CN114781432B - Displacement resolving method based on multi-source information fusion and trend removal fluctuation analysis - Google Patents
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Abstract
The invention provides a displacement resolving method based on multi-source information fusion and trend fluctuation analysis, which can be applied to the fields of industrial automation, building bridges, seismic monitoring, rehabilitation nursing and the like. According to the method, acceleration and angle data acquired by a three-axis attitude sensor are utilized, data fusion is realized by utilizing space attitude transformation, the influence of a gravity acceleration component in the acceleration data is eliminated, the acceleration data at equal time intervals is obtained by utilizing a linear interpolation function, a trend removal fluctuation analysis method and a Kalman filtering algorithm, trend items and noise in the acceleration data are eliminated, and finally displacement is calculated by utilizing a stable numerical integration method. According to the method, on the premise that an external displacement sensor is not adopted, the actual displacement is obtained by means of fusion of the acceleration and the angle posture information, so that the accurate solution of the spatial displacement is realized, and the defect that the displacement sensor can only measure one-dimensional displacement and the space of an installation position is limited is overcome.
Description
Technical Field
The invention relates to the technical field of sensors and signal processing, in particular to a displacement resolving method based on multi-source information fusion and trend removal fluctuation analysis.
Background
With the continuous development of sensor technology, various sensors are continuously applied to various fields, wherein the application range of the displacement sensor is quite wide, and the displacement sensor is commonly used in aspects of industrial automation, building bridges and the like. The displacement sensor outputs electric signals with different magnitudes according to the magnitude of the displacement, and then solves the magnitude of the displacement, and the displacement sensor is mostly used for measuring physical quantities such as length, distance, vibration, speed, azimuth and the like of equipment such as injection molding machines, hydraulic presses, hardware machines, rolling rod adjustment of steel mills, shield machines and the like. However, the displacement sensor has the defects of large volume, inconvenience for installation and use in smaller equipment, easiness in limitation of measurement distance, incapability of measuring two-dimensional and three-dimensional motion states and the like, and is often limited by various environmental states when the displacement sensor is applied to obtain displacement signals. Besides directly obtaining the displacement signal through the displacement sensor, the acceleration signal can also be obtained through an acceleration sensor with wider application field, and the obtained acceleration signal is used for integrating to obtain the displacement signal. In the existing method for obtaining displacement by integrating acceleration, the acceleration sensor is fixed on a test platform, analysis and processing are carried out by utilizing the obtained acceleration data, the flexibility is poor, and the displacement is difficult to transplant to a movable working condition environment for working. For the acceleration sensor with the inclined vertical axis, the existing method mostly uses a high-pass filter to adjust the baseline, so that not only is the baseline error removed, but also the low-frequency content including residual displacement in the signal is eliminated, and the integral result is smaller than a true value. Therefore, aiming at the characteristics of the acceleration sensor and the numerical integration, the invention provides a stable numerical integration method based on multi-source information fusion and trend fluctuation analysis, which can be separated from a test bed to realize the movement and portability of the sensor, is used for realizing the displacement solution of one-dimensional, two-dimensional and three-dimensional motion of the acceleration sensor, and is a key for further solving the displacement information of equipment under the condition of limited position space.
Disclosure of Invention
The invention provides a displacement solving method combining information fusion and trend fluctuation analysis, which provides a new thought and method for solving the displacement of equipment which is limited by a position space and is not suitable for adopting a displacement sensor, and aims to solve the problem of solving one-dimensional, two-dimensional and three-dimensional displacement according to time information, acceleration information and angle information output by the acceleration sensor under the condition that the acceleration sensor is adopted.
The invention provides a displacement resolving method based on multi-source information fusion and trend removal fluctuation analysis, which specifically comprises the following steps:
step one, detecting the obtained gesture data, preprocessing the data, and deleting a plurality of pieces of repeated data at the same moment.
And secondly, carrying out data fusion according to the obtained acceleration and the space attitude information, and eliminating a gravitational acceleration component in the obtained acceleration.
And thirdly, performing interpolation processing according to the time data and the acceleration data to obtain acceleration data with equal time intervals.
