CN112985383A - Multi-gyroscope system based on mutual compensation - Google Patents
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Abstract
The utility model provides a virtual gyroscope module, this module includes gyro matrix (1), data acquisition module (2) and algorithm compensation module (3), data acquisition module (2) are used for the data output of the gyro matrix of high-speed collection to transmit for algorithm compensation module (3), algorithm compensation module is used for receiving the gyro matrix data of the high-speed collection of data acquisition module, algorithm compensation module (3) carry out random error compensation and optimal estimation to the gyro matrix data of receiving from data acquisition module, output virtual gyroscope data, send into back level attitude angle algorithm system.
Description
Technical Field
The invention relates to a sensing system, in particular to a gyroscope sensing system.
Background
In the field of precision agriculture, gyroscopes are often used in implement positioning and navigation systems including automatic harvesters and automatic pesticide sprayers; in the field of intelligent home furnishing, the application of the gyroscope mainly comprises an intelligent sweeping machine, a service robot and the like; in the fields of intelligent industry and intelligent logistics, a gyroscope is mainly applied to an unmanned aerial attitude reference system and unmanned aerial vehicle inertial navigation, and comprises an unmanned aerial vehicle attitude reference system, an aerial photography stabilizing system and an unmanned aerial vehicle express delivery system; in the field of car networking, gyroscopes are commonly used in intelligent management systems for vehicles and freight transportation, for example, the gyroscopes can monitor the position and the track of a vehicle and also can manage and ask for help for the attitude of the vehicle; in the field of intelligent medical treatment, common applications of gyroscopes include deaf-mute gesture recognition systems, biomimetic artificial limbs and the like.
The micromechanical gyroscope, i.e. the MEMS gyroscope, also called a silicon micro gyroscope, is manufactured by integrating a mechanical device and an electronic circuit on a tiny silicon chip by using the mature processes of deposition, etching, doping, and the like in semiconductor production, and finally, the micro gyroscope with the size of an integrated circuit chip is formed. The biggest short plate or fatal shortcoming of the MEMS gyroscope is that there is a large error, which results in a large attitude angle error (mainly YAW angle) of the final output, and poor stability, and cannot meet the requirements of some high-demand scenes. The method is simple, has small calculation amount, has limited error compensation, and is suitable for scenes with low requirements on precision and stability. In addition, the attitude angle is calculated by data fusion, an additional sensor such as an accelerometer or a magnetometer is required to be added in the method, the cost is increased, the calculated amount is relatively complex, but a certain compensation effect is realized on the error of the gyroscope, a certain precision can be ensured, and the method is suitable for scenes with requirements on the precision and the like. However, the above two methods cannot meet the requirements of application scenarios requiring high precision and high stability.
Disclosure of Invention
The invention provides a mutual compensation method based on multiple gyroscopes, which greatly compensates the uncertainty error of an MEMS gyroscope and improves the output stability and accuracy of the YAW angle of the attitude angle. The randomness of the error due to the gyro is objective and cannot be completely eliminated. The invention provides a method capable of effectively eliminating error randomness and improving the output stability and accuracy of attitude angles.
In probability theory and statistics, the mathematical expectation (mean) (or mean, also called expectation for short) is the probability of each possible result in an experiment multiplied by the sum of the results, and is one of the most basic mathematical features. It reflects the magnitude of the average value of the random variable. The present invention proposes the mathematical expectation of using multiple gyroscopes as gyroscope data that ultimately enters the algorithm for data fusion. In addition, in the attitude angle algorithm system, the accuracy of the attitude output attitude angle (YAW) angle can also be improved by respectively preprocessing each path of acquired data of the virtual gyroscope, the accelerometer and the magnetometer and then sending the preprocessed data to the attitude angle resolving and data fusion processing module. Thus, the output angular rate obtained by redundant measurements and optimal estimation can be more stable than the performance of any single element.
