse counter has the redundancy of the left and right wheel speed pulse counters in front of the vehicle. Since the INS is removed, this scheme greatly reduces the cost.

Description of drawings:

The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

FIG. 1 is a schematic structural diagram of a vehicle-mounted navigation vehicle combined positioning device according to the present invention.

FIG. 2 is a schematic block diagram of a combined positioning calculation flow chart of the combined positioning method of the vehicle-mounted navigation vehicle of the present invention.

FIG. 3 is a schematic structural diagram of a vehicle-mounted navigation vehicle combined positioning device in the prior art.

Detailed ways

Example 1:

In the embodiment shown in FIG. 1, a vehicle-mounted navigation vehicle integrated positioning device includes an MCU integrated navigation calculation module 10, a GNSS module 20, a positioning output 40 obtained by calculation by the MCU integrated navigation calculation module, and also includes a wheel wheel speed pulse counter 30. The wheel speed pulse counter 30 and the GNSS module 20 are combined and input to the MCU integrated navigation calculation module 10.

The wheel wheel speed pulse counter 30 adopts a wheel wheel speed counter installed on the four wheels of the vehicle to measure the number of pulses in one revolution of the vehicle.

Example 2:

In the embodiment shown in FIG. 2 , a combined positioning method for an on-board navigation vehicle adopts the combined positioning device described in Embodiment 1, based on the positioning output calculated by the wheel wheel speed pulse counter and the GNSS module combined navigation calculation module; including the following Combined positioning process:

a Read the number of wheel speed pulses, and obtain the wheel speed pulse input 01; that is, read the increment of the number of wheel speed pulses of the left and right wheels behind the vehicle within a unit time, indicating the distance traveled by the vehicle per unit time, and the two wheels in front of the vehicle. Wheel speed pulse counter, generally used as a backup;

b Use the wheel speed pulse number to recursively calculate the position 02 of the vehicle, and the calculation formula is:

The meaning of each symbol letter in the above calculation formula is:

The subscript k represents the current moment, and k-1 is the previous moment;

当前时刻纬度，

前一时刻纬度；

Latitude of the current time,

the latitude of the previous moment;

The longitude of the current moment of L _k , the longitude of the previous moment of L _k-1 ;

ψ _k is the heading angle at the current moment, that is, the angle with the north direction; ψ _k-1 is the heading angle at the previous moment;

Comparing the increase in the number of pulses of the left wheel speed at time N _lk k and time k-1;

Comparing the number of right wheel speed pulses at time N _rk k and time k-1;

α _l is the nominal pulse scale of the left wheel, the unit is: m/pulse;

α _r is the nominal pulse scale of the right wheel, the unit is: m/pulse;

_Re is the radius of the earth;

lw is the distance between the left and right wheels behind the vehicle.

c, after step b, judge whether to read in GNSS data 03, take reading in GNSS data as a condition, read in reliable GNSS data; If read in GNSS data, then execute the next step;

d In step c, if the GNSS data is not read in, continue to read the number of wheel speed pulses from step a to execute the subsequent positioning process steps.

e After step c, obtain the positioning output 04, and the positioning output is the best output position, such as longitude and latitude;

f After step c, carry out the next error calculation 05;

g for error correction 06;

After the h error is modified, continue to return to step a to re-read the number of wheel speed pulses and start the subsequent positioning process steps.

The error calculation step includes the following steps:

4-a System establishment steps;

The system error state equation is established as follows:

X _k =F _k-1 X _k-1 +w _k

in:

X _k is the state variable matrix,

为纬度误差；

is the latitude error;

ΔL _k is the longitude error;

Δα _lk is the left wheel pulse scale error, unit: m/pulse;

Δα _rk is the right wheel pulse scale error, unit: m/pulse;

F _k-1 is the state variable transition matrix, as follows:

here:

为前一时刻纬度；

is the latitude of the previous moment;

ψ _k is the heading angle at the current moment, that is, the angle with the north direction; ψ _k-1 is the heading angle at the previous moment;

N _lk is the increase in the number of left wheel speed pulses at time k and time k-1;

N _rk is the increase in the number of right wheel speed pulses at time k and time k-1;

α _l is the nominal pulse scale of the left wheel, the unit is: m/pulse;

α _r is the nominal pulse scale of the right wheel, the unit is: m/pulse;

_Re is the radius of the earth;

lw is the distance between the left and right wheels behind the vehicle.

w _k is Gaussian white noise that obeys a normal distribution. It satisfies:

Mean E(w _k )=0

Covariance Cov( _wi , w _j )=Q _k δij

Q _k noise variance matrix

4-b Establish the measurement equation in the error calculation:

Z _k =H _k X _k +v _k

Where: Z _k is the observation matrix

为GNSS测得到的纬度；

is the latitude measured by GNSS;

L _kG is the longitude measured by GNSS;

为当前时刻轮速计算的纬度；

The latitude calculated for the wheel speed at the current moment;

L _k is the longitude calculated by the wheel speed at the current moment;

H _k is the transposed matrix,

v _k is the Gaussian white noise of the GNSS measured value, which is normally distributed

Mean: E(v _k )=0

Covariance: Cov(v _i , v _j )=Rδij

R measures the noise matrix.

