CN114018279B - Multi-sampling-rate data fusion posture correction method for array sensor - Google Patents
Multi-sampling-rate data fusion posture correction method for array sensor Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C23/00—Combined instruments indicating more than one navigational value, e.g. for aircraft; Combined measuring devices for measuring two or more variables of movement, e.g. distance, speed or acceleration
- G01C23/005—Flight directors
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
- G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
- G01C21/16—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
- G01C21/183—Compensation of inertial measurements, e.g. for temperature effects
- G01C21/188—Compensation of inertial measurements, e.g. for temperature effects for accumulated errors, e.g. by coupling inertial systems with absolute positioning systems
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Abstract
The invention discloses a multi-sampling rate data fusion posture correction method of an array sensor, which comprises the steps of sampling values of a gyroscope and an accelerometer in an array at the multi-sampling rate; zero-speed judgment is carried out on data obtained by different sampling rates, and the combined acceleration measured by the triaxial accelerometer is compared with the gravity acceleration to obtain the credibility of the horizontal attitude angle calculated by the accelerometer; obtaining a measurement error estimated value of the horizontal attitude by comparing accelerometer measured values with different sampling rates with the previous strapdown matrix, and carrying out weighted fusion on the accelerometer data according to the credibility of the accelerometer data with different sampling rates; and the horizontal attitude angle variation obtained by the accelerometer is fused with the angular speed measured by the gyroscope to obtain a final attitude angle.
Description
Technical Field
The invention belongs to the technical field of navigation measurement of an array sensor, and particularly relates to a multi-sampling rate data fusion posture correction method of an array sensor.
Background
Along with the rapid development of science and technology, the research and development of precise striking weapons with high precision and intelligent vehicles, unmanned aerial vehicles and high precision are not separated from an inertial navigation system, and the inertial navigation system provides precise attitude and position information for the navigation and positioning of intelligent and automatic machines, 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.
After the engine of the aircraft is shut down, the acceleration is mainly caused by attitude adjustment and flight resistance, the resistance is basically negligible when the engine is in a rarefaction atmosphere or above, the attitude adjustment is mainly caused, and at the moment, the attitude is directly updated by a gyroscope, and attitude errors are accumulated due to divergence along with time, so that a correction method is required to be introduced. The invention provides a correction method for correcting the gesture by adopting a multi-sampling rate of an accelerometer array, and because the maneuvering acceleration of an unmanned aerial vehicle generally belongs to a high-frequency signal, different sub-sensors in the sensor array are respectively sampled in real time by adopting 20Hz, 10Hz, 2Hz and 1Hz, and compared with a previous strapdown matrix, a measurement error estimated value of the horizontal gesture is obtained, the estimated value is weighted and fused to obtain a final estimated value, and meanwhile, the angular velocity measured by a gyroscope is corrected to obtain fused gesture information.
The multi-sampling rate data fusion posture correction method of the array type sensor fully utilizes parameters of each sensor in the array type sensor, and effectively improves posture measurement accuracy. Research on attitude measurement techniques for array sensors would benefit not only the aircraft control domain, but also the navigation measurement related fields of array sensors. Therefore, research on this technology is of great importance.
Disclosure of Invention
Because of errors such as zero offset, random walk and the like in the gyroscope in unmanned aerial vehicle attitude measurement, accumulated errors are easily introduced in the calculation of an attitude angle; the accelerometer can obtain a horizontal attitude angle with no error accumulation along with time, however, the attitude angle is easily influenced by the acceleration of the carrier, so that the optimal attitude angle can be obtained by fusing the attitude angles measured by the gyroscope and the accelerometer. And (3) carrying out multi-sampling-rate acceleration measurement by utilizing different accelerometers in the array, obtaining a measurement error estimated value of the horizontal attitude by comparing with the previous strapdown matrix, carrying out weighted fusion on the variation, and obtaining the optimal estimation of the attitude angle by combining the measured value of the gyroscope. The specific technical scheme of the invention is as follows:
a multi-sampling rate data fusion posture correction method of an array sensor comprises the following steps:
s1: sampling the gyroscope and accelerometer measurements in the array sensor at a plurality of sampling rates;
s2: performing zero-speed judgment on the data obtained in the step S1, and comparing the combined acceleration measured by the accelerometer with the gravity acceleration to obtain the reliability of the horizontal attitude angle calculated by the accelerometer;
s3: comparing the measured value of the accelerometer obtained in the step S1 with the gravity acceleration to obtain a measured error estimated value of the horizontal attitude, and carrying out weighted fusion on the measured value of the accelerometer according to the credibility of the accelerometer data under the corresponding sampling rate;
s4: and the final attitude angle is obtained by fusing the measurement error estimated value of the horizontal attitude angle obtained by the accelerometer and the angular speed measured by the gyroscope.
