CN110779553A - Calibration method for magnetometer data - Google Patents
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Abstract
A method of calibrating magnetometer data, the method comprising: acquiring the rotating gravity acceleration, magnetometer data at the previous moment and attitude information at the previous moment; calculating an angular velocity error according to the measured acceleration and the rotational gravitational acceleration measured by the accelerometer and the attitude information at the previous moment; performing PI correction on the measured angular velocity measured by the gyroscope by using the angular velocity error to obtain an angular velocity correction value; calculating theoretical magnetometer data according to the angular speed correction value, the attitude information at the previous moment and the magnetometer data at the previous moment; constructing an extended Kalman filter according to theoretical magnetometer data and measured magnetometer data measured by magnetometers; and calculating the magnetometer data calibration value according to the measured acceleration and the measured angular velocity by using an extended Kalman filter.
Description
Technical Field
The present disclosure relates to the field of inertial navigation, and in particular, to a method for calibrating magnetometer data.
Background
Magnetic fields have an important role in determining heading angle and attitude information, but are susceptible to interference from surrounding ferromagnetic materials. When the magnetic field is interfered, the ideal spherical distribution of the magnetic field vector shifts and deforms to present an ellipsoidal distribution. The case of the center of the sphere deviating from the origin is hard magnetic interference, and the case of the deformation of the sphere is soft magnetic interference. The magnetic field calibration is to calculate parameters of hard magnetic interference and soft magnetic interference and compensate the magnetic field data.
In the related art, the ellipsoid fitting algorithm cannot realize real-time calibration, the requirement on the acquired magnetic field data is extremely high, when the acquired magnetic field data set is small or is unevenly distributed, the calibration result is easily deteriorated, and in addition, a user is required to execute a specific complex gesture, so that the user experience is reduced. In the existing method, magnetometer data are predicted through gyroscope data, and the prediction data and measurement data are fused by using an extended Kalman filter to realize magnetometer calibration.
Disclosure of Invention
Technical problem to be solved
In view of the above, the present disclosure provides a calibration method for magnetometer data to solve the above technical problems.
(II) technical scheme
The present disclosure provides a method for calibrating magnetometer data, comprising: acquiring the rotating gravity acceleration, magnetometer data at the previous moment and attitude information at the previous moment; calculating an angular velocity error according to the measured acceleration measured by the accelerometer, the rotational gravitational acceleration and the attitude information at the previous moment; performing PI correction on the measured angular velocity measured by the gyroscope by using the angular velocity error to obtain an angular velocity correction value; calculating theoretical magnetometer data according to the angular speed correction value, the attitude information at the previous moment and the magnetometer data at the previous moment; constructing an extended Kalman filter according to the theoretical magnetometer data and measured magnetometer data measured by the magnetometers; and calculating a magnetometer data calibration value according to the measured acceleration and the measured angular velocity by using the extended Kalman filter.
Optionally, the calculating an angular velocity error according to the measured acceleration measured by the accelerometer, the rotational gravitational acceleration, and the posture information at the previous time includes: calculating theoretical acceleration according to the rotating gravity acceleration and the posture information at the previous moment; and calculating the angular speed error according to the theoretical acceleration and the measured acceleration.
Optionally, the theoretical acceleration and angular velocity errors are:
wherein,
is a theoretical acceleration, e
aAs error of angular velocity, R
k-1Is attitude information at the previous moment, g is rotational gravitational acceleration, a
kTo measure acceleration.
Optionally, the angular velocity correction value is:
wherein,
as a correction value for angular velocity, omega
kTo measure angular velocity, e
aError of angular velocity, K
pFor proportional control parameters in PI corrections, K
iThe integral control parameter in the PI correction is used.
Optionally, the calculating theoretical magnetometer data according to the angular velocity correction value, the attitude information at the previous time, and the magnetometer data at the previous time includes: calculating the attitude information of the current moment according to the angular speed correction value; and calculating the theoretical magnetometer data according to the attitude information at the current moment, the attitude information at the previous moment and the magnetometer data at the previous moment.