The invention provides a virtual gyroscope module. The module comprises a gyro matrix (1), a data acquisition module (2) and an algorithm compensation module (3). The gyroscope matrix (1) is composed of N gyroscopes (N is an integer greater than or equal to 2), and the N gyroscopes can be gyroscopes of different types and different manufacturers or gyroscopes of the same type and the same manufacturer. And the data acquisition module (2) is used for acquiring data output of the N gyroscopes at a high speed and transmitting the data output to the following algorithm compensation module (3). And the algorithm compensation module is used for receiving the N paths of gyroscope data acquired by the data acquisition module at high speed and outputting the virtual gyroscope data through random error compensation and optimal estimation. And the output virtual gyroscope data is provided for a subsequent attitude angle algorithm system. The algorithm compensation module comprises a random error model module (4) and an optimal estimation module (5). And the random error model module in the algorithm compensation module is used for receiving the N paths of gyroscope data acquired by the data acquisition module at a high speed, performing smooth filtering and data processing on the N paths of gyroscope data, sending the data into the optimal estimation module after the smooth filtering processing of the random error model module, and performing weighted average on the data by the optimal estimation module by adopting a mathematical expectation method to obtain final optimal estimation output to serve as the output of the virtual gyroscope.
The invention provides an attitude angle algorithm system which comprises a sensor input module, a data acquisition module and an attitude angle calculation module. Wherein the sensor input module comprises a virtual gyroscope module, an accelerometer, and a magnetometer. The attitude angle calculation module (3) comprises a data preprocessing module (4) and an attitude calculation and data fusion processing module (5). The data acquisition module is used for acquiring output data of the virtual gyroscope module, the accelerometer and the magnetometer. The data acquisition module sends the acquired data to the data preprocessing module for corresponding calibration filtering, normal value offset reduction and other processing.
In the data preprocessing module, for data acquired from the accelerometer, firstly, the acquired data Rawa is subjected to calibration coefficient operation to generate Calibrated data, and then the preprocessed acceleration data Acc is obtained through one-dimensional Kalman filtering processing.
In the data preprocessing module, for data acquired from the virtual gyroscope module, the raw data Rawg is subjected to calibration coefficient operation to generate Calibrated data, and then the Calibrated data is subjected to filtering processing by the real-time calibration module to obtain preprocessed virtual gyroscope data Gyro.
In the data preprocessing module, aiming at data collected from the magnetometer, firstly, the collected data Rawm is subjected to calibration coefficient operation to generate Calibrated data, and then the preprocessed magnetometer output data Mag is obtained through one-dimensional Kalman filtering processing.
Acc/Gyro/Mag respectively represents accelerometer, virtual gyroscope module and magnetometer data obtained after being respectively processed by the preprocessing module.
And the data of the accelerometer, the virtual gyroscope and the magnetometer obtained after the data preprocessing module is processed are sent to an attitude calculation and data fusion module, and high-precision attitude angle values are finally output through the attitude calculation and data fusion processing. And the attitude angle output mainly comprises Roll angle (Roll), Pitch angle (Pitch) and course angle (Yaw) output, and information such as quaternion for 3D image display can also be output.
Drawings
FIG. 1 virtual gyroscope Module Structure
FIG. 2 attitude angle algorithm system
Detailed Description
Fig. 1 is a virtual gyroscope module architecture. The module comprises a gyro matrix (1), a data acquisition module (2) and an algorithm compensation module (3). The gyro matrix (1) is composed of N gyros (N is an integer greater than or equal to 2), and the N gyros can be different types of gyros of different manufacturers or the same type of gyros of the same manufacturer. And the data acquisition module (2) is used for acquiring data output of the gyroscope at a high speed and transmitting the data output to the following algorithm compensation module (3). The algorithm compensation module is used for receiving the N paths of gyroscope data acquired by the data acquisition module at a high speed, and outputting virtual gyroscope data to the attitude angle algorithm system through random error compensation and optimal estimation. The algorithm compensation module comprises a random error model module (4) and an optimal estimation module (5). And the random error model module in the algorithm compensation module is used for receiving the N paths of gyroscope data acquired at high speed by the data acquisition module, and performing smooth filtering and data processing on the received N paths of gyroscope data to acquire the optimal estimation value of the gyroscope. In the random error model module, a plurality of sliding windows (window size m) are designed to carry out smooth filtering processing on each path of gyro data. The following formula may be employed:
G1＝[G1(t0)+G1(t1)+…+G1(tm)]/m；
G2＝[G2(t0)+G2(t1)+…+G2(tm)]/m；
……
Gn＝[Gn(t0)+Gn(t1)+…+Gn(tm)]/m；
……
GN＝[GN(t0)+GN(t1)+…+GN(tm)]/m；
in the above formula, the first and second light sources are,
-t0, t1, … tm are respectively different sampling instants;
-gn (tm) is the sample value of the n-th gyroscope at time tm;
and Gn is the output of the n-th path of gyroscope data after the smoothing filtering processing.