Further, the number of sampling rates is determined by the number of sensors n in the array sensor.
Further, the specific process of step S2 is as follows:
judging the maneuvering state of the carrier of the current array sensor for each sampling rate, comparing the difference value between the module of the triaxial measurement value of the accelerometer and the gravity acceleration, and taking the difference value as the weight of the corresponding sampling rate, wherein the larger the difference value is, the lower the weight is:
wherein ,the weight of the triaxial measurement value of the accelerometer at the s sampling rate; ax (ax) s ,ay s ,az s G is the gravity acceleration, which is the measured value of the accelerometer of the x axis, the y axis and the z axis under the s sampling rate;
normalizing the weights occupied by the triaxial measurement values of the accelerometer at different sampling rates:
wherein ,Ks The weight of the accelerometer measurement value at the normalized s sampling rate is as followss 1 For the first sampling rate, s n Is the nth sampling rate.
Further, the specific process of step S3 is as follows:
first, accelerometer triaxial measurements are converted to a navigation system:
wherein ,an A is the projection of the accelerometer triaxial measurements under the navigation system b For the triaxial acceleration values measured by the accelerometer,a transformation matrix from a sensor coordinate system to a navigation system;
normalization:
wherein ,for the acceleration measurement under the normalized navigation system, a nx ,a ny ,a nz Acceleration measurement values of an x axis, a y axis and a z axis under a navigation system are respectively obtained;
normalized gravitational acceleration of navigation systemThe method comprises the following steps: />
Calculating a measurement error estimate of an accelerometer horizontal attitude angle
The accelerometers with different sampling rates are subjected to weighted fusion to obtain a final measurement error estimated value e:
wherein ,is a measurement error estimate of the accelerometer horizontal attitude angle at the normalized s-sample rate.
Further, the proportional integral of the horizontal attitude angle measurement error estimation obtained by the accelerometer measurement is used for correcting the residual drift in the angular velocity measured by the gyroscope, and the specific process of the step S4 is as follows:
δ＝K p e+K i ∫e
ω＝ω g +δ
wherein delta is compensation quantity of gyro drift generated after error e passes through a proportional integral module, K p and Ki Proportional and integral coefficients, ω, respectively g For the angular velocity obtained by gyroscopic measurement, ω is the angular velocity corrected by integrating the accelerometer data, ω is substituted into the following formula, and the first-order Rong Geku arrival method is adopted to obtain an accurate quaternion transmitted along with time:
wherein ,q0 (t+T)q 1 (t+T)q 2 (t+T)q 3 (t+T) is a quaternion value at time t+T, q 0 (t)q 1 (t)q 2 (t)q 3 (t) is the quaternion value, ω, of the time t x 、ω y 、ω z Respectively the angular velocity values in the xyz direction in the updating period, wherein T is the current moment, T is the attitude updating period, and the attitude angle at the final t+T moment is obtained by the following steps:
wherein θ is a pitch angle, γ is a roll angle, and φ is a heading angle.
Further, the number of sampling rates and the number of sensors in the array sensor are both 4, and the multiple sampling rates in the step S1 are respectively 20Hz, 10Hz, 2Hz and 1Hz.
The invention has the beneficial effects that:
1. the invention corrects the gyro error in the sensor array, and the peak value of the angular velocity error is reduced from 1 degree/s to 0.3 degree/s after the correction of the 8 gyroscopic data in the array generated by simulation;
2. the method corrects the attitude error in the inertial navigation process.