Optionally, the angular velocity correction value includes a roll angle correction value, a pitch angle correction value, and a heading angle correction value, and the calculating the attitude information of the current time according to the angular velocity correction value includes: and calculating the attitude information of the current moment according to the roll angle correction value, the pitch angle correction value and the course angle correction value.
Optionally, the theoretical magnetometer data is:
wherein,
as theoretical magnetometer data, R
kAs attitude information at the present time, R
k-1As attitude information at the previous time, B
k-1Magnetometer data at the last moment.
Optionally, the constructing an extended kalman filter according to the theoretical magnetometer data and the measured magnetometer data measured by the magnetometer includes: calculating a state transition matrix and a measured matrix according to the theoretical magnetometer data and the measured magnetometer data; and constructing an extended Kalman filter by using the state transition matrix and the measurement matrix.
Optionally, the calculating a state transition matrix and a measured matrix according to the theoretical magnetometer data and the measured magnetometer data includes: establishing a soft magnetic parameter matrix and a hard magnetic parameter matrix; generating a current moment state value according to the theoretical magnetometer data, the soft magnetic parameter matrix and the hard magnetic parameter matrix; and obtaining the state transition matrix and the measurement matrix according to the current time state value and the measured magnetometer data.
Optionally, the calculating, by using the extended kalman filter, a magnetometer data calibration value according to the measured acceleration and the measured angular velocity includes: and inputting the state value at the last moment, the variance estimation value of the state value at the last moment, the measured acceleration and the measured angular velocity into the extended Kalman filter to obtain a calibrated state value at the current moment and a variance estimation value of the state value at the current moment, wherein the calibrated state value at the current moment comprises the magnetometer data calibration value.
(III) advantageous effects
The calibration method of magnetometer data provided by the disclosure has the following beneficial effects:
(1) fusing accelerometer data and gyroscope data by using a six-axis algorithm to accurately predict magnetometer data;
(2) the calibration of the magnetometer is realized by utilizing the extended Kalman filter, so that the calibration result is more accurate and stable;
(3) need not the user and carry out specific action alright realize real-time calibration, the calibration is more convenient, quick, promotes user experience and feels.
Drawings
FIG. 1 schematically illustrates a flow chart of a method of calibrating magnetometer data provided by an embodiment of the disclosure; and
fig. 2 schematically shows an effect diagram of the calibration method for magnetometer data provided by the embodiment of the disclosure after performing magnetometer calibration.
Detailed Description
For the purpose of promoting a better understanding of the objects, aspects and advantages of the present disclosure, reference is made to the following detailed description taken in conjunction with the accompanying drawings.
Fig. 1 schematically shows a flowchart of a calibration method for magnetometer data provided by an embodiment of the present disclosure.
Referring to fig. 1, the method shown in fig. 1 will be described in detail with reference to fig. 2. As shown in fig. 1, the calibration method of magnetometer data includes operations S110 to S160.
And S110, acquiring the rotating gravity acceleration, magnetometer data at the previous moment and attitude information at the previous moment.
In the disclosed embodiment, for example, real-time monitoring can be performedListening to a nine-axis sensor of the MEMS to obtain data measured by the magnetometer, the accelerometer and the gyroscope at each moment, and simultaneously storing magnetometer data B at the previous moment
k-1And attitude information R of the previous time
k-1。
The rotational gravitational acceleration g represents the value of the gravitational acceleration in the carrier coordinate system. The carrier coordinate system is a coordinate system which takes the centroid of the sensor as an origin and three sensitive axes of the sensor as coordinate axes.
And S120, calculating an angular velocity error according to the measured acceleration, the rotational gravitational acceleration and the posture information of the previous moment measured by the accelerometer.
According to an embodiment of the present disclosure, operation S120 includes operation S120A and operation S120B.
In operation S120A, a theoretical acceleration is calculated from the rotational gravitational acceleration and the posture information at the previous time. Specifically, the rotational gravitational acceleration g and the posture information R at the previous time are used
k-1Predicting the value of the gravity acceleration of the current position in the carrier coordinate system so as to obtain the theoretical acceleration
Theoretical acceleration
Comprises the following steps:
wherein,
as theoretical acceleration, R
k-1G is the rotational gravitational acceleration.
