CN115790645A - Wheel speed meter error online estimation and compensation method for vehicle-mounted integrated navigation system - Google Patents
Wheel speed meter error online estimation and compensation method for vehicle-mounted integrated navigation system Download PDFInfo
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Abstract
The application discloses an on-line error estimation and compensation method for a wheel speed meter of a vehicle-mounted integrated navigation system, which designs a double-loop updating mode of the wheel speed meter on the premise of not changing the original integrated navigation algorithm architecture, and a low-dimensional filter is used for estimating the error of the wheel speed meter on line in the error estimation loop, and the output of the wheel speed meter is compensated by combining the error estimation result in the filtering loop, so that the combined navigation precision of the inertial navigation/wheel speed meter is improved. The method realizes the on-line estimation of the error of the wheel speed meter based on the double-loop updating mode of the wheel speed meter and the dimensionality reduction filter, does not influence the original combined navigation algorithm framework, has small calculated amount and high estimation precision, can improve the combined navigation positioning performance under the condition that the GNSS signal is shielded after error estimation and compensation, and embodies higher engineering application value.
Description
Technical Field
The application belongs to the technical field of vehicle-mounted integrated navigation systems, in particular to an on-line error estimation and compensation method for a wheel speed meter of a vehicle-mounted integrated navigation system.
Background
The integrated navigation system is an indispensable module in vehicle-assisted driving and automatic driving, and the system is responsible for providing real-time and high-precision position, speed and attitude information for the carrier. The vehicle-mounted integrated Navigation System mainly comprises 3 modules, namely an inertial Navigation module (inertial Navigation System), a Satellite Navigation module (Global Navigation Satellite System) and a wheel speed meter module of a vehicle. The vehicle-mounted inertial navigation generally uses a Micro-Mechanical System (MEMS), which has a small volume, low cost and a high data update rate, and does not rely on external information, but because the accuracy of an internal gyroscope and an accelerometer is low, the internal gyroscope and the accelerometer cannot work independently for a long time, and therefore, data fusion needs to be performed on the inertial navigation and the GNSS module, so as to ensure high-accuracy navigation performance. However, under the working condition of urban roads, the GNSS signals are easily interfered or shielded, so that the GNSS positioning quality is poor or unavailable, at the moment, a wheel speed meter and the MEMS inertial navigation system are required to be combined, and the positioning and orientation accuracy under the condition that the GNSS signals are shielded is improved.
The wheel speed meter mainly has two errors, namely a degree coefficient error and an azimuth installation error, wherein the former is easily influenced by the deformation of vehicle tires, and the latter is easily influenced by the deformation of a vehicle body structure, so that the two errors are difficult to accurately calibrate in advance, and the two errors need to be estimated and compensated on line in real time. The wheel speed meter error is estimated by using an extended state estimation method in the existing literature, the wheel speed meter error is added into a combined navigation filter, and all state variables are estimated together. Therefore, in order to improve the estimation accuracy of the error of the wheel speed meter of the vehicle-mounted integrated navigation system, an online estimation and compensation method which is simple and easy to implement, small in calculation amount and free of influence on the original filter structure needs to be researched, the online estimation efficiency and the estimation accuracy of the error of the wheel speed meter are improved, and therefore the positioning performance of the vehicle-mounted integrated navigation system under the condition that a GNSS signal is shielded is improved.
Disclosure of Invention
The application provides an on-line estimation and compensation method for errors of a wheel speed meter of a vehicle-mounted integrated navigation system, which is suitable for on-line estimation and compensation of the errors of the wheel speed meter in a high-precision vehicle-mounted positioning and orientation system, and is particularly suitable for application occasions requiring combination of low-cost inertial navigation and the wheel speed meter to realize high-precision positioning under the condition that satellite signals are interfered. The technical scheme adopted by the application is as follows:
a wheel speed meter error online estimation and compensation method of a vehicle-mounted integrated navigation system comprises the following steps:
step 4, according to the measurement information of the error estimation of the wheel speed meter, utilizing a Kalman filtering algorithm to realize the online estimation of the scale coefficient error and the azimuth installation error of the wheel speed meter;
and 5, compensating the speed and position updating process of the wheel speed meter in real time according to the online estimation result of the error of the wheel speed meter.
Further, in step 2, updating the speed and position of the wheel speed meter includes updating an error estimation loop and updating a filter loop.