N paths of gyroscope data acquired by the data acquisition module (2) are subjected to smoothing filtering processing by the filtering random error model module and then sent to the optimal estimation module, the optimal estimation module performs probability analysis on the data subjected to smoothing filtering, the input data is evaluated and calculated by the probability analysis method, the optimal gyroscope output is acquired and sent to the post-stage attitude angle algorithm system as the virtual gyroscope output.
In the optimal estimation module, the probability analysis principle is utilized, a mathematical expectation method is adopted, the N paths of gyroscope data after smooth filtering are weighted and averaged to obtain the final optimal estimation output, and the following formula is adopted:
G＝E[G1,G2,…Gn…GN]
in the above formula:
gn represents the output of the nth gyroscope after smoothing and filtering.
E represents the mathematical expectation of all N gyroscope outputs.
And G is the final optimal estimation output, namely the output of the virtual gyroscope.
FIG. 2 is a schematic diagram of an attitude angle algorithm system. The system comprises a sensor input module, a data acquisition module and an attitude angle calculation module. The sensor input module includes a virtual gyroscope module, an accelerometer, and a magnetometer, and the virtual gyroscope module is the virtual gyroscope module shown in fig. 1. The attitude angle calculation module (3) comprises a data preprocessing module (4) and an attitude calculation and data fusion processing module (5). The data acquisition module is used for acquiring output data of the virtual gyroscope module, the accelerometer and the magnetometer. That is, the optimal estimated values of the N-way gyroscopes, the output values of the accelerometers and the output values of the magnetometers output by the virtual gyroscope module system shown in fig. 1 are transmitted to the data acquisition module. And the data acquisition module is used for acquiring data output of the virtual gyroscope module, the accelerometer and the magnetometer at a high speed and transmitting the data output to the subsequent data preprocessing module (4). And the acquired data enters a data preprocessing module, corresponding calibration filtering is carried out, and the offset of a constant value is reduced.
In the data preprocessing module, for data acquired from the accelerometer, firstly, the acquired data Rawa is subjected to calibration coefficient operation to generate Calibrated data, and then the preprocessed acceleration data Acc is obtained through one-dimensional Kalman filtering processing. The calculation formula is as follows: acc ═ FK (CoeffKa × Rawa + CoeffBa).
In the data preprocessing module, for data acquired from the virtual gyroscope module, the raw data Rawg is subjected to calibration coefficient operation to generate Calibrated data, and then the Calibrated data is subjected to filtering processing by the real-time calibration module to obtain preprocessed output virtual gyroscope data Gyro. The calculation formula is as follows: gyro ═ CoeffKg ═ Rawg + CoeffBg-Gyro Bias
In the data preprocessing module, aiming at data collected from the magnetometer, firstly, the collected data Rawm is subjected to calibration coefficient operation to generate Calibrated data, and then the preprocessed magnetometer output data Mag is obtained through one-dimensional Kalman filtering processing. The calculation formula is as follows: mag ═ FK (CoeffKm × Rawm + CoeffBm)
The parameters in the above formula are explained:
Acc/Gyro/Mag respectively represents accelerometer, virtual gyroscope and magnetometer data which are obtained after being respectively processed by the preprocessing module.
FK () represents a kalman processing function.
CoeffK, CoeffB denote two parts of the calibration coefficient.
CoeffKa, CoeffBa denote the calibration coefficients of the accelerometer.
CoeffKg, CoeffBg represent the calibration coefficients of the gyroscope.
CoeffKm, CoeffBm represent calibration coefficients for the magnetometer.
GyroBias represents the output of the gyroscope real-time calibration module.
And the data of the accelerometer, the virtual gyroscope and the magnetometer obtained after the data preprocessing module is processed are sent to an attitude calculation and data fusion module (5), and high-precision attitude angle values are finally output through the attitude calculation and data fusion processing. And the attitude angle output mainly comprises Roll angle (Roll), Pitch angle (Pitch) and course angle (Yaw) output, and information such as quaternion for 3D image display can also be output. The attitude calculation and data fusion processing of the module mainly adopts a conventional strapdown inertial navigation algorithm, and is not described in detail here.
The technical scheme of the invention can accurately estimate the error of the GYRO uncertainty, and further greatly improve the precision of the attitude output attitude angle (YAW) angle through compensation.
In the embodiments of the present invention, the Processor may be a general-purpose Processor, such as but not limited to a Central Processing Unit (CPU), or may be a special-purpose Processor, such as but not limited to a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA), and so on. Further, the processor may be a combination of a plurality of processors.