In operation S120B, an angular velocity error is calculated based on the theoretical acceleration and the measured acceleration. Specifically, the theoretical acceleration is set
And accelerationThe measured acceleration a measured by the meter
kCross product to obtain an angular velocity error e
aThe error of angular velocity e
aComprises the following steps:
wherein,
to theoretical acceleration, a
kTo measure acceleration.
And S130, performing PI correction on the measured angular velocity measured by the gyroscope by using the angular velocity error to obtain an angular velocity correction value.
In the embodiment of the present disclosure, the drift error of the gyroscope is eliminated by a Proportional Integral (PI) controller, and as long as there is an error, the PI controller continues to act until the error is 0.
According to the embodiment of the present disclosure, the obtained angular velocity correction value is:
wherein,
as a correction value for angular velocity, omega
kTo measure angular velocity, e
aError of angular velocity, K
pFor proportional control parameters in PI corrections, K
iThe integral control parameter in the PI correction is used. It can be understood that ω is
kThe measured angular velocity at time k is,
to eliminate the correction value of the angular velocity at the time k after the error.
And S140, calculating theoretical magnetometer data according to the angular speed correction value, the attitude information at the previous moment and the magnetometer data at the previous moment.
In the disclosed embodiment, the attitude information of the sensor mainly comprises a rotation matrixR, quaternion q and angular velocity, including roll angle
The pitch angle theta and the heading angle phi have the following conversion relationship:
q＝w+xi+yj+zk
according to an embodiment of the present disclosure, operation S140 includes operation S140A and operation S140B.
In operation S140A, attitude information at the current time is calculated from the angular velocity correction value. Using the correction value of angular velocity according to the conversion relation
And updating the quaternion to obtain real-time attitude information. According to an embodiment of the present disclosure, an angular velocity correction value
The attitude information R at the current moment can be calculated according to the roll angle correction value, the pitch angle correction value and the course angle correction value
k。
In the embodiment of the disclosure, since the accelerometer has long-term stability when calculating the pitch angle and the roll angle, the attitude information obtained by the magnetometer data calibration method can avoid drift in the pitch angle and the roll angle directions, and the attitude stability is greatly improved.
In operation S140B, theoretical magnetometer data are calculated from the attitude information at the current time, the attitude information at the previous time, and the magnetometer data at the previous time.
In the embodiment of the disclosure, the attitude information R at the current moment is used
kAnd upper part ofAttitude information R at one time
k-1Calculating the change value delta R of the attitude information
kThen using the change value Δ R of the attitude information
kFor magnetometer data B at the last moment
k-1Predicting to obtain the theoretical value of the magnetometer data at the current moment, namely obtaining the theoretical magnetometer data at the current moment
According to an embodiment of the disclosure, the theoretical magnetometer data is:
wherein,
as theoretical magnetometer data, R
kAs attitude information at the present time, R
k-1As attitude information at the previous time, B
k-1Magnetometer data at the last moment.
S150, constructing an extended Kalman filter according to the theoretical magnetometer data and the measured magnetometer data measured by the magnetometers.
According to an embodiment of the present disclosure, operation S150 includes operation S150A and operation S150B.
In operation S150A, a state transition matrix and a metrology matrix are calculated from the theoretical magnetometer data and the metrology magnetometer data. Specifically, according to embodiments of the present disclosure, a soft magnetic parameter matrix W and a hard magnetic parameter matrix V are established, based on theoretical magnetometer data
The soft magnetic parameter matrix W and the hard magnetic parameter matrix V generate the state value X at the current moment
kAccording to the current time state value X
kAnd magnetometer data B
kAnd obtaining a state transition matrix and a measurement matrix.
In the embodiment of the present disclosure, the generated current time state value is X
k：
Wherein, W
11、W
22、W
33、W
12、W
13、W
13Are elements of the corresponding position in the soft magnetic parameter matrix W.
