CN113790737B - On-site rapid calibration method of array sensor - Google Patents
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Abstract
The invention discloses a field quick calibration method of an array sensor, which belongs to the field of intelligent system navigation measurement, wherein N sensors in an array except for the sensor to be calibrated form a virtual high-precision sensor, the sensor to be calibrated is calibrated, parameters of an accelerometer in the array are calibrated by utilizing static sampling data, parameters of a gyroscope in the array are calibrated by utilizing dynamic sampling data, and calibration results are checked to judge the calibration effect. The method can realize the rapid calibration of the array sensor in real time on a task site, utilizes the correlation of the array sensor by combining a static test and a dynamic test, adopts a least square fitting method to estimate the optimal calibration parameters, corrects the zero offset and the scale factor of the IMU, can remarkably improve the navigation precision, greatly reduces the field use difficulty of the array sensor, and can ensure the reliability of the calibration by checking the calibration result.
Description
Technical Field
The invention relates to the field of intelligent system navigation measurement, in particular to a field rapid calibration method of an array sensor.
Background
Along with the rapid development of science and technology, the development of the intelligent vehicle, unmanned aerial vehicle and high-precision precise striking weapon is not separated from an inertial navigation system. The inertial navigation system provides accurate attitude and position information for the navigation positioning of intelligent and automatic machinery, wherein the MEMS inertial device plays a key role in various industries due to the characteristics of low cost, small volume, light weight and easy mass production.
The use of inertial navigation systems is not separated from the prior calibration. The traditional calibration method is to use an installation datum plane as a reference, calibrate based on a speed position turntable, and mainly complete calibration of zero position and scale factor, wherein the installation error is only related to the initial relative position relation, and after calibration is completed, the stability of the general installation error parameter is better, but the stability of the zero position and the scale factor parameter is poorer. As the factory time is prolonged, the zero position of the MEMS inertial device and the parameter holding capability of the scale factor are poor, which can cause the performance degradation of the MEMS inertial device, so a simple on-site calibration method is needed to quickly calibrate the parameters.
The on-site quick calibration technology of the array sensor is not only beneficial to correcting various parameters of the IMU sensor and improving navigation precision, but also can greatly reduce on-site use difficulty. The research on the field calibration technology of the array sensor is beneficial to the field of automatic driving and benefits in the related field of navigation measurement of the array sensor. Therefore, research on-site rapid calibration technology of the array sensor is of great importance.
Disclosure of Invention
In view of the above, the invention provides a field quick calibration method of an array sensor, which is used for quick calibration of the array sensor on a product use field, correction of sensor residual errors and reduction of field use difficulty.
The invention provides a field rapid calibration method of an array sensor, which comprises the following steps:
s1: turning over at least 6 different positions of the array sensor, and simultaneously performing static sampling on each position for a first preset time length by using the array sensor to obtain static sampling data; the overturning position ensures that acceleration projection components are arranged on the x axis, the y axis and the z axis of all sensors in the array sensor;
s2: freely rotating the array sensor, and dynamically sampling for a second preset time length by using the array sensor to obtain dynamic sampling data; wherein, the free rotation ensures that all sensors in the array sensor have angular velocity projection components on the x axis, the y axis and the z axis;
s3: respectively selecting data with maximum acceleration of the sensor to be calibrated in the x axis, the y axis and the z axis from the static sampling data, and calculating zero offset and scale factors of accelerometers of the sensor to be calibrated by using the selected data and acceleration data of other sensors except the sensor to be calibrated in the static sampling data; calculating zero offset and scale factors of a gyroscope of the sensor to be calibrated by using angular velocity data of other sensors except the sensor to be calibrated in the dynamic sampling data; repeating the step S3 until all the sensors in the array type sensor are traversed;
s4: zero bias of the accelerometers and gyroscopes of all sensors in the array sensor is checked, and scale factors of the accelerometers and gyroscopes of all sensors in the array sensor are checked.