Further, the calculation formula of the error estimation loop update is as follows:
the updated calculation formula of the filter loop is as follows:
wherein v is OD Showing the original speed output of the wheel speed meter, deltak showing the scale factor error of the wheel speed meter, deltaψ showing the azimuth mounting error of the wheel speed meter,
representing the attitude transformation matrix of the inertial navigation system from a carrier coordinate system (right-front-up) to a navigation coordinate system (east-north-sky),
representing east, north and sky speeds of the error estimation loop of the wheel speed meter,
indicating east, north, and sky speeds, L, of the wheel speed meter filter loop OD-E ,λ OD-E Latitude and longitude, L, representing error estimation loop of wheel speed meter OD-F ,λ OD-F Representing the latitude and longitude of the wheel speed meter filter loop, R x ,R y Represents the radius of the meridian and the prime unit circle of the earth, t k And t k+1 Indicating the last and current time instants, dt indicates the time update period.
Further, if the GNSS signal is good, the position and speed of the integrated navigation is assigned to the position and speed of the wheel speed meter of the filter circuit after the INS/GNSS integrated navigation is completed:
where the subscript KF denotes the combined navigation result,
L KF ,λ KF respectively representing east-direction speed, north-direction speed, latitude and longitude of the combined navigation.
Further, in step 3, the wheel speed error estimation measurement information is calculated according to the following formula:
Z Δk ＝Δd×cos(ΔH) (11)
Z Δψ ＝Δd×sin(ΔH) (12)
wherein, Δ p E And Δ p N East and north position errors, p, representing the error estimation loop of the wheel speed meter E And p N Representing east displacement and north displacement, delta d representing horizontal positioning error of a wheel speed meter error estimation loop, d representing distance between the current position and the initial position of the vehicle, delta H representing a direction angle corresponding to the horizontal positioning error of the wheel speed meter error estimation loop, and Z Δk And Z Δψ And respectively representing the measurement information of the scale coefficient error and the azimuth installation error of the wheel speed meter.
Further, in step 4, the wheel speed online estimation model based on kalman filtering is calculated as follows:
wherein X represents a state variable, Z represents a measurement variable, F represents a system matrix, and H represents a measurement matrix; w and V represent the system noise and the measurement noise, respectively, and both the noises are white noise.
Further, discretizing the online estimation model, wherein a calculation formula is as follows:
wherein I 2 Representing a second order unit matrix, phi k/k-1 Representing the system state transition matrix, t representing the state transition step, and Q representing the system noise matrix.
Further, in step 4, the initial value of the kalman filter is set as follows:
wherein R is k Representing the measurement noise matrix, X 0 Representing the initial value of the state variable, P 0 Representing the initial value of the state covariance matrix and Q representing the system noise matrix.
Compared with the prior art, the beneficial effect that this application has is:
(1) According to the wheel speed meter error online estimation method of the vehicle-mounted integrated navigation system, a double-loop updating mode of the wheel speed meter is designed, an error estimation loop is decoupled from a filter loop, mutual influence of various errors in the dynamic estimation process of the wheel speed meter is avoided, and error compensation is easy to realize;
(2) The method for on-line estimation of the error of the wheel speed meter of the vehicle-mounted integrated navigation system is simple to implement, does not influence the original filtering algorithm framework and calculation process, realizes on-line estimation of the scale coefficient error and the azimuth installation error of the wheel speed meter through the position error of the wheel speed meter and the low-dimensional Kalman filter, and is small in calculated amount and high in estimation precision.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present application, and those skilled in the art can also obtain other drawings according to the drawings without inventive labor.
FIG. 1 is a schematic flow diagram of an online estimation and compensation method;
FIG. 2 is a simulation estimation curve of the wheel speed meter scale factor error;
FIG. 3 is a simulation estimation curve of wheel speed meter azimuth installation error;
FIG. 4 is a diagram of the motion trajectory of a vehicle test;
FIG. 5 is a true estimation curve of the wheel speed gauge scale factor error;
FIG. 6 is a true estimated curve of wheel speed gauge orientation mounting error;
FIG. 7 is a comparison of east position error for the vehicle test;
FIG. 8 is a comparison of vehicle test north position errors.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The principles of the online estimation and compensation method of the present application are as follows: the output of the wheel speed meter is the speed along the y axis (vertical axis) under a carrier coordinate system, when the speed and position are updated by using the output of the wheel speed meter, the speed and position need to be decomposed by combining with an attitude matrix to obtain a speed component under a navigation coordinate system, and then the position is obtained through integration.