And step four, eliminating trend components in the acceleration according to a trend removal fluctuation analysis technology.
And fifthly, eliminating noise in acceleration according to a Kalman filtering method.
And step six, integrating acceleration data according to a stable numerical integration method to solve displacement.
The invention has the beneficial effects that:
(1) And compared with a displacement sensor, the acceleration sensor with smaller volume is less influenced by position space and is convenient for transplanting, and the acceleration sensor can be combined with a microminiature wireless transmission module to realize wireless communication with an upper computer.
(2) The influence of gravity acceleration is eliminated from the aspect of pose transformation by combining acceleration and a corresponding angle information fusion algorithm, and the influence of gravity acceleration component change caused by the spatial pose transformation of the acceleration sensor in the motion process is eliminated.
(3) By adopting the trend-removing fluctuation analysis and Kalman filtering method, the drift and noise of the acceleration sensor in the long-time working process are eliminated, so that the result of solving displacement in the long-time working process is more accurate.
(4) The reason that the traditional numerical integration method generates drift in the integration process is considered in terms of transfer function, and an improved integration algorithm becomes a stable system by correcting the integration parameters.
Drawings
FIG. 1 is a displacement resolution flow chart of the present invention;
FIG. 2 is a schematic diagram of the position and posture transformation of the Cartesian coordinate system of the acceleration sensor;
FIG. 3 is an illustration of acceleration data and angle data acquired during movement along the X-axis using an acceleration sensor in accordance with the present invention;
FIG. 4 is an illustration of acceleration data and angle data collected during movement along the Y-axis using an acceleration sensor in accordance with the present invention;
FIG. 5 is an illustration of acceleration data and angle data collected from an acceleration sensor moving at an angle of-61℃along the X-axis and Y-axis;
FIG. 6 is an illustration of acceleration data and angle data collected from an acceleration sensor moving at an angle of 53℃along the X-axis and the Y-axis;
FIG. 7 is a graph showing acceleration data collected when an acceleration sensor is used to move along the X-axis and the acceleration data processed by the method of the present invention;
FIG. 8 is an illustration of acceleration data collected during movement of an acceleration sensor along the Y-axis and processed by the method of the present invention;
FIG. 9 is a graph showing acceleration data collected when an acceleration sensor is used to move along an X-axis and a Y-axis at an included angle of-61 degrees and the acceleration data processed by the method of the invention;
FIG. 10 shows acceleration data collected when an acceleration sensor is adopted to move along an X axis and a Y axis at an included angle of 53 degrees and the acceleration data processed by the method of the invention;
FIG. 11 is a graph showing the integrated displacement versus the actual displacement obtained by the solution of the method of the present invention when the acceleration sensor moves along the X-axis;
FIG. 12 is a graph showing the integrated displacement versus the actual displacement obtained by the solution of the method of the present invention when the acceleration sensor moves along the Y-axis;
FIG. 13 is a graph showing the integrated displacement versus the actual displacement obtained by the solution of the method of the present invention when the acceleration sensor moves at an angle of-61℃along the X-axis and the Y-axis;
FIG. 14 is a graph showing the comparison of the integrated displacement and the actual displacement obtained by the calculation of the method of the present invention when the acceleration sensor moves at an angle of 53 degrees along the X-axis and the Y-axis;
FIG. 15 is a three-dimensional graph of displacement trajectories obtained by resolving acceleration data acquired when an acceleration sensor is used in the present invention to move in four different directions.
Detailed Description
The displacement resolving method based on information fusion and detrending fluctuation analysis of the present invention is described in detail below with reference to the accompanying drawings and examples.
Step one, detecting time data in the obtained data sample, deleting repeated data groups in the data transmission and reception process, only reserving one group of effective data at the same time, processing the time data into absolute time, taking the first group of data as initial data, and setting a time node to be 0.
And step two, introducing a concept of a rotation matrix in robot kinematics to eliminate a gravity acceleration component contained in the original acceleration data. As shown in fig. 2, the acceleration sensor is designed by using a cartesian coordinate system, and can determine the positive direction of rotation around the X 0,Y0,Z0 axis according to the right-hand rule, and the rotation angles are respectively denoted as α, β, γ.
