CN116954395B - Mouse displacement regulation and control system based on multisensor fusion - Google Patents
Mouse displacement regulation and control system based on multisensor fusion Download PDFInfo
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Abstract
The application belongs to the technical field of mouse displacement regulation and control, and provides a multi-sensor fusion-based mouse displacement regulation and control system, wherein displacement signal data of a mouse are divided into different movement processes through a movement process dividing module; carrying out self-adaptive analysis on the mouse displacement related data through a motion process analysis module, constructing a noise degree index of the data based on the similarity between motion processes and the interfered degree of the data, and self-adaptively constructing a size adjustment value of a filtering window through the change of the noise degree index corresponding to the filtering window and the noise degree index; the size of the filtering window is adaptively adjusted through an adaptive filtering module; finally, the mouse displacement regulation and control module is used for efficiently completing the mouse displacement regulation and control based on the estimated motion trail. The mouse displacement regulation and control system provided by the application acquires the optimal filter window to complete data filtering, so that the data quality is improved, and the speed and the accuracy of mouse displacement regulation and control are improved.
Description
Technical Field
The application relates to the technical field of mouse displacement regulation and control, in particular to a mouse displacement regulation and control system based on multi-sensor fusion.
Background
The mouse is an important input device for a computer and plays an important role in operating the computer to realize the functions thereof. The mouse displacement regulation and control system based on multi-sensor fusion is a system for realizing accurate regulation and control of mouse displacement by utilizing various sensor technologies. By fusing the data of the plurality of sensors, the accuracy of positioning and displacement measurement of the mouse can be improved, and therefore better user experience is provided.
The digital signal output of the mouse system sensor contains various noises and interferences from the sensor itself, mouse circuit system, external electromagnetic interference and the like. In order to accurately measure various displacement parameters in a mouse system, firstly, noise and interference in a measured signal must be effectively eliminated, namely, digital filtering is used for the measured signal, and the proportion of the interference signal in a swimming signal is reduced through calculation or judgment of a program. The conventional digital filtering algorithm generally uses a moving average filtering method, and the window size is often set manually, however, the window size is too large, so that data transition is smooth, time is long, the window size is too small, and the data smoothing effect is poor. Therefore, the effect of digital filtering can be effectively improved by adaptively acquiring the optimal filtering window.
Disclosure of Invention
In order to solve the technical problems, the application provides a mouse displacement regulation and control system based on multi-sensor fusion.
The provided mouse displacement regulation and control system based on multi-sensor fusion comprises: the device comprises a mouse displacement data acquisition module, a motion process segmentation module, a motion process analysis module, a filtering window self-adaptive adjustment module, a self-adaptive filtering module and a mouse displacement regulation and control module;
the motion process segmentation module is used for analyzing the motion characteristics of the displacement signals and segmenting the displacement signals into different motion processes;
the motion process analysis module comprises:
the disturbed degree analysis submodule is used for analyzing the possibility that all data points in the motion process are disturbed data points and acquiring the whole disturbed degree of the motion process;
the noise degree analysis submodule is used for analyzing the similarity of the overall disturbed degrees of different motion processes and obtaining the noise degree of the data points by combining the change characteristics of the corresponding data points in different motion processes;
the moving average filtering sub-module is used for carrying out moving average filtering on the data points according to the noise degree to obtain filtered data points, and then obtaining the filtered noise degree corresponding to the filtered data points;
the adjustment index obtaining submodule is used for obtaining an adjustment index of the size of the filtering window corresponding to the data point according to the noise degree of the data point, the noise degree change after filtering and the noise degree of the data point in the current window;
the filter window self-adaptive adjusting module is used for carrying out self-adaptive adjustment on the size of the filter window according to the adjustment index to obtain an optimal filter window.
In some embodiments of the present application, the mouse displacement data acquisition module is configured to acquire a displacement signal of the mouse, where the displacement signalLinear acceleration including x, y, z axis directions，/>，/>And the angular velocity of the mouse rotating around the x, y and z axes>，/>，/>。
In some embodiments of the application, the overall interference level is: all data points in the motion process are the sum of the probability of interfering data points.
In some embodiments of the application, the likelihood acquisition formula is:
in the formula (i),represents the probability of the data point being an interfering data point, +.>Representing the distance of a data point from a feature point in its local neighbors,/->Representing the acceleration difference between a data point and its local neighbors and the next data point +.>，/>Acceleration maxima for locally adjacent data points of the data points,acceleration minima for locally adjacent data points that are data points;
the acquisition method of the local adjacent data points of the data points comprises the following steps: the first 4 data points that are closest in time sequence to the data point time and the last 4 data points that are closest in time sequence to the data point time;
the method for acquiring the characteristic points comprises the following steps:the data points corresponding to the sign changes are characteristic points.
