CN114279449A - Attitude estimation method considering temperature drift error of accelerometer - Google Patents
Attitude estimation method considering temperature drift error of accelerometer Download PDFInfo
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Abstract
The invention provides an attitude estimation method considering an accelerometer temperature drift error, which is characterized in that when a GPS signal exists, a temperature proportionality coefficient k and a zero offset are estimated on line through Kalman filtering; when there is no GPS signal, firstly, the scale coefficient k and zero offset estimated before the GPS signal is interrupted are used for carrying out temperature compensation on the acceleration; secondly, integrating the acceleration obtained after temperature compensation for one time to obtain a speed parameter; thirdly, integrating the speed parameters for the first time to obtain position parameters, and calculating an attitude angle; and finally, updating the orientation cosine array. The invention improves the positioning precision, and updates the attitude matrix by compensating the temperature error when the vehicle is static, thereby improving the attitude estimation precision.
Description
Technical Field
The invention belongs to the technical field of attitude estimation of moving objects, and particularly relates to an attitude estimation method considering temperature drift errors of an accelerometer, in particular to a method for improving the accuracy of attitude estimation in a stationary state.
Background
The accelerometer is used for detecting independent triaxial acceleration signals of a measured object in a carrier coordinate system, and consists of a detection mass (also called a sensitive mass), a support, a potentiometer, a spring, a damper and a shell. The precision of the accelerometer can directly influence the positioning precision, although the accelerometer has the advantages of good environmental performance (impact, vibration and temperature), low cost and the like, in the actual processing process, the acceleration per se has certain errors due to inevitable reasons such as installation errors and the like, in addition, the errors are increased due to heat generated in the running process of the acceleration, and the errors are accumulated along with the time. In order to solve the problems of the traditional methods, many people may select expensive components to improve the accuracy, or perform off-line estimation after data acquisition, but the methods are not suitable in consideration of the problem of economic time cost, so that the method compensates the temperature error through an on-line estimation method which is not used before, not only can improve the attitude estimation accuracy when the GPS signal is interrupted, but also can calculate more accurate speed and position parameters through integration.
Disclosure of Invention
The invention aims to overcome the defects in the prior art, and provides an attitude estimation method considering accelerometer temperature drift errors.
The purpose of the invention can be realized by the following technical scheme:
because the accelerometer is an electronic component, heat is generated only by working, and the following relation exists between the acceleration and the temperature through curve fitting of a large amount of data:
where k is the proportionality coefficient and T is the temperature.
In the original study, only the zero offset was considered as the state estimator, and the equation is as follows:
wherein
Is zero offset.
But actually, because the estimation is not accurate due to the temperature drift error, now the temperature error is also used as a state estimator, and the zero offset-temperature error model is established as follows:
the goal is to solve for the estimated value of k so that acceleration can be better compensated for when the GPS is interrupted.
In order to better estimate the value of k, a Kalman filter is fused:
considering that each parameter of the state estimator is different in different systems, the aim is to find the proportionality coefficient k and the zero bias
To compensate for acceleration, kalman filtering is fused, where the state estimator is generally written as:
let x be [ phi ]E；φN；φU；δvE；δvN；δvU]
Wherein: phi is aEIs the east misalignment angle phiNIs the north misalignment angle phiUAngle of day misalignment, δ vEFor east velocity error, δ vNFor north velocity error, δ vUIs the speed error in the sky direction;
then:
wherein:
zero bias, k is a proportionality coefficient;
the zero offset-temperature error is developed into the equation of state as follows:
wherein:
is the first derivative;
the measurement equation is as follows:
the state analysis and fusion Kalman filtering technology specifically comprises the following steps:
system state vector:
system state transition matrix:
a system measurement matrix:
H1＝[B 1 1]
calculating a state prediction:
state one-step prediction mean square error matrix:
filtering gain vector:
state estimation vector update:
state estimation mean square error update:
p1＝(I-K1H1)p1/0
wherein:
zero bias, k is the scaling factor, A, B is the state matrix, Q1In order for the process noise variance to be an equation of state,
is a state prediction value, phi0For the last time the system state transition matrix,
is the system state quantity, p, at the last moment1/0As a predictor of the covariance matrix, K1As Kalman filter gain, R1For measuring the variance of the noise in the process, Z1To measure the observed quantity of the current system by the sensor, I is an identity matrix.