In one possible implementation manner, in the method for rapidly calibrating an array sensor on site provided by the present invention, in step S1, the array sensor is flipped over by at least 6 different positions, which specifically includes:
the method comprises the steps of turning over an array sensor to the x-axis vertical direction of all sensors, turning over the array sensor to the y-axis vertical direction of all sensors, turning over the array sensor to the z-axis vertical direction of all sensors, and turning over the array sensor to the z-axis vertical direction of all sensors.
In a possible implementation manner, in the method for on-site rapid calibration of an array sensor provided by the present invention, in step S1, the first preset duration is at least 3min.
In a possible implementation manner, in the method for on-site rapid calibration of an array sensor provided by the present invention, in step S2, the second preset duration is at least 3min.
In a possible implementation manner, in the on-site rapid calibration method of an array sensor provided by the invention, step S3, data with maximum acceleration of the sensor to be calibrated in the x-axis, the y-axis and the z-axis are selected from the static sampling data respectively, and zero offset and scale factors of accelerometers of the sensor to be calibrated are calculated by using the selected data and acceleration data of other sensors except the sensor to be calibrated in the static sampling data; calculating zero offset and scale factors of a gyroscope of a sensor to be calibrated by using angular velocity data of other sensors except the sensor to be calibrated in dynamic sampling data, wherein the method specifically comprises the following steps:
assuming that the array sensor includes n+1 sensors, the measurement equations for the gyroscopes or accelerometers for the x, y and z axes of the p-th sensor to be calibrated are:
wherein i is real-x 、i real-y And i real-z The theoretical values of the band dimensions of the x axis, the y axis and the z axis of the sensor to be calibrated are respectively represented and are determined by fusion of Kalman filters of N sensors except the sensor to be calibrated; measured value sensor px 、sensor py And sensor pz The method comprises the steps of respectively representing the output digital quantities without dimension of an x axis, a y axis and a z axis of a sensor to be calibrated; SF (sulfur hexafluoride) p x、SF p y and SF p z represents the scale factors of the x-axis, y-axis and z-axis of the p-th sensor to be calibrated, b p Indicating the zero offset, v, of the p-th sensor to be calibrated p Representing the residual error of the p-th sensor to be calibrated;
the measurement equation and the observation equation for the MEMS array composed of N sensors except the sensor to be calibrated are as follows:
Z(t)＝H·ω+v(t) (3)
wherein X (t) represents a state variable of the Kalman filter, the state variable is real acceleration or real angular velocity, and the state variable is 1 dimension; z (t) represents the sensor to be calibratedOutput values of other N sensors outside the sensor; h is a measurement matrix, and represents the conversion relation between each sensor and the carrier system; ω represents the true acceleration or true angular velocity,n ω representing a mean value of 0 and a variance of q ω White noise of (a); f is zero matrix, ω (t) is process noise, v (t) is observation noise;
the Kalman filter equation is:
K(t)＝P(t)H T R -1 (5)
wherein K (t) represents the gain variation of the Kalman filter with time, and P (t) represents the estimated error variation of the Kalman filter with time; r is the covariance matrix of the measurement noise, expressed as:
wherein q n Representing the variance of ARW noise of the sensor to be calibrated, wherein ρ represents the cross correlation coefficient of the array sensor;
k (t) iteratively converges to a fixed value, which is obtained by:
c＝H T R -1 H (8)
wherein K is ∞ Represents the iterative convergence value, P, of the gain of the Kalman filter ∞ Representation ofThe estimated error of the Kalman filter is subjected to iterative convergence value;
the estimate of the Kalman filter state variable for continuous time using K (t) is:
discretizing the Kalman filtering state variable in continuous time, using zero-order maintenance, and assuming that the acceleration or angular velocity is constant in the whole sampling period to obtain:
wherein t is 0 The sampling interval is represented by the number of samples,representing the output of gyroscopes or accelerometers of virtual sensors, Z, from N sensors other than the sensor to be calibrated k+1 Representing the original output of N sensors except the sensor to be calibrated; performing linear least square fitting by utilizing the relation between the output of the sensor to be calibrated and the virtual sensor to obtain zero offset and scale factors of the sensor to be calibrated:
wherein,scale factor representing the sensor to be calibrated, < +.>Indicating the zero offset of the sensor to be calibrated.