In some embodiments of the application, the noise level analysis sub-module is further configured to:
analyzing the similarity of the whole disturbed degrees of different movement processes to obtain a reference index, wherein the calculation formula of the reference index is as follows:
in the formula (i),representing the course of exercise +.>And->Reference index between->Representing two exercise courses->And exercise course->Pearson correlation coefficient between medium acceleration curves, +.>Representing the course of exercise +.>Number of data points>For exercise course->Number of data points>Representing the course of exercise +.>Is subject to interference in its entirety;
analyzing the change characteristics of corresponding data points in different motion processes, and combining the reference indexes to obtain the noise degree of the data points, wherein the noise degree calculation formula is as follows:
in the formula (i),representing the course of exercise +.>Noise level of data points>Is->Reference index of individual locomotor processes, < >>Representing data points/>Acceleration value of the point on the x-axis, < >>Representing data points +.>Point at +.>Acceleration values of corresponding data points in the course of the movement on the x-axis, +.>Indicating the number of motion processes.
In some embodiments of the application, the moving average filtering sub-module is further configured to:
carrying out moving average filtering on the data points according to the noise degree to obtain filtered data points, wherein the calculation method of the filtered data points is as follows;
in the formula (i),representing the course of exercise +.>Middle filtered data point values, +.>Representing the number of data points in the current filter window, +.>Is the +.>Acceleration value of data point +.>Is the +.>Noise level of the data points;
and based on the filtered data points, obtaining the filtered noise degree corresponding to the filtered data points by using the noise degree analysis submodule.
In some embodiments of the application, the adjustment index is: the ratio of the noise degree after filtering to the absolute value of the difference between the noise degrees before and after filtering is calculated, the sum of the noise degrees of all data points in the current filtering window is calculated, and then the product of the ratio and the sum is obtained to obtain the adjustment index.
In some embodiments of the application, the filter window adaptation module is further configured to:
when the adjustment index is larger than a first preset adjustment threshold, adaptively adjusting the size of the filter window to obtain the adjusted size of the filter window;
and repeating the self-adaptive adjustment of the size of the filter window until the size adjustment index of the filter window is smaller than or equal to a first preset adjustment threshold value, wherein the acquired size of the filter window is the optimal filter window or reaches the adjustment frequency threshold value, and considering the filter window corresponding to the minimum size adjustment index of the filter window as the optimal filter window.
In some embodiments of the present application, when the adjustment index is greater than or equal to a second preset adjustment threshold, the adjusted filter window size is: 1 adding a normalized adjustment index and then taking an odd integer upwards by the product of the normalized adjustment index and the original window size;
when the adjustment index is smaller than a second preset adjustment threshold, the adjusted filter window size is: and subtracting the normalized adjustment index from 1, and taking the product of the normalized adjustment index and the original window size to an odd integer.
In some embodiments of the present application, the adaptive filtering module is configured to adaptively filter data points using the optimal filtering window as a window size of a moving average filtering;
the mouse displacement regulation and control module is used for carrying out data fusion by using a Kalman filtering sensor data fusion technology to finish the estimation of the mouse displacement track and realize the regulation and control of the mouse displacement.
As can be seen from the above embodiments, the mouse displacement regulation and control system based on multi-sensor fusion provided by the embodiment of the present application has the following beneficial effects:
the provided mouse displacement regulation and control system based on multi-sensor fusion comprises: the device comprises a mouse displacement data acquisition module, a motion process segmentation module, a motion process analysis module, a filtering window self-adaptive adjustment module, a self-adaptive filtering module and a mouse displacement regulation and control module. According to the application, the displacement signal data of the mouse is acquired through the mouse displacement data acquisition module; the displacement signal data of the mouse is divided into different motion processes through the motion process dividing module, a self-adaptive moving average filtering method is carried out on data points in the relatively stable motion process, the obtained filtered data points are not influenced by the reverse motion direction of the mouse, and the quality of data filtering is improved; carrying out self-adaptive analysis on the mouse displacement related data through a motion process analysis module, constructing a noise degree index of the data based on the similarity between motion processes and the interfered degree of the data, carrying out moving average filtering on the data points according to the noise degree to obtain filtered data points and the filtered noise degree, and then self-adaptively constructing a size adjustment value of a filtering window through the change of the noise degree index corresponding to the filtering window and the noise degree index; the size of the filter window is adaptively adjusted through the adaptive filter module, so that the optimal filter window is obtained to finish data filtering, the data quality is improved, and the efficiency and the precision of a Kalman data fusion algorithm and the estimation precision and the efficiency of an obtained mouse motion track are further improved; finally, the mouse displacement regulation and control module is used for efficiently completing the mouse displacement regulation and control based on the estimated motion trail.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application as claimed.