Those of ordinary skill in the art will appreciate that the various illustrative modules and method steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described device embodiments are merely illustrative, and for example, the division of the units is only one logical functional division, and other divisions may be realized in practice.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention.
Claims (9)
1. The utility model provides a virtual gyroscope module, this module includes gyro matrix (1), data acquisition module (2) and algorithm compensation module (3), data acquisition module (2) are used for the data output of the gyro matrix of high-speed collection to transmit for algorithm compensation module (3), algorithm compensation module is used for receiving the gyro matrix data of the high-speed collection of data acquisition module, algorithm compensation module (3) carry out random error compensation and optimal estimation to the gyro matrix data of receiving from data acquisition module, output virtual gyroscope data.
2. The virtual gyroscope module of claim 1, the gyroscope matrix comprising N gyroscopes, the N gyroscopes being of the same type or of different types, where N is an integer greater than or equal to 2.
3. The virtual gyroscope module of claim 2, wherein the algorithm compensation module comprises a random error model module (4) and an optimal estimation module (5), the random error model module is used for receiving the gyro matrix data acquired by the data acquisition module at a high speed, performing smoothing filtering and data processing on the received gyro matrix, sending the data into the optimal estimation module after the smoothing filtering and data processing, the optimal estimation module performs probability analysis on the data after the smoothing filtering and data processing, and the optimal estimation module performs weighted average on the gyro matrix data after the smoothing filtering and data processing by adopting a mathematical expectation method to obtain optimal estimation.
4. The virtual gyroscope module of claim 3, wherein the random error model module designs a plurality of sliding windows, the window size is m, and each path of gyroscope data is subjected to smoothing filtering processing by using the following formula:
G1＝[G1(t0)+G1(t1)+…+G1(tm)]/m；
G2＝[G2(t0)+G2(t1)+…+G2(tm)]/m；
……
Gn＝[Gn(t0)+Gn(t1)+…+Gn(tm)]/m；
……
GN＝[GN(t0)+GN(t1)+…+GN(tm)]/m；
in the above formula, the first and second light sources are,
-t0, t1, … tm are respectively different sampling instants;
-gn (tm) is the sample value of the n-th gyroscope at time tm;
and Gn is the output of the n-th path of gyroscope data after the smoothing filtering processing.
5. The virtual gyroscope module as claimed in claim 4, wherein the N gyroscope data acquired by the data acquisition module (2) are processed by smoothing filtering of the filtering stochastic error model module and then sent to the optimal estimation module, the optimal estimation module adopts a mathematical expectation method to obtain a final optimal estimation output by weighted average of the N gyroscope data after being processed by smoothing filtering, and G is E [ G1, G2, … Gn … Gn ] in the above formula: gn represents the output of the nth path of gyroscope after smoothing filtering; e represents the mathematical expectation of all the N paths of gyroscope outputs; and G is the final optimal estimation output, namely the virtual gyro output.
6. An attitude angle algorithm system comprises a sensor input module, a data acquisition module and an attitude angle calculation module, wherein the sensor input module comprises the virtual gyroscope module, the accelerometer and the magnetometer of claims 1 to 5, the attitude angle calculation module (3) comprises a data preprocessing module (4), an attitude calculation and data fusion processing module (5), and the data acquisition module is used for acquiring the output data of the virtual gyroscope module, the accelerometer and the magnetometer at a high speed, and the data are transmitted to a data preprocessing module (4), the data preprocessing module carries out corresponding calibration filtering on each received path of data, the accelerometer, the virtual gyroscope and the magnetometer data obtained after the data are processed by the data preprocessing module are sent to an attitude calculation and data fusion module, and high-precision attitude angle values are output through the attitude calculation and data fusion processing.
7. The attitude angle algorithm system according to claim 6, wherein the data preprocessing module performs the following processing on the output data of the virtual gyroscope module, the accelerometer, and the magnetometer acquired at a high speed: for data collected from an accelerometer, firstly, calculating collected accelerometer data Rawa through a calibration coefficient to generate Calibrated data, and then, performing one-dimensional Kalman filtering to obtain preprocessed acceleration data Acc; aiming at data collected from a virtual gyroscope module, firstly, calculating original data Rawg of the virtual gyroscope module through a calibration coefficient to generate calibrated data, and then, filtering the calibrated data through a real-time calibration module to obtain preprocessed virtual gyroscope data Gyro; after data Rawm collected from the magnetometer is subjected to calibration coefficient operation, calibrated data are generated, and preprocessed magnetometer output data Mag are obtained through one-dimensional Kalman filtering processing; Acc/Gyro/Mag respectively represents accelerometer, virtual gyroscope module and magnetometer data which are obtained after being processed by the preprocessing module.