In one possible implementation manner, in the method for on-site rapid calibration of an array sensor provided by the present invention, step S4 is performed to test zero offset of accelerometers and gyroscopes of all sensors in the array sensor, and to test scale factors of accelerometers and gyroscopes of all sensors in the array sensor, and specifically includes:
s41: standing the array type sensor, collecting an accelerometer output value and a gyro output value of each sensor in the array type sensor, checking the zero offset calibration effect of the accelerometer by utilizing the square sum of the accelerometer output values of each sensor to be equal to the gravity acceleration, and checking the zero offset calibration effect of the gyro by utilizing the gyro output value of each sensor to be zero;
s42: and the array sensor is put back to the original position after freely rotating, and the calibration effect of the scale factors of the accelerometer and the gyroscope is checked by carrying out navigation calculation on the accelerometer and the gyroscope.
The invention provides a field rapid calibration method of an array sensor, which belongs to the field of intelligent system navigation measurement, N sensors in an array except for the sensor to be calibrated form a virtual high-precision sensor, the sensor to be calibrated is calibrated, parameters of an accelerometer in the array are calibrated by static sampling data, parameters of a gyroscope in the array are calibrated by dynamic sampling data, and calibration results are checked to judge calibration effects. The method can realize the rapid calibration of the array sensor in real time on a task site, utilizes the correlation of the array sensor by combining a static test and a dynamic test, adopts a least square fitting method to estimate the optimal calibration parameters, corrects the zero offset and the scale factor of the IMU, can remarkably improve the navigation precision, greatly reduces the field use difficulty of the array sensor, and can ensure the reliability of the calibration by checking the calibration result.
Drawings
Fig. 1 is a flow chart of a method for on-site quick calibration of an array sensor according to embodiment 1 of the present invention;
FIG. 2 is a diagram of the raw output of a sensor including zero offset and scale factor error;
FIG. 3 is a schematic diagram of the generation of raw data as shown in FIG. 2 using a sensor track generator;
FIG. 4 is a graph of compensated sensor output;
FIG. 5 is a diagram of a noise model of a single axis sensor;
FIG. 6 is a Kalman filtering flow chart for an array sensor.
Detailed Description
The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is apparent that the described embodiments are merely examples and are not intended to limit the present invention.
The invention provides a field rapid calibration method of an array sensor, which comprises the following steps:
s1: turning over at least 6 different positions of the array sensor, and simultaneously performing static sampling on each position for a first preset time length by using the array sensor to obtain static sampling data; the overturning position ensures that acceleration projection components are arranged on the x axis, the y axis and the z axis of all sensors in the array sensor;
s2: freely rotating the array sensor, and dynamically sampling for a second preset time length by using the array sensor to obtain dynamic sampling data; wherein, the free rotation ensures that all sensors in the array sensor have angular velocity projection components on the x axis, the y axis and the z axis;
s3: respectively selecting data with maximum acceleration of the sensor to be calibrated in the x axis, the y axis and the z axis from the static sampling data, and calculating zero offset and scale factors of accelerometers of the sensor to be calibrated by using the selected data and acceleration data of other sensors except the sensor to be calibrated in the static sampling data; calculating zero offset and scale factors of a gyroscope of the sensor to be calibrated by using angular velocity data of other sensors except the sensor to be calibrated in the dynamic sampling data; repeating the step S3 until all the sensors in the array type sensor are traversed;
s4: zero bias of the accelerometers and gyroscopes of all sensors in the array sensor is checked, and scale factors of the accelerometers and gyroscopes of all sensors in the array sensor are checked.