The rotation matrix obtained by rotating alpha, beta and gamma around X 0,Y0,Z0 axis is respectively:
The initial coordinate system 0-X 0Y0Z0 rotates around X 0 axis by alpha, then rotates around Y 0 axis by beta, and finally rotates around Z 0 axis by gamma, thus obtaining the real pose coordinate system 0-XYZ in the actual running process. The relationship between the rotation matrix R and the two coordinate systems is respectively as follows:
Accinit＝R·Accgra (5)
Wherein Acc init is the gravitational acceleration component of the gravitational acceleration in each axis in the world coordinate system, and Acc gra is the gravitational acceleration component of the gravitational acceleration in each axis in the actual pose coordinate system. Due to the influence of gravity, when the acceleration sensor is static in a world coordinate system, the acceleration sensor always has an acceleration with the magnitude of g, and the direction of the acceleration is coincident with the positive direction of the Z 0 axis, namely Acc init＝[00g]T. The gravitational acceleration component of gravitational acceleration in each axis in the actual pose coordinate system can be expressed as:
the acceleration of each axis after the gravitational acceleration component is removed in the actual pose coordinate system is as follows:
Accact＝Acc-Accgra＝[Accx+gsinβAccy-gcosβsinαAccz-gcosαcosβ]T (7)
Where acc= [ Acc x Accy Accz]T,Accx,Accy,Accz ] represents the acquired three-axis acceleration data, respectively.
And thirdly, the time intervals of the acceleration data after eliminating the gravity acceleration component may be unequal, and interpolation processing is carried out on the known acceleration data and the time data, so that the same time interval among all acceleration data in the subsequent integration process is ensured, and the simplicity of the integration process and the accuracy of an integration result can be ensured to a certain extent. Using piecewise linear interpolation of time data and acceleration data, the interpolation function Φ (x) can be expressed as:
Where x k (k=0, 1,., n) represents a time node, and f k (k=0, 1,., n) represents an acceleration at x k. The interpolation function over the entire time interval t s,te is represented by the interpolation basis function l (x) as:
Where l i (x) is the interpolation basis function of the i-th time node (i=0, 1..n), which can be expressed as:
Wherein t s,te represents the start time and the end time of the entire time interval, respectively. The linear interpolation function can determine the equal interval time and the corresponding acceleration data according to the requirement.
And fourthly, the data output by the acceleration sensor in the long-time operation process contains a certain trend component, and if the trend component is not filtered during analysis, the actual data gradually deviates from a true value along with the time increase, and larger deviation is caused in the subsequent integration process. Therefore, a trending fluctuation analysis technology is applied to the interpolated acceleration data to remove trend components in the acceleration data. The trending fluctuation analysis method comprises the following steps:
For a sequence of length N { phi j, j=1, 2,..once again, N }, a new sequence is created:
Wherein, Is the average value of the original sequence phi j.
The new sequence y (m) is divided into non-overlapping equal-length subintervals of length s, and the sequence of length N is divided into N s = int (N/s) subintervals altogether. Since the sequence length N is not necessarily divided by the subinterval length s, in order to ensure that the original sequence information is not lost, the sequence may be divided back and forth from the end of the sequence to obtain a total of 2N s subintervals.
Polynomial regression fitting is performed on the data for each subinterval v (v=1, 2,..2N s), and the resulting local trend function may be a polynomial of first, second or higher order. Eliminating trend in each subinterval, and calculating variance mean F 2 (v, s) as follows:
determining a q-order fluctuation function of the whole sequence:
Where q may take any non-zero real number. When q=0, formula (14) becomes:
in actual calculation, q may be any non-zero real number when polynomial regression fitting is performed on the data of each subinterval v (v=1, 2,..2N s).
And fifthly, the original acceleration signal contains noise generated in the data acquisition process, and a Kalman filtering algorithm is adopted to process the noise signal. The algorithm has the advantages of small calculated amount and good real-time performance. The estimated value of the future motion state can be continuously corrected by utilizing the actual motion parameters, and the estimated precision is improved on the premise of considering the real-time performance and the stability.