Further, according to the current time state value X
kAnd magnetometer data B
kThe corresponding state transition matrix F is calculated according to the relation between the variables
kAnd a measurement matrix H
k。
Specifically, the state transition matrix F is calculated according to the following relational expression
k：
X
k+1＝F
kX
k+d
k
Wherein d is
kBeing noise during the state transition, Δ k is the time difference between time k +1 and time k.
Specifically, the measurement matrix H is calculated according to the following relation
k：
Z
k＝H
kX
k+v
k
Wherein Z is
kIs the measured value of the magnetometer at time k, v
kThe measurement error of the magnetometer measurement at the time k.
In operation S150B, an extended kalman filter is constructed using the state transition matrix and the measurement matrix. In the embodiment of the disclosure, the calculation parameters in the extended kalman filter include a state transition matrix F
kAnd a measurement matrix H
k。
And S160, calculating the magnetometer data calibration value according to the measured acceleration and the measured angular velocity by using an extended Kalman filter.
According to the embodiment of the disclosure, the last moment state value X is used
k-1Variance estimation value P of state value at last moment
k-1Measuring the acceleration a
kAnd measuring the angular velocity omega
kInputting the extended Kalman filter to obtain the state value X at the current time after calibration
kAnd the variance estimation value P of the current time state value
kThe calibrated current time state value X
kIncluding magnetometer data calibration values.
Specifically, according to the extended kalman filter, the formula for calibrating magnetometer data is as follows:
a prediction stage:
X
k|k-1＝F
k-1X
k-1
and (3) an updating stage:
X
k＝X
k|k-1+K
k(Z
k-H
kX
k|k-1)
P
k＝(1-K
kH
k)P
k|k-1
wherein, P
l(l ═ 0, 1, 2, …, k) is the variance estimate of the state value at time l calculated by the extended kalman filter, and its initial value P
0It can be set as a unit matrix, and then a new variance estimation value is output every time the extended kalman filtering is performed, and is used for the next extended kalman filtering. F
k-1Is the state transition matrix at time k-1, X
k-1Is the state value at time k-1, X
k|k-1Is a one-step predicted value of the state value from the time k-1 to the time k, F
k-1Is the state transition matrix at time k-1, P
k-1Is the variance of the state value at the time k-1Estimated value, Q
k-1Is the variance matrix of the noise at time K-1 during the state transition, K
kIs a filter gain matrix, which is an optimum gain matrix for minimizing the mean square error of the estimate, H
kIs a measurement matrix at time k, R
kIs the covariance matrix of the measurement error at time k, P
k|k-1Is a one-step predicted value from k-1 to k of the variance estimation value of the state value, Z
kIs the measured value of the magnetometer at time k, X
k、P
kThe state value and the variance estimation value at the current time (namely k time) are calculated and obtained. X
kIncluding magnetometer data calibration values.
Fig. 2 schematically shows an effect diagram of the calibration method for magnetometer data provided by the embodiment of the disclosure after performing magnetometer calibration. Referring to fig. 2, it can be seen. The calibration method for magnetometer data in the embodiment of the disclosure can inhibit the drift of the calibrated magnetometer data, and realize more stable magnetic field calibration.
In summary, in the calibration method for magnetometer data in the embodiment of the present disclosure, accelerometer data and gyroscope data are fused by using a six-axis fusion algorithm, the gyroscope data is corrected by using acceleration, the magnetometer is rotated by using the corrected gyroscope data, so as to predict the magnetometer data more accurately, and then the predicted value and the magnetometer measurement value are fused by using extended kalman filtering, so that dynamic calibration of the magnetometer is achieved, drift of the magnetometer data can be suppressed, and stable and high-precision real-time magnetometer calibration is achieved. The calibration method of magnetometer data has wide application potential in devices needing to use a magnetometer to determine the attitude, such as consumer electronics, vehicle inertial navigation systems, mobile phones, AR glasses and the like.
The above-mentioned embodiments are intended to illustrate the objects, aspects and advantages of the present disclosure in further detail, and it should be understood that the above-mentioned embodiments are only illustrative of the present disclosure and are not intended to limit the present disclosure, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present disclosure should be included in the scope of the present disclosure.