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In order to more clearly illustrate the technical solution of the present application, the drawings that are needed in the embodiments will be briefly described below, and it will be obvious to those skilled in the art that other drawings can be obtained from these drawings without inventive effort.
Fig. 1 is a schematic diagram of basic components of a mouse displacement control system based on multi-sensor fusion according to an embodiment of the present application.
Description of sequence number: the device comprises a 10-mouse displacement data acquisition module, a 20-motion process segmentation module, a 30-motion process analysis module, a 40-filter window self-adaptive adjustment module, a 50-self-adaptive filter module, a 60-mouse displacement regulation and control module, a 31-interfered degree analysis sub-module, a 32-noise degree analysis sub-module, a 33-moving average filter sub-module and a 34-adjustment index acquisition sub-module.
Detailed Description
In order to make the technical solution of the present application better understood by those skilled in the art, the technical solution of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
The following describes the mouse displacement control system based on multi-sensor fusion according to the present embodiment in detail with reference to the accompanying drawings.
Fig. 1 is a schematic diagram of basic components of a mouse displacement control system based on multi-sensor fusion according to an embodiment of the present application.
As shown in fig. 1, the mouse displacement regulation and control system based on multi-sensor fusion includes: the device comprises a mouse displacement data acquisition module 10, a motion process segmentation module 20, a motion process analysis module 30, a filter window self-adaptive adjustment module 40, a self-adaptive filter module 50 and a mouse displacement regulation and control module 60.
Specifically, the mouse displacement data acquisition module 10 is configured to acquire a displacement signal of the mouse, where the displacement signal includes linear accelerations in x, y, and z axes，/>，/>And the angular velocity of the mouse rotating around the x, y and z axes>，/>，/>. The mouse displacement regulating and controlling system collects displacement signals of a mouse through the combination of the acceleration sensor and the gyroscope, and the acceleration sensor and the gyroscope collect linear acceleration +.>，/>，/>And the angular velocity of the mouse rotating around the x, y and z axes>，/>，/>。
The mouse displacement data acquisition module 10 acquires a displacement signal of the mouse and sends the displacement signal to the motion process segmentation module 20, so as to provide basic data for subsequent data analysis.
The acquisition of the data related to the mouse displacement is completed through the sensor and the gyroscope. And when the quality of data acquisition is poor, the precision of the obtained data is poor when the data are fused by using Kalman filtering, and the regulation precision and efficiency are poor when the mouse displacement is regulated and controlled based on the fused data.
The digital signal output of the system sensor in the mouse displacement regulating system contains various noises and interferences from the sensor, a mouse circuit system, external interferences and the like. In order to accurately measure various displacement parameters of the mouse system and further complete mouse displacement regulation, noise and interference in a measured signal must be effectively eliminated, acquired data quality is improved, and further quality of subsequent data fusion is improved.
When the data quality of the collected data is improved by using a data filtering algorithm, a sliding average filtering window method is often used, the size of a window directly influences the quality of data smoothing, and the larger the window is, the more delay is needed in data processing, the worse the real-time performance of mouse displacement regulation is, the smoother the data is possibly, the smaller the window is, the poorer the data smoothing effect is, and the data quality cannot be effectively improved.
Based on the above analysis, the embodiment of the present application performs adaptive adjustment of the filter window through the motion process segmentation module 20, the motion process analysis module 30, and the filter window adaptive adjustment module 40, which is described in detail below.
The motion process segmentation module 20 is used for analyzing the motion characteristics of the displacement signal and segmenting the displacement signal into different motion processes.
Firstly, carrying out preliminary processing on collected displacement signal data of the mouse. Because the movement of the mouse is always a continuous process, the acquired displacement signal data of the mouse is divided into different movement processes, and the data in the same movement process is filtered by a self-adaptive moving average filtering window method. The acquired displacement signal data of the mouse are continuous time sequence data, namely the mouse has the corresponding relation at each momentIs different from the acceleration on the axis and the angular velocity of the rotating shaft. Analyzing the multidimensional displacement signal data at any moment, and collecting data points at the first momentStarting at the point, analyzing the time data points collected later, when +.>When the linear velocity or angular velocity of the point in any direction is not 0, then the data point is a motion data point, when +.>When the linear velocity and the angular velocity in any direction of the point are both 0, the data point is a stationary data point.