The state vector prediction equation can be expressed as:
the state vector covariance matrix prediction can be expressed as:
The kalman gain matrix can be expressed as:
The state vector update equation can be expressed as:
the state vector covariance update equation can be expressed as:
Wherein, Representing the predicted state,/>Representing state estimation, A representing state transition matrix,/>Represents a prediction error covariance, P k represents an estimation error covariance, Q represents a process error, K k represents a kalman gain, H represents a measurement matrix, R represents a measurement error, a k represents a measurement value, and I represents an identity matrix.
Step six, integrating the processed acceleration data by adopting a trapezoidal method, wherein for a time continuous function x (T) with a sampling interval of T, a trapezoidal integral formula in the interval [0, KT ] is as follows:
y represents the value obtained by integrating a time-continuous function x (T) for a sampling interval T. The transfer function of the trapezoidal integral formula is:
from the transfer function of equation (22), the poles lie on the unit circle, i.e., in a critical steady state, and are prone to divergence during integration. For j (j=1.,), N) the velocity v (j) and the displacement s (j) integrated by the acceleration a (j) are expressed as:
v(j)＝T[a(j)+a(j-1)]/2+ωv(j-1) (23)
s(j)＝T[v(j)+v(j-1)]/2+ωs(j-1) (24)
Wherein ω is a stability factor.
Examples
The method of the invention is adopted to perform stable numerical integration calculation on the data acquired by the triaxial acceleration sensor as follows:
Four groups of cyclic reciprocating motions are carried out by a testing instrument with a built-in triaxial acceleration sensor, the testing instrument moves along the directions of an X axis and a Y axis, included angles of the X axis and the Y axis are-61 degrees and 53 degrees respectively, and time data, acceleration data and angle data are sent by the collecting testing instrument through a wireless module. Checking the time data, and deleting the repeated data to obtain the acceleration data and the angle data as shown in fig. 3-6. In order to verify the correctness of the method, a laser displacement sensor is adopted to collect actual displacement data for reference.
And (3) aiming at the obtained triaxial acceleration data and triaxial angle data corresponding to the triaxial acceleration data, obtaining triaxial acceleration data after removing the gravity acceleration component according to a formula (7). Since the time data intervals obtained by the acceleration sensor are not equal, the triaxial acceleration interpolation data of the time interval t=0.01 s is obtained by linear interpolation. Trend components in the interpolated data are then removed using a detrending fluctuation analysis technique, where q=15. The acceleration data is then subjected to a final processing using a kalman filter algorithm with a process error q=0.001 and a measurement error r=0.01. As in fig. 7-10.
And solving the displacement according to the obtained final acceleration signal by utilizing a stable numerical integration algorithm of the formula (23) and the formula (24), wherein a stable factor omega=0.996, comparing the stable factor omega=0.996 with displacement data obtained by a laser displacement sensor, and comparing an integration result with an actual displacement result as shown in fig. 11-14, wherein a three-dimensional result of the integration result is as shown in fig. 15.
The peak value error, the difference value error, the absolute error and the mean square error are quoted to evaluate the error of the acceleration integral result by the method.
The peak error err_peak represents the average of the relative differences of the integrated result peak and the true peak:
The difference error err_diff represents the average of the integrated result value and the true value difference:
the absolute error err_abs represents the ratio of the difference between the integration result and the true value to the true value:
the difference in mean square error MSE represents the degree of difference between the integration result and the true value:
s (t) represents an integration result, s t (t) represents a true value, and displacement data acquired by a default laser displacement sensor is the true value.
The calculation error results are shown in Table 1, based on the formulas (25) to (28).
TABLE 1 error results
As can be seen from Table 1, the accuracy of the integration result is significantly improved by processing the acceleration using the method of the present invention, and err_peak, err_diff, err_abs and MSE are improved by 91.68%, 63.41%, 72.40% and 86.83% on average, respectively.