The following describes in detail the implementation of the method for on-site rapid calibration of an array sensor according to the present invention by means of a specific embodiment.
Example 1:
taking a MEMS (Micro-Electro-Mechanical System) array as an example, a certain single-axis sensor in a sub-IMU in the MEMS array is modeled first, and for an accelerometer or a gyroscope of a q-th axis sensor of a p-th sub-IMU, a model formula of the sensor is as follows:
wherein x is m 、y m 、z m Representing the real acceleration or angular velocity of the x-axis, y-axis and z-axis of the selected carrier coordinate system respectively; the h matrix is a transformation matrix for converting the carrier coordinate system into the sub IMU coordinate system, and is determined to be known when the MEMS array configuration is determined and is mounted on the carrier; sensor p Representing the number of measured values of the p-th sub-sensor without dimension;zero offset of the q-th axis sensor representing the p-th sub-IMU,/and>representing random noise of the q-th axis sensor of the p-th sub-IMU.
Since the array sensor may be sensitive to gravity as a known stimulus when resting, the zero offset and scale factors of the accelerometer of the array sensor may be calibrated using gravity as a reference. Since the rotational angular velocity of the earth is a small amount that is insensitive to the MEMS gyroscope, an external stimulus is required to calibrate the MEMS gyroscope, and in particular, the angular velocity stimulus can be applied to the MEMS gyroscope by using a method of rotating the MEMS gyroscope.
When the accelerometer in the MEMS array is calibrated by gravity excitation, the gravity excitation is required to be applied to all x, y and z three axes, and because the equation (1) contains 6 unknown quantities, namely zero offset and scale of the accelerometer of the x, y and z three-axis sensor, the MEMS array is required to be turned over for 6 different positions to be sampled by IMU, and then the calibration parameters of the accelerometer of the MEMS array are obtained by solving an equation set (consisting of an equation of each axis of each sensor). For gyroscopes, a hand-held MEMS array is required to rotate freely, which is only necessary to ensure that all of the x, y, z axes of the sensors within the MEMS array are sensitive to angular velocity.
As shown in fig. 1, the specific steps are as follows:
the first step: turning over at least 6 different positions of the array sensor, and simultaneously performing static sampling on each position for a first preset time length by using the array sensor to obtain static sampling data; wherein, the overturned position needs to ensure that all sensors in the array sensor have acceleration projection components on the x axis, the y axis and the z axis.
In particular, flipping the array sensor over at least 6 different positions may be achieved by: the method comprises the steps of turning over an array sensor to the x-axis vertical direction of all sensors, turning over the array sensor to the y-axis vertical direction of all sensors, turning over the array sensor to the z-axis vertical direction of all sensors, and turning over the array sensor to the z-axis vertical direction of all sensors. The first preset duration is at least 3 minutes.
And a second step of: freely rotating the array sensor, and dynamically sampling for a second preset time length by using the array sensor to obtain dynamic sampling data; wherein the free rotation ensures that all sensors within the array sensor have angular velocity projection components in the x, y and z axes.
Specifically, the hand-held array sensor can freely rotate, and the second preset time period is at least 3min.
And a third step of: respectively selecting data with maximum acceleration of the sensor to be calibrated in the x axis, the y axis and the z axis from the static sampling data, and calculating zero offset and scale factors of accelerometers of the sensor to be calibrated by using the selected data and acceleration data of other sensors except the sensor to be calibrated in the static sampling data; and calculating zero offset and scale factors of the gyroscopes of the sensors to be calibrated by using angular velocity data of other sensors except the sensors to be calibrated in the dynamic sampling data.