According to
The desired proportionality coefficient k and zero offset can be obtained
When there is no GPS signal, the proportional coefficient k is offset from zero
Used to compensate for acceleration, and then twice integrated to obtain velocity and position parameters. Meanwhile, when the vehicle is stationary, the estimation accuracy of the attitude matrix can be improved by using the compensated acceleration:
when the inertial system is just powered on and started, the direction of each axis of the carrier coordinate system relative to the reference navigation coordinate system is completely unknown or not accurate enough, and navigation cannot be immediately carried out, so that the spatial direction of the carrier coordinate system relative to the navigation coordinate system must be determined firstly, namely, the direction cosine array between the two coordinate systems is solved
Wherein b represents the carrier system and n represents the navigation reference coordinate system. In the conventional method, the error generated by the accelerometer due to the influence of temperature is not considered, and the formula is shown
The following were used:
wherein:
is the angular velocity output by the gyroscope,
is the acceleration output by the accelerometer(s),
for a carrier coordinate system and a reference seatDirectional cosine arrays between the standards, in addition: b represents a carrier coordinate system, and n represents a navigation reference coordinate system;
after considering the error generated by the accelerometer due to the influence of temperature, the equation is updated as follows:
acceleration compensation:
orientation cosine array
Updating:
after the updating, the attitude estimation precision can be theoretically improved.
Drawings
FIG. 1 is an exemplary graph of acceleration error versus time;
FIG. 2 is an exemplary graph of temperature versus time;
FIG. 3 is an exemplary graph of temperature versus acceleration error;
FIG. 4 is a schematic diagram of an attitude estimation method of the present invention that takes into account accelerometer temperature drift errors;
FIG. 5 is a flow chart of an attitude estimation method in consideration of accelerometer temperature drift error according to the present invention.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments. The present embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation manner and a specific operation process are given, but the scope of the present invention is not limited to the following embodiments.
As shown in fig. 4 and 5, an attitude estimation method considering accelerometer temperature drift error includes a zero offset-temperature error equation:
kalman filtering module based on GPS signalsThe signal and the structural state quantity are fused, and the scaling coefficient k and the zero offset are estimated on line according to the following steps
System state vector:
system state transition matrix:
a system measurement matrix:
H1＝[B 1 1]
calculating a state prediction:
calculating a state one-step prediction mean square error matrix:
calculating a Kalman filtering gain vector:
calculating state estimation vector update:
calculating state estimation mean square error update:
p1＝(I-K1H1)p1/0
wherein:
zero bias, k is the scaling factor, A, B is the state matrix, Q1In order for the process noise variance to be an equation of state,
is a state prediction value, phi0For the last time the system state transition matrix,
is the system state quantity, p, at the last moment1/0As a predictor of the covariance matrix, K1As Kalman filter gain, R1For measuring the variance of the noise in the process, Z1To measure the observed quantity of the current system by the sensor, I is an identity matrix.
According to XkThe desired proportionality coefficient k and zero offset can be obtained
Outputting the attitude angle, the speed and the position p of the carrier according to the state quantity at the latest moment; when the GPS signal is interrupted, the scale coefficient k and zero offset estimated at the moment before the GPS interruption are used for carrying out temperature compensation on the acceleration; integrating the acceleration obtained after temperature compensation for one time to obtain a speed parameter; integrating the speed parameter for one time to obtain a position parameter; and when the attitude estimation device is static, the temperature error of the measurement value of the accelerometer is compensated, the orientation cosine array is updated, and the attitude estimation precision is improved.
From the error versus time curve of fig. 1, it can be seen that: the acceleration error increases continuously as time accumulates.
From the temperature vs. time curve of fig. 2 it can be seen that: with increasing time, the temperature also rises continuously.
From the temperature versus error curve of fig. 3, it can be seen that: there is a nearly linear relationship between temperature and acceleration error.
The foregoing detailed description of the preferred embodiments of the invention has been presented. It should be understood that numerous modifications and variations could be devised by those skilled in the art in light of the present teachings without departing from the inventive concepts. Therefore, the technical solutions available to those skilled in the art through logic analysis, reasoning and limited experiments based on the prior art according to the concept of the present invention should be within the scope of protection defined by the claims.