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For a clearer description of an embodiment of the invention or of the solutions of the prior art, reference will be made to the accompanying drawings, which are used in the embodiments and which are intended to illustrate, but not to limit the invention in any way, the features and advantages of which can be obtained according to these drawings without inventive labour for a person skilled in the art. Wherein:
FIG. 1 is a schematic flow diagram of the method of the present invention;
FIG. 2 is a detailed flow chart of the method of the present invention;
FIG. 3 is an output plot of an array 4 gyroscopes at 0 angular velocity excitation;
FIG. 4 is a fusion output graph of the array gyroscope after multiple sample rate data fusion correction;
FIG. 5 is the effectiveness of the method of the present invention for attitude error suppression;
fig. 6 is a trace generator.
Detailed Description
In order that the above-recited objects, features and advantages of the present invention will be more clearly understood, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description. It should be noted that, without conflict, the embodiments of the present invention and features in the embodiments may be combined with each other.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, however, the present invention may be practiced in other ways than those described herein, and therefore the scope of the present invention is not limited to the specific embodiments disclosed below.
As shown in fig. 1-2, a multi-sampling rate data fusion posture correction method for an array sensor includes the following steps:
s1: sampling the gyroscope and accelerometer measurements in the array sensor at a plurality of sampling rates;
s2: performing zero-speed judgment on the data obtained in the step S1, and comparing the combined acceleration measured by the accelerometer with the gravity acceleration to obtain the reliability of the horizontal attitude angle calculated by the accelerometer;
s3: comparing the measured value of the accelerometer obtained in the step S1 with the gravity acceleration to obtain a measured error estimated value of the horizontal attitude, and carrying out weighted fusion on the measured value of the accelerometer according to the credibility of the accelerometer data under the corresponding sampling rate;
s4: and the final attitude angle is obtained by fusing the measurement error estimated value of the horizontal attitude angle obtained by the accelerometer and the angular speed measured by the gyroscope.
Preferably, the number of sampling rates is determined by the number of sensors n in the array sensor.
In some embodiments, the specific process of step S2 is:
judging the maneuvering state of the carrier of the current array sensor for each sampling rate, comparing the difference value between the module of the triaxial measurement value of the accelerometer and the gravity acceleration, and taking the difference value as the weight of the corresponding sampling rate, wherein the larger the difference value is, the lower the weight is:
wherein ,the weight of the triaxial measurement value of the accelerometer at the s sampling rate; ax (ax) s ,ay s ,az s G is the gravity acceleration, which is the measured value of the accelerometer of the x axis, the y axis and the z axis under the s sampling rate;
normalizing the weights occupied by the triaxial measurement values of the accelerometer at different sampling rates:
wherein ,Ks The weight of the accelerometer measurement value at the normalized s sampling rate is as followss 1 For the first sampling rate, s n Is the nth sampling rate.
In some embodiments, the specific process of step S3 is:
first, accelerometer triaxial measurements are converted to a navigation system:
wherein ,an A is the projection of the accelerometer triaxial measurements under the navigation system b For the triaxial acceleration values measured by the accelerometer,a transformation matrix from a sensor coordinate system to a navigation system;
normalization:
wherein ,for the acceleration measurement under the normalized navigation system, a nx ,a ny ,a nz Acceleration measurement values of an x axis, a y axis and a z axis under a navigation system are respectively obtained;
normalized gravitational acceleration of navigation systemThe method comprises the following steps: />
Calculating a measurement error estimate of an accelerometer horizontal attitude angle
The accelerometers with different sampling rates are subjected to weighted fusion to obtain a final measurement error estimated value e:
wherein ,is a measurement error estimate of the accelerometer horizontal attitude angle at the normalized s-sample rate.
In some embodiments, the proportional integral of the horizontal attitude angle measurement error estimation obtained by using the accelerometer measurement is used for correcting the residual drift in the angular velocity measured by the gyroscope, and the specific process of the step S4 is as follows:
δ＝K p e+K i ∫e
ω＝ω g +δ
wherein delta is compensation quantity of gyro drift generated after error e passes through a proportional integral module, K p and Ki Proportional and integral coefficients, ω, respectively g For the angular velocity obtained by gyroscopic measurement, ω is the angular velocity corrected by integrating the accelerometer data, ω is substituted into the following formula, and the first-order Rong Geku arrival method is adopted to obtain an accurate quaternion transmitted along with time:
wherein ,q0 (t+T)q 1 (t+T)q 2 (t+T)q 3 (t+T) is a quaternion value at time t+T, q 0 (t)q 1 (t)q 2 (t)q 3 (t) is the quaternion value, ω, of the time t x 、ω y 、ω z Respectively the angular velocity values in the xyz direction in the updating period, wherein T is the current moment, T is the attitude updating period, and the attitude angle at the final t+T moment is obtained by the following steps:
wherein θ is a pitch angle, γ is a roll angle, and φ is a heading angle.