δij is the Kronecker function

i, j represent time i and j, respectively.

4-c According to the system state equation and measurement equation, implement the following error calculation steps

4-c-1 State variable prediction prediction.

The state variable prediction equation is described as follows:

X _k/k-1 =F _k-1 X _k-1

4-c-2 Covariance Prediction of State Variables

The prediction equation for covariance is described as follows:

4-c-3 Gain Calculation

The filter gain matrix is calculated as follows:

4-c-4 Optimal estimation of state variable weight parameters

The optimal estimation of the state variable parameters in this case, that is, the error calculation is as follows:

X _k =X _k/k-1 +K _k [Z _k -H _k X _k/k-1 ]

4-c-5 The state variable covariance estimation in this case is as follows:

P _k =[IK _k H _k ]P _k/k-1

The above five formula parameters are explained as follows:

The subscript k is the current moment, and k-1 is the previous moment of k;

The superscript T is the matrix transpose mark;

X _k/k-1 is to predict the state variable at time k according to the state variable at time k-1;

F _k-1 is the motion state matrix of the target entity;

X _k-1 is the state variable matrix at time k-1;

H _k is the transposed matrix;

P _k-1 is the covariance matrix of the state variable at time k-1, which is a 4X4 order matrix;

P _k/k-1 is the covariance matrix of the state variable at time k estimated according to the covariance matrix of the state variable at time k-1;

P _k is the covariance matrix of the state variable at time k, which is a 4X4 order matrix;

Q _k is the noise variance matrix;

R is the measurement noise matrix;

I is an identity matrix of order 4X4.

The error correction step is calculated as follows:

为修正后的k时刻纬度；

is the corrected latitude at time k;

为修正后的k时刻经度；

is the corrected longitude at time k;

为修正会后的左车轮脉冲刻度误差；

In order to correct the left wheel pulse scale error after the meeting;

为修正会后的左车轮脉冲刻度误差；

In order to correct the left wheel pulse scale error after the meeting;

The correction result is used as the initial value for the next step loop.

The above content and structure describe the basic principles, main features and advantages of the present invention, which should be understood by those skilled in the art. What is described in the above examples and specification is only to illustrate the principle of the present invention, without departing from the spirit and scope of the present invention, the present invention will also have various changes and improvements, and these changes and improvements all belong to the scope of the claimed invention. Inside. The claimed scope of the present invention is defined by the appended claims and their equivalents.

Claims (3)

1. a vehicle-mounted navigation vehicle combination positioning method, is characterized in that: The following combined positioning device is used: including the MCU integrated navigation calculation module, the GNSS module, and the positioning output calculated by the MCU integrated navigation calculation module, as well as the wheel speed pulse counter, which is jointly input to the MCU by the wheel wheel speed pulse counter and the GNSS module. The integrated navigation calculation module; the wheel wheel speed pulse counter adopts the wheel wheel speed counter installed on the four wheels of the vehicle to measure the number of pulses in one revolution of the vehicle; The positioning output calculated based on the wheel speed pulse counter and the GNSS module combined navigation calculation module; including the following combined positioning process: a Read the number of wheel speed pulses, and obtain the wheel speed pulse input; that is, read the increment of the number of wheel speed pulses of the left and right wheels behind the vehicle within a unit time, indicating the distance traveled by the vehicle per unit time, and the two wheels in front of the vehicle. Speed pulse counter, generally used as backup; b Use the wheel speed pulse number to calculate the position of the vehicle recursively. The calculation formula is as follows:

The meanings of each symbol letter in the above calculation formula are: The subscript k represents the current moment, and k-1 is the previous moment;

current time latitude,

the latitude of the previous moment; The longitude of the current moment of L _k , the longitude of the previous moment of L _k-1 ; ψ _k is the heading angle at the current moment, that is, the angle with the north direction; ψ _k-1 is the heading angle at the previous moment; Comparing the increase in the number of pulses of the left wheel speed at time N _lk k and time k-1; Comparing the number of right wheel speed pulses at time N _rk k and time k-1; α _l is the nominal pulse scale of the left wheel, the unit is: m/pulse; α _r is the nominal pulse scale of the right wheel, the unit is: m/pulse; _Re is the radius of the earth; lw is the distance between the left and right wheels behind the vehicle; c After step b, judge whether to read in GNSS data, take the read in GNSS data as a condition, read in reliable GNSS data; If read in GNSS data, then execute the next step; d In step c, if the GNSS data is not read in, continue to read the wheel speed pulse number from step a and start the subsequent positioning process steps; e After step c, obtain the positioning output; f After step c, perform the next error calculation; g for error correction; After the h error is modified, continue to return to step a to re-read the number of wheel speed pulses and start the subsequent positioning process steps; The described error calculation step includes the following steps: 4-a System establishment steps; The system error state equation is established as follows: X _k =F _k-1 X _k-1 +W _k in: X _k is the state variable matrix,

is the latitude error; ΔL _k is the longitude error; Δα _lk is the left wheel pulse scale error, unit: m/pulse; Δα _rk is the right wheel pulse scale error, unit: m/pulse; F _k-1 is the state variable transition matrix, as follows:

here:

is the latitude of the previous moment; ψ _k is the heading angle at the current moment, that is, the included angle with the north direction; ψ _k-1 is the heading angle at the previous moment; N _lk is the increase in the number of left wheel speed pulses at time k and time k-1; N _rk is the increase in the number of wheel speed pulses of the right wheel at time k and time k-1; α _l is the nominal pulse scale of the left wheel, the unit is: m/pulse; α _r is the nominal pulse scale of the right wheel, the unit is: m/pulse; _Re is the radius of the earth; lw is the distance between the left and right wheels behind the vehicle; w _k is Gaussian white noise obeying a normal distribution; it satisfies: Mean E(w _k )=0 Covariance Cov( _wi , w _j )=Q _k δij Q _k noise variance matrix 4-b Establish the measurement equation in the error calculation: Z _k =H _k X _k +V _k Where: Z _k is the observation matrix

is the latitude measured by GNSS;

is the longitude measured by GNSS;

The latitude calculated for the wheel speed at the current moment; L _k is the longitude calculated by the wheel speed at the current moment; H _k is the transposed matrix,

v _k is the Gaussian white noise of the measured value of GNSS, which obeys the normal distribution mean: E(v _k )=0 Covariance: Cov(v _i , v _j )=Rδij R measurement noise matrix; δij is the Kronecker function

i, j represent time i and j, respectively; 4-c According to the system state equation and measurement equation, implement the following error calculation step 4-c-1 state variable prediction and prediction; The state variable prediction equation is described as follows: X _k/k-1 =F _k-1 X _k-1 4-c-2 Covariance Prediction of State Variables The prediction equation for covariance is described as follows:

4-c-3 Gain Calculation The filter gain matrix is calculated as follows:

4-c-4 Optimal estimation of state variable parameters The optimal estimation of the state variable parameters in this case, that is, the error calculation is as follows: X _k =X _k/k-1 +K _k [Z _k -H _k X _k/k-1 ] 4-c-5 The state variable covariance estimation in this case is as follows: P _k =[IK _k H _k ]P _k/k-1 The above five formula parameters are explained as follows: The subscript k is the current moment, and k-1 is the previous moment of k; The superscript T is the matrix transpose mark; X _k/k-1 is to predict the state variable at time k according to the state variable at time k-1; F _k-1 is the motion state matrix of the target entity; X _k-1 is the state variable matrix at time k-1; H _k is the transposed matrix; P _k-1 is the covariance matrix of the state variable at time k-1, which is a 4X4 order matrix; P _k/k-1 is the covariance matrix of the state variable at time k estimated according to the covariance matrix of the state variable at time k-1; P _k is the covariance matrix of the state variable at time k, which is a 4X4 order matrix; Q _k is the noise variance matrix; R is the measurement noise matrix; I is an identity matrix of order 4X4. 2. according to the vehicle-mounted navigation vehicle combination positioning method of claim 1, it is characterized in that: described error correction step is calculated as follows,

is the corrected latitude at time k;

is the corrected longitude at time k;

In order to correct the left wheel pulse scale error after the meeting;

In order to correct the left wheel pulse scale error after the meeting; The correction result is used as the initial value for the next step cycle. 3. The vehicle-mounted navigation vehicle combination positioning method according to claim 1, wherein the positioning output is an output optimal position, such as longitude and latitude.

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