8. The attitude angle algorithm system according to claim 7, wherein in the data preprocessing module, with respect to the data collected from the accelerometer, firstly, the collected accelerometer data Rawa is subjected to a calibration coefficient operation, and then calibrated data is generated, and then a one-dimensional Kalman filtering process is performed to obtain preprocessed acceleration data Acc, wherein the formula Acc ═ FK (CoeffKa × Rawa + CoeffBa) is used for calculation; for data collected from the virtual gyroscope module, firstly, calculating original data Rawg of the virtual gyroscope module through a calibration coefficient to generate calibrated data, and then, obtaining preprocessed output virtual gyroscope data Gyro through filtering processing of a real-time calibration module, wherein a formula Gyro ═ CoeffKg × Rawg + CoeffBg-Gyro Bias is adopted for calculation; the method comprises the steps of calculating data Rawm collected from a magnetometer through a calibration coefficient, generating calibrated data, and obtaining preprocessed magnetometer output data Mag through one-dimensional Kalman filtering, wherein a formula Mag is calculated by FK (CoeffKm Rawm + CoeffBm), FK () represents a Kalman processing function, CoeffK and CoeffB represent two parts of the calibration coefficient respectively, CoeffKa and CoeffBa represent the calibration coefficient of the accelerometer respectively, CoeffKg and CoeffBg represent the calibration coefficient of a gyroscope respectively, CoeffKm and CoeffBm represent the calibration coefficient of the magnetometer respectively, and GyrcoBias represents the real-time calibration output of the gyroscope.
9. The attitude angle algorithm system of claim 7, wherein the attitude angle numerical outputs include Roll angle (Roll), Pitch angle (Pitch), and heading angle (Yaw) outputs.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104081313A (en) * | 2011-11-11 | 2014-10-01 | 高通股份有限公司 | Sensor auto-calibration |
| US9217639B1 (en) * | 2012-03-20 | 2015-12-22 | Moog Inc. | North-finding using inertial navigation system |
| CN107830871A (en) * | 2017-10-12 | 2018-03-23 | 歌尔科技有限公司 | A kind of method, apparatus, gyroscope and system for compensating gyroscope angular velocity data |
| CN108180905A (en) * | 2018-01-04 | 2018-06-19 | 北京原子机器人科技有限公司 | The signal conditioner and method of intelligent inertial navigation system |
| CN108801292A (en) * | 2017-04-27 | 2018-11-13 | 成都虚拟世界科技有限公司 | A kind of gyro data calibration method and computer readable storage medium |
-
2019
- 2019-12-14 CN CN201911299081.9A patent/CN112985383A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104081313A (en) * | 2011-11-11 | 2014-10-01 | 高通股份有限公司 | Sensor auto-calibration |
| US9217639B1 (en) * | 2012-03-20 | 2015-12-22 | Moog Inc. | North-finding using inertial navigation system |
| CN108801292A (en) * | 2017-04-27 | 2018-11-13 | 成都虚拟世界科技有限公司 | A kind of gyro data calibration method and computer readable storage medium |
| CN107830871A (en) * | 2017-10-12 | 2018-03-23 | 歌尔科技有限公司 | A kind of method, apparatus, gyroscope and system for compensating gyroscope angular velocity data |
| CN108180905A (en) * | 2018-01-04 | 2018-06-19 | 北京原子机器人科技有限公司 | The signal conditioner and method of intelligent inertial navigation system |
Non-Patent Citations (4)
| Title |
|---|
| 周存忠主编: "《地震词典》", 上海辞书出版社, pages: 509 - 514 * |
| 夏克寒 等，: "余度捷联惯性测量装置中的数据融合方法", 《计算机测量与控制》 * |
| 夏克寒 等，: "余度捷联惯性测量装置中的数据融合方法", 《计算机测量与控制》, vol. 12, no. 7, 31 July 2004 (2004-07-31), pages 601 - 603 * |
| 曲从善 等，: "基于ARM的多陀螺融合算法仿真研究", 《计算机测量与控制》, vol. 15, no. 12, 31 December 2007 (2007-12-31), pages 1804 - 1806 * |
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