In some embodiments, the number of sampling rates and the number of sensors in the array sensor are both 4, and the multiple sampling rates in step S1 are respectively 20Hz, 10Hz, 2Hz, and 1Hz.
In order to facilitate understanding of the above technical solutions of the present invention, the following detailed description of the above technical solutions of the present invention is provided by specific embodiments.
Example 1
By matlab software, setting the angle random walk ARW parameter of the MEMS gyroscope to be 0.0833 degrees/(h (1/2)), and the velocity random walk RRW parameter to be 600 degrees/(h (3/2)), generating 18000s of sensor original data according to a track generator shown in FIG. 6, wherein T is shown in the figure s The sampling interval is set to 10ms for high frequency and the raw data is sampled at 20Hz, 10Hz, 2Hz, 1Hz sampling rate.
FIG. 3 is an output diagram of 4 gyroscopes in the x-axis of the array under 0 angular velocity excitation, and FIG. 4 is a fused output diagram of the array gyroscopes after multiple sampling rate data fusion correction, and the result shows that the peak-to-peak value of angular velocity errors is reduced from 1 degree/s to 0.3 degree/s after the data of 4 gyroscopes in the array generated through simulation are corrected;
example 2
In the embodiment, a plurality of groups of simulation tests are carried out, the experimental results are shown in fig. 5, the 3-axis attitude angle errors, namely pitch angle, roll angle and course angle errors, of a plurality of experiments are not divergent and are limited to be within 5 degrees, and the method provided by the invention is proved to obviously improve the navigation attitude errors.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (6)
1. The multi-sampling rate data fusion posture correction method for the array sensor is characterized by comprising the following steps of:
s1: sampling the gyroscope and accelerometer measurements in the array sensor at a plurality of sampling rates;
s2: performing zero-speed judgment on the data obtained in the step S1, and comparing the combined acceleration measured by the accelerometer with the gravity acceleration to obtain the reliability of the horizontal attitude angle calculated by the accelerometer;
s3: comparing the measured value of the accelerometer obtained in the step S1 with the gravity acceleration to obtain a measured error estimated value of the horizontal attitude, and carrying out weighted fusion on the measured value of the accelerometer according to the credibility of the accelerometer data under the corresponding sampling rate;
s4: and the final attitude angle is obtained by fusing the measurement error estimated value of the horizontal attitude angle obtained by the accelerometer and the angular speed measured by the gyroscope.
2. The method for correcting the multi-sampling-rate data fusion posture of the array sensor according to claim 1, wherein the number of sampling rates is determined by the number n of sensors in the array sensor.
3. The method for correcting the multi-sampling rate data fusion posture of the array sensor according to claim 2, wherein the specific process of the step S2 is as follows:
judging the maneuvering state of the carrier of the current array sensor for each sampling rate, comparing the difference value between the module of the triaxial measurement value of the accelerometer and the gravity acceleration, and taking the difference value as the weight of the corresponding sampling rate, wherein the larger the difference value is, the lower the weight is:
wherein ,the weight of the triaxial measurement value of the accelerometer at the s sampling rate; ax (ax) s ,ay s ,az s G is the gravity acceleration, which is the measured value of the accelerometer of the x axis, the y axis and the z axis under the s sampling rate;
normalizing the weights occupied by the triaxial measurement values of the accelerometer at different sampling rates:
wherein ,Ks The weight of the accelerometer measurement value at the normalized s sampling rate is as followss 1 For the first sampling rate, s n Is the nth sampling rate.