Claims (5)
1. A displacement resolving method based on multi-source information fusion and detrending fluctuation analysis is characterized by comprising the following steps:
firstly, detecting the obtained gesture data, preprocessing the data, and deleting a plurality of pieces of repeated data at the same moment;
step two, data fusion is carried out according to the obtained acceleration and the space attitude information, and a gravity acceleration component in the obtained acceleration is eliminated;
step three, interpolation processing is carried out according to the time data and the acceleration data to obtain acceleration data at equal time intervals;
step four, eliminating trend components in acceleration according to a trend removal fluctuation analysis technology;
step five, eliminating noise in acceleration according to a Kalman filtering method;
step six, integrating acceleration data to solve displacement according to a stable numerical integration method;
In the second step, specifically: introducing the concept of a rotation matrix in robot kinematics to eliminate a gravitational acceleration component contained in original acceleration data; the acceleration sensor is designed by adopting a Cartesian coordinate system, the positive direction of rotation around an X 0,Y0,Z0 shaft is determined according to the right hand rule, and the rotation angles are respectively expressed as alpha, beta and gamma;
The rotation matrix obtained by rotating alpha, beta and gamma around X 0,Y0,Z0 axis is respectively:
The initial coordinate system 0-X 0Y0Z0 rotates around X 0 axis by alpha, then rotates around Y 0 axis by beta, and finally rotates around Z 0 axis by gamma to obtain the real pose coordinate system 0-XYZ in the actual running process; the relationship between the rotation matrix R and the two coordinate systems is respectively as follows:
Accinit＝R·Accgra (5)
Wherein, acc init is the gravitational acceleration component of the gravitational acceleration in each axis in the world coordinate system, and Acc gra is the gravitational acceleration component of the gravitational acceleration in each axis in the actual pose coordinate system;
in the third step, specifically: the time intervals of the acceleration data after eliminating the gravity acceleration component may be unequal, the known acceleration data and the time data are subjected to interpolation processing, the same time interval among the acceleration data in the subsequent integration process is ensured, and the simplicity of the integration process and the accuracy of the integration result are ensured to a certain extent; using piecewise linear interpolation of time data and acceleration data, the interpolation function Φ (x) is expressed on each cell segment [ x k,xk+1 ] as:
Where x k represents a time node, k=0, 1,..n, f k represents acceleration at x k, k=0, 1,..n; the interpolation function over the entire time interval t s,te is represented by the interpolation basis function l (x) as:
Where l i (x) is the interpolation basis function of the i-th time node, i=0, 1,..n, expressed as:
Wherein t s,te represents the starting time and the ending time of the whole time interval respectively; determining equal interval time and corresponding acceleration data according to the need by a linear interpolation function;
In the fourth step, specifically: the data output by the acceleration sensor in the long-time running process contains a certain trend component, and the trend component in the acceleration data needs to be removed by applying a trend removal fluctuation analysis technology to the interpolated acceleration data; the trending fluctuation analysis method comprises the following steps:
For a sequence of length N { phi j, j=1, 2,..once again, N }, a new sequence is created:
Wherein, Is the average value of the original sequence phi j;
dividing the new sequence y (m) into non-overlapping equal-length subintervals of length s, wherein the sequence of length N is divided into N s =int (N/s) subintervals; since the sequence length N is not necessarily divided by the subinterval length s, in order to ensure that the original sequence information is not lost, dividing the sequence again from the tail end of the sequence reversely to the front, and obtaining 2N s subintervals;
In the fifth step, specifically: the original acceleration signal contains noise generated in the data acquisition process, and a Kalman filtering algorithm is adopted to process the noise signal; the algorithm has the advantages of small calculated amount and good real-time performance; the estimated value of the future motion state is continuously corrected by utilizing the actual motion parameters, and the estimated precision is improved on the premise of considering the real-time performance and the stability;
The state vector prediction equation is expressed as:
the state vector covariance matrix prediction is expressed as:
the kalman gain matrix is expressed as:
the state vector update equation is expressed as:
The state vector covariance update equation is expressed as:
Wherein, Representing the predicted state,/>Representing state estimation, A representing state transition matrix,/>Representing a prediction error covariance, P k representing an estimation error covariance, Q representing a process error, K k representing a kalman gain, H representing a measurement matrix, R representing a measurement error, a k representing a measurement value, I representing an identity matrix;
in the sixth step, specifically: integrating the acceleration data processed by the method by adopting a trapezoidal method, and for a time continuous function x (T) with a sampling interval of T, the trapezoidal integral formula in the interval [0, KT ] is as follows:
wherein y represents a value obtained by integrating a time continuous function x (T) with a sampling interval of T; the transfer function of the trapezoidal integral formula is:
The pole of the transfer function is positioned on a unit circle, namely in a critical stable state, and is easy to cause divergence in the integration process, and the transfer function is obtained by the formula (22); the velocity v (j) and the displacement s (j) integrated for the jth acceleration a (j) are formulated as:
v(j)＝T[a(j)+a(j-1)]/2+ωv(j-1) (17)
s(j)＝T[v(j)+v(j-1)]/2+ωs(j-1) (18)
wherein j=1, N; omega is a stability factor.