Specifically, assuming that the array sensor includes n+1 sensors, the measurement equations for gyroscopes or accelerometers for the x, y, and z axes of the p-th sensor to be calibrated may be:
wherein i is real-x 、i real-y And i real-z The theoretical values of the band dimensions of the x axis, the y axis and the z axis of the sensor to be calibrated are respectively represented and are determined by fusion of Kalman filters of N sensors except the sensor to be calibrated; measured value sensor px 、sensor py And sensor pz The method comprises the steps of respectively representing the output digital quantities without dimension of an x axis, a y axis and a z axis of a sensor to be calibrated; SF (sulfur hexafluoride) p x、SF p y and SF p z represents the scale factors of the x-axis, y-axis and z-axis of the p-th sensor to be calibrated, b p Indicating the zero offset, v, of the p-th sensor to be calibrated p Representing the residual error of the p-th sensor to be calibrated.
After obtaining static sampling data of 6 positions, the accelerometer of the 1 st sensor in the MEMS array is calibrated first, and then the accelerometers of the following N sensors are calibrated in sequence. Taking the x-axis accelerometer of the first sensor in the MEMS array as an example, 2 groups of data with the largest average output of the x-axis accelerometer of the 1 st sensor in the 6 groups of data are selected as data groups for calibrating the x-axis accelerometer, and zero offset and scale factor correction are carried out on the accelerometers to be calibrated through virtual accelerometers consisting of the accelerometers of the other N sensors. For the gyroscope, after dynamic free rotation gyroscope data are obtained, the gyroscope of the 1 st sensor is calibrated firstly, then the gyroscopes of the N rear sensors are calibrated sequentially, and zero offset and scale factor correction are carried out on the accelerometer to be calibrated through a virtual gyroscope formed by the gyroscopes of the first sensor in the MEMS array by taking the x-axis gyroscope of the other N sensors as an example.
The measurement equation and the observation equation for the MEMS array composed of N sensors except the sensor to be calibrated are as follows:
Z(t)＝H·ω+v(t) (4)
wherein X (t) represents a state variable of the Kalman filter, the state variable is real acceleration or real angular velocity, and the state variable is 1 dimension; z (t) represents the output values of N sensors except the sensor to be calibrated; h is a measurement matrix, and represents the conversion relation between each sensor and the carrier system; ω represents the true acceleration or true angular velocity,n ω representing a mean value of 0 and a variance of q ω White noise of (a); f is zero matrix, ω (t) is process noise, and v (t) is observation noise.
The Kalman filter equation may be expressed as:
K(t)＝P(t)H T R -1 (6)
wherein K (t) represents the gain variation of the Kalman filter with time, and P (t) represents the estimated error variation of the Kalman filter with time; r is the covariance matrix of the measurement noise, which is not a diagonal matrix, because of the correlation between different MEMS sensors, expressed as:
wherein q n Representing the variance of the ARW noise of the sensor to be calibrated, ρ representing the cross-correlation coefficient of the array sensor.
Since the kalman filter system is fully observable, the K (t) iteration converges to a fixed value, which can be obtained by:
c＝H T R -1 H (9)
wherein K is ∞ Represents the iterative convergence value, P, of the gain of the Kalman filter ∞ Representing the iterative convergence value of the estimated error of the kalman filter.
The estimate of the Kalman filter state variable for continuous time using K (t) is:
discretizing the Kalman filtering state variable in continuous time, using zero-order maintenance, assuming that acceleration or angular velocity is constant in the whole sampling period, it can be obtained:
wherein t is 0 The sampling interval is represented by the number of samples,represented by N other sensors than the sensor to be calibratedOutput of gyroscopes or accelerometers of virtual sensor of sensor composition, Z k+1 Representing the original output of N sensors except the sensor to be calibrated; performing linear least square fitting by utilizing the relation between the output of the sensor to be calibrated and the virtual sensor to obtain zero offset and scale factors of the sensor to be calibrated:
wherein,scale factor representing the sensor to be calibrated, < +.>Indicating the zero offset of the sensor to be calibrated.