4. The method for correcting the multi-sampling rate data fusion posture of the array sensor according to claim 3, wherein the specific process of the step S3 is as follows:
first, accelerometer triaxial measurements are converted to a navigation system:
wherein ,an For accelerometer triaxial measurements in a navigational systemProjection, a b For the triaxial acceleration values measured by the accelerometer,a transformation matrix from a sensor coordinate system to a navigation system;
normalization:
wherein ,for the acceleration measurement under the normalized navigation system, a nx ,a ny ,a nz Acceleration measurement values of an x axis, a y axis and a z axis under a navigation system are respectively obtained;
normalized gravitational acceleration of navigation systemThe method comprises the following steps: />
Calculating a measurement error estimate of an accelerometer horizontal attitude angle
The accelerometers with different sampling rates are subjected to weighted fusion to obtain a final measurement error estimated value e:
wherein ,is a measurement error estimate of the accelerometer horizontal attitude angle at the normalized s-sample rate.
5. The method for correcting the multi-sampling rate data fusion posture of the array sensor according to claim 4, wherein the residual drift in the angular velocity measured by the gyroscope is corrected by using the proportional integral of the horizontal posture angle measurement error estimation obtained by the accelerometer measurement, and the specific process of the step S4 is as follows:
δ＝K p e+K i ∫e
ω＝ω g +δ
wherein delta is compensation quantity of gyro drift generated after error e passes through a proportional integral module, K p and Ki Proportional and integral coefficients, ω, respectively g For the angular velocity obtained by gyroscopic measurement, ω is the angular velocity corrected by integrating the accelerometer data, ω is substituted into the following formula, and the first-order Rong Geku arrival method is adopted to obtain an accurate quaternion transmitted along with time:
wherein ,q0 (t+T)q 1 (t+T)q 2 (t+T)q 3 (t+T) is a quaternion value at time t+T, q 0 (t)q 1 (t)q 2 (t)q 3 (t) is the quaternion value, ω, of the time t x 、ω y 、ω z Respectively the angular velocity values in the xyz direction in the updating period, wherein T is the current moment, T is the attitude updating period, and the attitude angle at the final t+T moment is obtained by the following steps:
wherein θ is a pitch angle, γ is a roll angle, and φ is a heading angle.
6. The method for correcting the data fusion posture of the array sensor according to any one of claims 1 to 5, wherein the number of sampling rates and the number of sensors in the array sensor are both 4, and the multiple sampling rates in the step S1 are respectively 20Hz, 10Hz, 2Hz and 1Hz.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102023010A (en) * | 2010-10-26 | 2011-04-20 | 西安理工大学 | MEMS (micro-electromechanical system)-based wavelet field multisensor information fusion system and fusion method |
| CN106767805A (en) * | 2017-01-08 | 2017-05-31 | 上海拓攻机器人有限公司 | High accuracy inertia measuring method and measuring system based on MEMS sensor array |
| CN111121820A (en) * | 2019-12-16 | 2020-05-08 | 南京理工大学 | MEMS inertial sensor array fusion method based on Kalman filtering |
| CN113325920A (en) * | 2020-02-28 | 2021-08-31 | 阿里巴巴集团控股有限公司 | Data processing method, traveling data fusion method, device and storage medium |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US11378399B2 (en) * | 2015-09-14 | 2022-07-05 | The Regents Of The University Of Michigan | High-performance inertial measurements using a redundant array of inexpensive inertial sensors |
| US11733023B2 (en) * | 2018-03-20 | 2023-08-22 | Muvr Labs, Inc. | System and method for angle calculations for a plurality of inertial measurement units |
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Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102023010A (en) * | 2010-10-26 | 2011-04-20 | 西安理工大学 | MEMS (micro-electromechanical system)-based wavelet field multisensor information fusion system and fusion method |
| CN106767805A (en) * | 2017-01-08 | 2017-05-31 | 上海拓攻机器人有限公司 | High accuracy inertia measuring method and measuring system based on MEMS sensor array |
| CN111121820A (en) * | 2019-12-16 | 2020-05-08 | 南京理工大学 | MEMS inertial sensor array fusion method based on Kalman filtering |
| CN113325920A (en) * | 2020-02-28 | 2021-08-31 | 阿里巴巴集团控股有限公司 | Data processing method, traveling data fusion method, device and storage medium |
Non-Patent Citations (1)
| Title |
|---|
| Towards Real-Time Partially Self-Calibrating Pedestrian Navigation With an Inertial Sensor Array;Razavi H. et al.;《IEEE SENSORS JOURNAL》;第20卷(第12期);6634-6641 * |
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