2. The displacement resolving method based on multi-source information fusion and detrending fluctuation analysis according to claim 1, wherein the displacement resolving method is characterized by comprising the following steps: in the first step, specifically: detecting time data in the obtained data sample, deleting repeated data groups in the data transmission and receiving process, only reserving one group of effective data at the same time, and processing the time data into absolute time.
3. The displacement resolving method based on multi-source information fusion and detrending fluctuation analysis according to claim 1, wherein the displacement resolving method is characterized by comprising the following steps: in the second step, the method further comprises: because of the influence of gravity, when the acceleration sensor is static in a world coordinate system, the acceleration sensor always has an acceleration with the magnitude of g and the direction coincident with the positive direction of the Z 0 axis, namely Acc init＝[0 0 g]T; the gravitational acceleration component of the gravitational acceleration in each axis in the actual pose coordinate system is expressed as:
the acceleration of each axis after the gravitational acceleration component is removed in the actual pose coordinate system is as follows:
Accact＝Acc-Accgra＝[Accx+gsinβ Accy-gcosβsinα Accz-gcosαcosβ]T (20)
Wherein acc= [ Acc x Accy Accz]T,Accx,Accy,Accz ] represents the acquired triaxial acceleration data, respectively.
4. The displacement resolving method based on multi-source information fusion and detrending fluctuation analysis according to claim 1, wherein the displacement resolving method is characterized by comprising the following steps: in the fourth step, the method further comprises: polynomial regression fitting was performed on the data for each subinterval v, where v=1, 2,..2N s; the obtained local trend function is a polynomial of first order, second order or higher order; eliminating trend in each subinterval, and calculating variance mean F 2 (v, s) as follows:
determining a q-order fluctuation function of the whole sequence:
wherein q takes any non-zero real number.
5. The displacement resolving method based on multi-source information fusion and detrending fluctuation analysis according to claim 4, wherein the displacement resolving method is characterized by comprising the following steps: in the fourth step, the method further comprises: when q=0, formula (23) becomes:
In actual calculation, when polynomial regression fitting is performed on the data of each subinterval v, q is an arbitrary non-zero real number, where v=1, 2.
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106500695A (en) * | 2017-01-05 | 2017-03-15 | 大连理工大学 | A kind of human posture recognition method based on adaptive extended kalman filtering |
| CN109550219A (en) * | 2018-11-30 | 2019-04-02 | 歌尔科技有限公司 | A kind of determination method, system and the mobile device of motion information |
| CN110887652A (en) * | 2019-12-04 | 2020-03-17 | 武汉大学 | Interactive multi-model detection method for vibration detection and displacement extraction of accelerometer |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN106500695A (en) * | 2017-01-05 | 2017-03-15 | 大连理工大学 | A kind of human posture recognition method based on adaptive extended kalman filtering |
| CN109550219A (en) * | 2018-11-30 | 2019-04-02 | 歌尔科技有限公司 | A kind of determination method, system and the mobile device of motion information |
| CN110887652A (en) * | 2019-12-04 | 2020-03-17 | 武汉大学 | Interactive multi-model detection method for vibration detection and displacement extraction of accelerometer |
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