Repeating the third step until all the sensors in the array sensor are traversed.
Fourth step: zero bias of the accelerometers and gyroscopes of all sensors in the array sensor is checked, and scale factors of the accelerometers and gyroscopes of all sensors in the array sensor are checked.
Specifically, standing the array type sensor, collecting an accelerometer output value and a gyro output value of each sensor in the array type sensor, checking the zero offset calibration effect of the accelerometer by utilizing the square sum of the accelerometer output values of each sensor to be equal to the gravity acceleration, and checking the zero offset calibration effect of the gyro by utilizing the gyro output value of each sensor to be zero; and then, the array sensor is put back to the original position after freely rotating, and the calibration effect of the scale factors of the accelerometer and the gyroscope is checked by carrying out navigation calculation on the accelerometer and the gyroscope.
Fig. 2 is a simulation of 4 sets of gyro data, taking gyro output values in the x-axis direction as an example. Then, by matlab software, the angle random walk ARW parameter of the gyro in the MEMS array is set to 0.0833 °/(h (1/2)), the velocity random walk RRW parameter is 600 °/(h (3/2)), the input angular rate is zero, 18000s of sensor raw data is generated according to the track generator as shown in FIG. 3, the sampling interval is set to 10ms, and zero offset and scale factor errors are added thereto. FIG. 4 shows the calibrated gyro output values, and from FIG. 4, it can be seen that the data of 4 gyroscopes in the array generated by simulation is calibrated to obtain the residual zero offset and the scale factor of the sensor, and the peak-to-peak value of the compensated angular velocity error is reduced from 1 degree/s to 0.3 degree/s, which indicates that the accuracy of the sensor is obviously improved.
FIG. 5 is a graph of a single axis sensor noise model, where T in FIG. 5 is the sensor sampling period, and the output of each single axis sensor can be modeled as a combination of true angular velocity, angular random walk ARW, and angular rate random walk RRW, which can be unified into the same unit (deg/s) from the conversion relationship in FIG. 5.
FIG. 6 is a Kalman filtering flow chart of the array sensor, and the output of the calibration reference virtual sensor at the current moment can be obtained through iteration after each sampling by the method shown in FIG. 6.
The invention provides a field rapid calibration method of an array sensor, which belongs to the field of intelligent system navigation measurement, N sensors in an array except for the sensor to be calibrated form a virtual high-precision sensor, the sensor to be calibrated is calibrated, parameters of an accelerometer in the array are calibrated by static sampling data, parameters of a gyroscope in the array are calibrated by dynamic sampling data, and calibration results are checked to judge calibration effects. The method can realize the rapid calibration of the array sensor in real time on a task site, utilizes the correlation of the array sensor by combining a static test and a dynamic test, adopts a least square fitting method to estimate the optimal calibration parameters, corrects the zero offset and the scale factor of the IMU, can remarkably improve the navigation precision, greatly reduces the field use difficulty of the array sensor, and can ensure the reliability of the calibration by checking the calibration result.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (5)
1. The on-site rapid calibration method of the array sensor is characterized by comprising the following steps of:
s1: turning over at least 6 different positions of the array sensor, and simultaneously performing static sampling on each position for a first preset time length by using the array sensor to obtain static sampling data; the overturning position ensures that acceleration projection components are arranged on the x axis, the y axis and the z axis of all sensors in the array sensor;
s2: freely rotating the array sensor, and dynamically sampling for a second preset time length by using the array sensor to obtain dynamic sampling data; wherein, the free rotation ensures that all sensors in the array sensor have angular velocity projection components on the x axis, the y axis and the z axis;