When the wheel speed meter has scale factor error, the wheel speed meter is equivalent to the error in longitudinal speed:
considering that the pitch angle and the roll angle of a vehicle-mounted system are small under most conditions, when the wheel speed meter has azimuth installation errors, errors caused by course error decomposition exist equivalently in longitudinal speed and lateral speed:
considering that Δ ψ is small, sin (Δ ψ) ≈ Δ ψ, and cos (Δ ψ) ≈ 1, the above two equations are simplified and combined to obtain a wheel speed meter error model:
according to the wheel speed meter error model obtained by combination after simplification, the scale coefficient error of the wheel speed meter can cause the position error along the longitudinal direction of the driving track, the azimuth installation error can cause the position error along the lateral direction of the driving track, and the error is in direct proportion to the driving distance.
The method designs a double-loop mode for updating the wheel speed meter, the error estimation loop does not compensate scale coefficients and azimuth installation errors when updating the speed position of the wheel speed meter, and the position of the error estimation loop can be compared with the accurate position of INS/GNSS combined navigation in the running process of a vehicle to obtain an east position error delta p E And north position error Δ p N On the basis of which the error azimuth angle H at the moment can be calculated 1 ＝arctan(Δp E /Δp N ). Similarly, according to east displacement p E And north displacement p N An azimuth H of the current position compared to the initial position may be calculated 2 ＝arctan(p E /p N ) And obtaining the decomposition angle delta H of the position error of the wheel speed meter by subtracting the two values:
ΔH＝H 2 -H 1 (26)
according to the characteristics of the scale coefficient error and the azimuth installation error of the wheel speed meter, the corresponding relation between the error of the wheel speed meter and the position error can be obtained:
the direct use of the above-mentioned correspondence formula to calculate the error of the wheel speed meter may be affected by various noises to degrade the estimation accuracy, so that the above-mentioned correspondence formula is adjusted to a state space model corresponding to the expression of the state variable X. When error modeling is carried out, the error of the wheel speed meter is considered according to a constant model, a state noise matrix Q can be properly set in consideration of certain random slow-changing characteristics of the error of the wheel speed meter, and then online recursive estimation of the error of the wheel speed meter is realized through Kalman filtering.
In addition, the position error caused by the error of the wheel speed meter is considered to be proportional to the vehicle running range, so when the initial running range of the vehicle is short, the estimation result precision of the filter is not high, the error compensation is not carried out at the moment, and the compensation can be carried out only when the vehicle runs beyond a certain range. The precision index of the high-precision vehicle-mounted combined navigation system when the inertial navigation/wheel speed meter is combined is that the vehicle runs for 1km, the error does not exceed 2m, and if the positioning error of 2m is equally divided into the scale coefficient error and the azimuth installation error of the wheel speed meter, the corresponding error estimation precision can meet 1000ppm and 3.5 angle divisions. If the maximum driving range of the vehicle is 500m, the position errors caused by the wheel speed error of 1000ppm and 3.5 angular divisions are 0.5m, which can meet the requirement of measurement accuracy, so the wheel speed error compensation threshold value in step 5 is set to be 500m.
The present application is further described with reference to the accompanying drawings.
FIG. 1 is a flow diagram of an online estimation and compensation method. The online estimation method designs a double-loop updating mode of the wheel speed meter, decouples an error estimation loop and a filtering loop, only comprises 5 steps, is simple to implement, does not influence the original filtering algorithm framework and calculation process, realizes online estimation of scale coefficient errors and azimuth installation errors of the wheel speed meter through a position error of the wheel speed meter and a low-dimensional Kalman filter, and has small calculated amount and higher estimation precision.
In step 1, after the vehicle-mounted integrated navigation system is powered on, the GNSS module starts to search for satellites and solve, when positioning and orientation are completed, an initial position and a double-antenna course can be assigned to the inertial navigation system, and a pitch angle and a roll angle of inertial navigation are obtained through the following formulas:
wherein theta is 0 And gamma 0 Is the initial pitch and roll angles, g is the local gravitational acceleration,
and
is the average of the x and y accelerometer outputs during position initialization. In addition, the initial latitude L is recorded 0 And an initial longitude λ 0 。
In step 4, online estimation of the scale coefficient error and the azimuth installation error of the wheel speed meter is realized through a Kalman filtering algorithm, and the calculation formula is as follows:
wherein R is k Representing the measurement noise matrix, P k A covariance matrix representing the state variables.