Claims (4)
1. An attitude estimation method taking into account temperature drift errors of an accelerometer, characterized by:
the data collected by the accelerometer are subjected to curve fitting, and the following relation exists between the acceleration and the temperature:
wherein: k is a proportionality coefficient, T is temperature, and a is acceleration;
in order to better estimate the value of k, kalman filtering is fused, where the state estimator is written as:
recording: x is ═ phiE；φN；φU；δvE；δvN；δvU]Wherein: phi is aEIs the east misalignment angle phiNIs the north misalignment angle phiUAngle of day misalignment, δ vEFor east velocity error, δ vNFor north velocity error, δ vUIs the error of the speed in the direction of the sky
To facilitate the salient analysis, the state estimator X is rewritten as:
wherein:
zero bias, k is a proportionality coefficient;
the zero offset-temperature error is developed into the equation of state as follows:
wherein:
is the first derivative;
the measurement equation is as follows:
2. the attitude estimation method considering the temperature drift error of the accelerometer according to claim 1, wherein the kalman filtering is specifically:
system state vector:
system state transition matrix:
a system measurement matrix:
H1＝[B 1 1]
calculating a state prediction:
calculating a state one-step prediction mean square error matrix:
calculating a Kalman filtering gain vector:
calculating state estimation vector update:
calculating state estimation mean square error update:
p1＝(I-K1H1)p1/0
wherein:
zero bias, k is the scaling factor, A, B is the state matrix, Q1In order for the process noise variance to be an equation of state,
is a state prediction value, phi0For the last time the system state transition matrix,
is the system state quantity, p, at the last moment1/0As a predictor of the covariance matrix, K1As Kalman filter gain, R1For measuring the variance of the noise in the process, Z1To measure the observed quantity of the current system by the sensor, I is an identity matrix.
3. The attitude estimation method taking into account the temperature drift error of the accelerometer of claim 2, wherein:
according to
Obtaining a proportionality coefficient k and a zero offset
In the absence of GPS signals, the scaling factor k and zero offset are used to compensate for acceleration, and then twice integrated to obtain velocity and position parameters.
4. The attitude estimation method taking into account the temperature drift error of the accelerometer of claim 2, wherein:
according to
Obtaining a proportionality coefficient k and a zero offset
When the vehicle is static, estimating a direction cosine array with higher precision, and improving the attitude estimation precision;
after considering the error generated by the accelerometer due to the influence of temperature, the calculation formula is as follows:
acceleration compensation:
orientation cosine array
Updating:
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| CN108168574A (en) * | 2017-11-23 | 2018-06-15 | 东南大学 | A kind of 8 position Strapdown Inertial Navigation System grade scaling methods based on speed observation |
| CN109141479A (en) * | 2018-10-30 | 2019-01-04 | 中国船舶重工集团公司第七0七研究所 | A kind of system-level accelerometer temperature compensation method |
| CN113029199A (en) * | 2021-03-15 | 2021-06-25 | 中国人民解放军国防科技大学 | System-level temperature error compensation method of laser gyro inertial navigation system |
| CN113203429A (en) * | 2021-04-02 | 2021-08-03 | 同济大学 | Online estimation and compensation method for temperature drift error of gyroscope |
| CN113203418A (en) * | 2021-04-20 | 2021-08-03 | 同济大学 | GNSSINS visual fusion positioning method and system based on sequential Kalman filtering |
| CN113511080A (en) * | 2021-05-24 | 2021-10-19 | 南昌智能新能源汽车研究院 | Electric automobile starting condition analysis method considering double-layer vibration isolation |
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Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108168574A (en) * | 2017-11-23 | 2018-06-15 | 东南大学 | A kind of 8 position Strapdown Inertial Navigation System grade scaling methods based on speed observation |
| CN109141479A (en) * | 2018-10-30 | 2019-01-04 | 中国船舶重工集团公司第七0七研究所 | A kind of system-level accelerometer temperature compensation method |
| CN113029199A (en) * | 2021-03-15 | 2021-06-25 | 中国人民解放军国防科技大学 | System-level temperature error compensation method of laser gyro inertial navigation system |
| CN113203429A (en) * | 2021-04-02 | 2021-08-03 | 同济大学 | Online estimation and compensation method for temperature drift error of gyroscope |
| CN113203418A (en) * | 2021-04-20 | 2021-08-03 | 同济大学 | GNSSINS visual fusion positioning method and system based on sequential Kalman filtering |
| CN113511080A (en) * | 2021-05-24 | 2021-10-19 | 南昌智能新能源汽车研究院 | Electric automobile starting condition analysis method considering double-layer vibration isolation |
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