s3: respectively selecting data with maximum acceleration of the sensor to be calibrated in the x axis, the y axis and the z axis from the static sampling data, and calculating zero offset and scale factors of accelerometers of the sensor to be calibrated by using the selected data and acceleration data of other sensors except the sensor to be calibrated in the static sampling data; calculating zero offset and scale factors of a gyroscope of the sensor to be calibrated by using angular velocity data of other sensors except the sensor to be calibrated in the dynamic sampling data; repeating the step S3 until all the sensors in the array type sensor are traversed;
s4: checking the zero offset of the accelerometers and gyroscopes of all sensors in the array sensor, and checking the scale factors of the accelerometers and gyroscopes of all sensors in the array sensor;
step S3, respectively selecting data with maximum acceleration of the sensor to be calibrated in the x axis, the y axis and the z axis from the static sampling data, and calculating zero offset and scale factors of accelerometers of the sensor to be calibrated by using the selected data and acceleration data of other sensors except the sensor to be calibrated in the static sampling data; calculating zero offset and scale factors of a gyroscope of a sensor to be calibrated by using angular velocity data of other sensors except the sensor to be calibrated in dynamic sampling data, wherein the method specifically comprises the following steps:
assuming that the array sensor includes n+1 sensors, the measurement equations for the gyroscopes or accelerometers for the x, y and z axes of the p-th sensor to be calibrated are:
wherein i is real-x 、i real-y And i real-z The theoretical values of the band dimensions of the x axis, the y axis and the z axis of the sensor to be calibrated are respectively represented and are determined by fusion of Kalman filters of N sensors except the sensor to be calibrated; measured value sensor px 、sensor py And sensor pz The method comprises the steps of respectively representing the output digital quantities without dimension of an x axis, a y axis and a z axis of a sensor to be calibrated; SF (sulfur hexafluoride) p x、SF p y and SF p z represents the scale factors of the x-axis, y-axis and z-axis of the p-th sensor to be calibrated, b p Indicating the zero offset, v, of the p-th sensor to be calibrated p Representing the residual error of the p-th sensor to be calibrated;
the measurement equation and the observation equation for the MEMS array composed of N sensors except the sensor to be calibrated are as follows:
Z(t)＝H·ω+v(t) (3)
wherein X (t) represents a state variable of the Kalman filter, the state variable is real acceleration or real angular velocity, and the state variable is 1 dimension; z (t) represents the output values of N sensors except the sensor to be calibrated; h is a measurement matrix, and represents the conversion relation between each sensor and the carrier system; ω represents the true acceleration or true angular velocity,n ω representing a mean value of 0 and a variance of q ω White noise of (a); f is zero matrix, ω (t) is process noise, v (t) is observation noise;
the Kalman filter equation is:
K(t)＝P(t)H T R -1 (5)
wherein K (t) represents the gain variation of the Kalman filter with time, and P (t) represents the estimated error variation of the Kalman filter with time; r is the covariance matrix of the measurement noise, expressed as:
wherein q n Representing the variance of ARW noise of the sensor to be calibrated, wherein ρ represents the cross correlation coefficient of the array sensor;
k (t) iteratively converges to a fixed value, which is obtained by:
c＝H T R -1 H (8)
wherein K is ∞ Represents the iterative convergence value, P, of the gain of the Kalman filter ∞ Representing an iterative convergence value of the estimated error of the Kalman filter;
the estimate of the Kalman filter state variable for continuous time using K (t) is:
discretizing the Kalman filtering state variable in continuous time, using zero-order maintenance, and assuming that the acceleration or angular velocity is constant in the whole sampling period to obtain:
wherein t is 0 The sampling interval is represented by the number of samples,representing the output of gyroscopes or accelerometers of virtual sensors, Z, from N sensors other than the sensor to be calibrated k+1 Representing the original output of N sensors except the sensor to be calibrated; performing linear least square fitting by utilizing the relation between the output of the sensor to be calibrated and the virtual sensor to obtain zero offset and scale factors of the sensor to be calibrated:
wherein,scale factor representing the sensor to be calibrated, < +.>Indicating the zero offset of the sensor to be calibrated.