In step 5, after online estimation results of scale factor errors and azimuth installation errors of the wheel speed meters are obtained, if the distance d of the vehicle relative to the initial position exceeds a preset wheel speed meter error compensation threshold value, the scale factor errors delta k and the azimuth installation errors delta psi of the wheel speed meters in the filter circuit are updated;
and on the basis of the calculation, the speed and the position of the wheel speed meter of the filtering loop are calculated, and if the GNSS signal quality is reduced or the positioning cannot be carried out in the subsequent navigation, the speed and the position information of the filtering loop of the wheel speed meter are used as measurement, and the combined navigation is carried out by combining the MEMS pure inertial navigation result, so that the positioning performance is improved.
In order to verify the correctness of the wheel speed meter error online estimation method, firstly, a simulation verification test is designed, and a square-shaped motion track is generated through a track generator. Various errors are added according to the basic performances of the MEMS inertial navigation module, the GNSS module and the wheel speed meter, and then an error simulation estimation result of the wheel speed meter is obtained through a simulation program.
FIG. 2 is a simulation estimation curve of a scale factor error of a wheel speed meter, and FIG. 3 shows a simulation estimation result of an azimuth installation error of the wheel speed meter, wherein a true value of the scale factor error of the wheel speed meter is set to 1000ppm, a true value of the azimuth installation error is set to 3 angular divisions, and the simulation result shows that the estimation result of the scale factor error is 980ppm, the estimation result of the azimuth installation error is 2.8 angular divisions, the estimation precision is high, and the curve fluctuation in the estimation process is small, which proves that the wheel speed meter error online estimation method provided by the application is accurate and feasible.
FIG. 4 shows the motion trajectory of the vehicle-mounted test for the specific application of the present application, the zero offset stability of the MEMS gyroscope and the accelerometer used in the inertial navigation system is 10 °/h and 100ug, respectively, the UM482 chip is used in the GNSS module, the wheel speed meter is fixedly installed by using the photoelectric encoder through the external connection of the hub, and the pulse of the whole encoder ring is 4096. The total time of the vehicle-mounted test is about 20 minutes, the driving route is positioned between four east rings and five east rings of the sunny-oriented region in Beijing, the running forms include linear acceleration and deceleration, turning and the like, the course motion covers the whole range of 0-360 degrees, and the maximum speed is about 30m/s.
Fig. 5 and 6 show online estimation results of a wheel speed meter scale factor error and an azimuth installation error obtained by a vehicle-mounted test specifically applied to the present application, wherein the error estimation is started after the running range of the test vehicle exceeds 500m, the scale factor error is about-400 ppm, the azimuth installation error is about 21.3 angular points, and the fluctuation of the scale factor error and the azimuth installation error is small and basically consistent with a simulation result, so that the online estimation results of the wheel speed meter scale factor error and the azimuth installation error in the vehicle-mounted test process are proved to be credible.
In order to further verify the accuracy of the wheel speed meter error online estimation result in the vehicle-mounted test, the acquired raw data can be processed offline. In the processing process, a vehicle straight-going section is selected, GNSS positioning information is ignored in software, inertial navigation/wheel speed meter combined navigation is forced to be carried out, the position output after combination is observed, the original inertial navigation/GNSS combined navigation position output is used as a reference true value, and the position error is calculated, so that the accuracy of on-line estimation and compensation of the wheel speed meter error is verified.
Fig. 7 and 8 are diagrams showing a comparison between an east position error and a north position error obtained after an off-line processing of a vehicle-mounted test specifically applied to the present application. In the implementation process, the navigation time is selected from 600 th to 800 th seconds, the inertial navigation/GNSS integrated navigation is switched to the inertial navigation/wheel speed meter integrated navigation, the duration of the navigation time is 200 seconds at the stage, the driving direction is basically the east-west direction, and the driving distance is about 2800m. The dashed curves in fig. 7 and 8 show the position error without error compensation of the wheel speed meter, and it can be seen that the maximum value of the east position error is about 3m, the maximum value of the north position error is about 15m, and the north position error is large, which is related to the obvious azimuth installation error of the wheel speed meter. And after error compensation, the filtering loop of the wheel speed meter is combined with inertial navigation for navigation, and the maximum value of the obtained east position error is 0.8m, and the maximum value of the north position error is 2.6m. According to the propagation rule of the error of the wheel speed meter, the estimation accuracy of the scale coefficient error of the wheel speed meter at the moment can be estimated to be 280ppm, the estimation accuracy of the azimuth installation error is 3.1 angular minutes, and the compensated comprehensive positioning error is about 1 per thousand.