Claims (10)
1. A method of calibrating magnetometer data, comprising:
acquiring the rotating gravity acceleration, magnetometer data at the previous moment and attitude information at the previous moment;
calculating an angular velocity error according to the measured acceleration measured by the accelerometer, the rotational gravitational acceleration and the attitude information at the previous moment;
performing PI correction on the measured angular velocity measured by the gyroscope by using the angular velocity error to obtain an angular velocity correction value;
calculating theoretical magnetometer data according to the angular speed correction value, the attitude information at the previous moment and the magnetometer data at the previous moment;
constructing an extended Kalman filter according to the theoretical magnetometer data and measured magnetometer data measured by the magnetometers;
and calculating a magnetometer data calibration value according to the measured acceleration and the measured angular velocity by using the extended Kalman filter.
2. The method of claim 1, wherein said calculating an angular velocity error from the measured acceleration from the accelerometer, the rotational acceleration of gravity, and the attitude information at the previous time comprises:
calculating theoretical acceleration according to the rotating gravity acceleration and the posture information at the previous moment;
and calculating the angular speed error according to the theoretical acceleration and the measured acceleration.
3. The method of claim 2, wherein the theoretical acceleration and angular velocity errors are:
wherein,
is a theoretical acceleration, e
aAs error of angular velocity, R
k-1Is attitude information at the previous moment, g is rotational gravitational acceleration, a
kTo measure acceleration.
4. The method according to claim 1, wherein the angular velocity correction value is:
wherein,
as a correction value for angular velocity, omega
kTo measure angular velocity, e
aError of angular velocity, K
pFor proportional control parameters in PI corrections, K
iThe integral control parameter in the PI correction is used.
5. The method of claim 1, wherein said calculating theoretical magnetometer data from said angular rate correction value, attitude information at a previous time, and magnetometer data at a previous time comprises:
calculating the attitude information of the current moment according to the angular speed correction value;
and calculating the theoretical magnetometer data according to the attitude information at the current moment, the attitude information at the previous moment and the magnetometer data at the previous moment.
6. The method of claim 5, wherein the angular velocity correction values include a roll angle correction value, a pitch angle correction value, and a heading angle correction value, and the calculating the attitude information at the current time from the angular velocity correction values includes:
and calculating the attitude information of the current moment according to the roll angle correction value, the pitch angle correction value and the course angle correction value.
7. The method of claim 5, wherein the theoretical magnetometer data are:
wherein,
as theoretical magnetometer data, R
kAs attitude information at the present time, R
k-1As attitude information at the previous time, B
k-1Magnetometer data at the last moment.
8. The method of claim 1, wherein said constructing an extended kalman filter from said theoretical magnetometer data and magnetometer measured magnetometer data comprises:
calculating a state transition matrix and a measured matrix according to the theoretical magnetometer data and the measured magnetometer data;
and constructing an extended Kalman filter by using the state transition matrix and the measurement matrix.
9. The method of claim 8, wherein said computing a state transition matrix and a metrology matrix from said theoretical magnetometer data and metrology magnetometer data comprises:
establishing a soft magnetic parameter matrix and a hard magnetic parameter matrix;
generating a current moment state value according to the theoretical magnetometer data, the soft magnetic parameter matrix and the hard magnetic parameter matrix;
and obtaining the state transition matrix and the measurement matrix according to the current time state value and the measured magnetometer data.
10. The method of claim 9, wherein said calculating magnetometer data calibration values from said measured acceleration and measured angular velocity using said extended kalman filter comprises:
and inputting the state value at the last moment, the variance estimation value of the state value at the last moment, the measured acceleration and the measured angular velocity into the extended Kalman filter to obtain a calibrated state value at the current moment and a variance estimation value of the state value at the current moment, wherein the calibrated state value at the current moment comprises the magnetometer data calibration value.
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| CN115900770A (en) * | 2023-02-14 | 2023-04-04 | 北京理工大学前沿技术研究院 | Online correction method and system for magnetic sensor in airborne environment |
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Application publication date: 20200211 |