2. The method for on-site rapid calibration of an array sensor according to claim 1, wherein in step S1, the array sensor is flipped at least 6 different positions, specifically comprising:
the method comprises the steps of turning over an array sensor to the x-axis vertical direction of all sensors, turning over the array sensor to the y-axis vertical direction of all sensors, turning over the array sensor to the z-axis vertical direction of all sensors, and turning over the array sensor to the z-axis vertical direction of all sensors.
3. The method for on-site rapid calibration of an array sensor according to claim 1, wherein in step S1, the first preset duration is at least 3 minutes.
4. The method for on-site rapid calibration of an array sensor according to claim 1, wherein in step S2, the second preset time period is at least 3 minutes.
5. The method for on-site rapid calibration of an array sensor according to claim 1, wherein step S4 is performed to check zero offset of accelerometers and gyroscopes of all sensors in the array sensor, and to check scale factors of accelerometers and gyroscopes of all sensors in the array sensor, and specifically comprises:
s41: standing the array type sensor, collecting an accelerometer output value and a gyro output value of each sensor in the array type sensor, checking the zero offset calibration effect of the accelerometer by utilizing the square sum of the accelerometer output values of each sensor to be equal to the gravity acceleration, and checking the zero offset calibration effect of the gyro by utilizing the gyro output value of each sensor to be zero;
s42: and the array sensor is put back to the original position after freely rotating, and the calibration effect of the scale factors of the accelerometer and the gyroscope is checked by carrying out navigation calculation on the accelerometer and the gyroscope.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10025891B1 (en) * | 2015-09-30 | 2018-07-17 | The United States Of America As Represented By The Secretary Of The Navy | Method of reducing random drift in the combined signal of an array of inertial sensors |
| CN109186633A (en) * | 2018-08-30 | 2019-01-11 | 衡阳市衡山科学城科技创新研究院有限公司 | A kind of field calibration method and system of duplex measurement device |
| CN110207724A (en) * | 2019-07-04 | 2019-09-06 | 苏州邈航科技有限公司 | IMU array warm scaling method and caliberating device entirely |
| CN110672127A (en) * | 2019-11-01 | 2020-01-10 | 苏州大学 | Real-time calibration method for array type MEMS magnetic sensor |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20160178657A9 (en) * | 2013-12-23 | 2016-06-23 | InvenSense, Incorporated | Systems and methods for sensor calibration |
| JP7147275B2 (en) * | 2018-06-04 | 2022-10-05 | 愛知製鋼株式会社 | How to calibrate the gyro sensor |
-
2021
- 2021-08-12 CN CN202110924029.9A patent/CN113790737B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10025891B1 (en) * | 2015-09-30 | 2018-07-17 | The United States Of America As Represented By The Secretary Of The Navy | Method of reducing random drift in the combined signal of an array of inertial sensors |
| CN109186633A (en) * | 2018-08-30 | 2019-01-11 | 衡阳市衡山科学城科技创新研究院有限公司 | A kind of field calibration method and system of duplex measurement device |
| CN110207724A (en) * | 2019-07-04 | 2019-09-06 | 苏州邈航科技有限公司 | IMU array warm scaling method and caliberating device entirely |
| CN110672127A (en) * | 2019-11-01 | 2020-01-10 | 苏州大学 | Real-time calibration method for array type MEMS magnetic sensor |
Non-Patent Citations (4)
| Title |
|---|
| On-the-field calibration of an array of sensors;Dorveaux Eric et al.;《IEEE》;6795-6802 * |
| Self-Calibration of Inertial Sensor Arrays;Carlsson Hakan et al.;《IEEE SENSORS JOURNAL》;第21卷(第6期);8451-8463 * |
| 基于MEMS阵列的虚拟陀螺的实现;臧雪岩等;《传感技术学报》;第32卷(第3期);339-345 * |
| 阵列式MEMS惯性传感器及噪声抑制研究;荆通;赵鹤鸣;;传感技术学报(第06期);5-10 * |
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