While specific embodiments of the present application have been described above, it will be understood by those skilled in the art that these are by way of example only, and that the scope of the present application is defined by the appended claims. Various changes and modifications to these embodiments may be made by those skilled in the art without departing from the spirit and principles of this application, and these changes and modifications are intended to be included within the scope of this application.
Claims (8)
1. An on-line error estimation and compensation method for a wheel speed meter of a vehicle-mounted integrated navigation system is characterized by comprising the following steps of:
step 1, starting a vehicle-mounted integrated navigation system, completing the initialization of the position and the attitude of integrated navigation, and updating the position, the speed and the attitude through the integrated navigation of MEMS inertial navigation and GNSS;
step 2, updating the speed and the position of the wheel speed meter according to the attitude of the integrated navigation and the information output by the wheel speed meter;
step 3, constructing measurement information of error estimation of the wheel speed meter according to the position of the wheel speed meter and the position of the integrated navigation;
step 4, according to the measurement information of the error estimation of the wheel speed meter, utilizing a Kalman filtering algorithm to realize the online estimation of the scale coefficient error and the azimuth installation error of the wheel speed meter;
and 5, compensating the speed and position updating process of the wheel speed meter in real time according to the online estimation result of the wheel speed meter error.
2. The method of claim 1, wherein in step 2, the updating the speed and position of the wheel speed meter comprises an error estimation loop update and a filter loop update.
3. The method of claim 2, wherein the error estimation loop is updated according to the following equation:
the updated calculation formula of the filter loop is as follows:
wherein v is OD Representing the original speed output of the wheel speed meter, Δ k representing the scale factor error of the wheel speed meter, Δ ψ representing the azimuth mounting error of the wheel speed meter，
Representing the attitude transformation matrix of the inertial navigation system from a carrier coordinate system (right-front-up) to a navigation coordinate system (east-north-sky),
representing east, north and sky speeds of the error estimation loop of the wheel speed meter,
indicating east, north, and sky speeds, L, of the wheel speed meter filter loop OD-E ,λ OD-E Latitude and longitude, L, representing error estimation loop of wheel speed meter OD-F ,λ OD-F Latitude and longitude, R, representing wheel speed meter filter loop x ,R y Represents the radius of the meridian and the prime unit circle of the earth, t k And t k+1 Indicating the last and current time instants, dt indicates the time update period.
4. The method of claim 3, wherein if the GNSS signal is good, then after completing the INS/GNSS combined navigation, assigning the position and velocity of the combined navigation to the position and velocity of the wheel speed meter of the filter loop:
where the subscript KF denotes the combined navigation result,
L KF ,λ KF respectively representing east-direction speed, north-direction speed, latitude and longitude of the combined navigation.
5. The method of claim 1, wherein in step 3, the wheel speed error estimate measurement is calculated as follows:
Z Δk ＝Δd×cos(ΔH) (11)
Z Δψ ＝Δd×sin(ΔH) (12)
wherein, Δ p E And Δ p N East and north position errors, p, representing the error estimation loop of the wheel speed meter E And p N Representing east displacement and north displacement, delta d representing horizontal positioning error of a wheel speed meter error estimation loop, d representing distance between the current position and the initial position of the vehicle, delta H representing a direction angle corresponding to the horizontal positioning error of the wheel speed meter error estimation loop, and Z Δk And Z Δψ And respectively representing the measurement information of the scale coefficient error and the azimuth installation error of the wheel speed meter.
6. The method of claim 1, wherein in step 4, the calculation formula of the wheel speed meter online estimation model based on the kalman filter is as follows:
wherein X represents a state variable, Z represents a measurement variable, F represents a system matrix, and H represents a measurement matrix; w and V represent the system noise and the measurement noise, respectively, and both the two noises are white noises.
7. The method of claim 6, wherein the online estimation model is discretized by the following calculation formula:
in which I 2 Representing a second order unit matrix, phi k/k-1 Representing the system state transition matrix, t representing the state transition step, and Q representing the system noise matrix.
8. The method according to claim 1, characterized in that in step 4, the initial value of the kalman filter is set as follows:
wherein R is k Representing the measurement noise matrix, X 0 Representing the initial value of the state variable, P 0 And representing an initial value of a state covariance matrix, and Q represents a system noise matrix.
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| CN116990536B (en) * | 2023-09-26 | 2023-12-15 | 毫厘智能科技(江苏)有限公司 | Wheel speed error estimation method, device and readable medium |
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