Assume thatThe point is the motion data point, then pair +.>Data point at the point next time +.>(here assumed to be +.>Point) is analyzed if->Point and->The directions of the linear velocities of the dots on the different axes are all the same, it can be considered +.>Point and->The points are data points in the same movement process, if +.>Point and->If there is a difference in the linear velocity direction of the dots on different axes, it can be considered +.>Point and->The point belongs to two motion processes, namely, the point tends to shift leftwards after being horizontally rightwards similar to a mouse. If->The point is not a motion data point, then +.>Point and->The points do not belong to the same course of motion data point. The length of the logic mouse line and the length of the mouse pad are always fixed, so that the mouse tends to move in the opposite direction after moving in a certain range.
The collected data points are analyzed to segment the data into different motion processes. The benefits of the segmentation into different motion processes are: when the mouse is controlled by a person to move, the external force applied by the hand of the person to the mouse is relatively stable in each movement process, and the force is not suddenly increased or reduced, so that the self-adaptive moving average filtering method is carried out on the data points in the movement process, the obtained filtered data points are not influenced by the reverse movement direction of the mouse, and the quality of data filtering is improved.
The course of motion analysis module 30 includes: the device comprises a disturbed degree analysis sub-module 31, a noise degree analysis sub-module 32, a moving average filtering sub-module 33 and an adjustment index acquisition sub-module 34.
The disturbance degree analysis sub-module 31 is configured to analyze the likelihood that all data points in the motion process are disturbance data points, and obtain the overall disturbance degree of the motion process.
For analyzing the whole motion process, taking x-axis acceleration data as an example, the disturbance degree of the whole motion process is obtained through the burr and pulse disturbance of the waveform diagram data corresponding to the acceleration (angular velocity) sensor in the motion process. The overall interference degree is as follows: all data points in the motion process are the sum of the possibility of disturbing the data points, namely:
in the formula (i),indicating the overall disturbance of the course of the movement, +.>Indicating->Possibility that data point is an interfering data point, +.>Is the number of data points in the course of motion.
The likelihood acquisition formula is:
in the formula (i),representing data points +.>Dots (in +.>The points areExample) is the likelihood of interfering data points; />Representing the distance of a data point from a feature point in its local neighbors,/->Representing the acceleration difference between a data point and its local neighbors and the next data point +.>The acquisition method of the local adjacent data points of the data points comprises the following steps: data point->The first 4 data points nearest in time sequence of the point moments +.>The last 4 data points which are nearest in time sequence at the point and the data point moment are respectively used for acquiring acceleration difference values between the data point and the next data point for the 9 data pointsEach data point has a corresponding difference value +.>Obtain->The data points corresponding to the sign changes are characteristic points (here, adjacent +.>，/>Point example, if->Dot->Value = 1>Dot->The value is equal to-1, then->Points are characteristic points), if the sign is unchanged, the data point with the smallest difference value is the characteristic point; then data point->When the point is a feature point, the method comprises ++>=0, if data point->When the adjacent data point of the point is the characteristic point, then +.>=1, and so on, ++>The smaller the value, the greater the likelihood of being a spur data point; />The larger, the description data point +.>The greater the likelihood that a point is a pulse disturbance data point (a pulse disturbance may cause a data point with a greater local variance in the signal); />Acceleration maximum for a local adjacent data point to a data point, +.>Acceleration minimum for a local neighboring data point of a data point, +.>And (3) withThe larger the difference, the smaller the data change at the peak, the more likely the spur data point.
The acquisition method of the local adjacent data points of the data points comprises the following steps: data pointsThe first 4 data points nearest in time sequence of the point moments +.>The last 4 data points with the nearest point and data point time sequence;
the method for acquiring the feature points comprises the following steps:the data points corresponding to the sign changes are characteristic points.
The noise level analysis sub-module 32 is configured to analyze the similarity of the overall disturbed levels of different motion processes, and combine the variation characteristics of corresponding data points in different motion processes to obtain the noise level of the data points.
The noise level analysis sub-module 32 is further configured to:
analysis of different courses of movement (here in terms of course of movement，/>For example), the similarity of the overall disturbed degree, a reference index is obtained>Reference index->The calculation formula is as follows:
in the formula (i),representing the course of exercise +.>And->A reference index therebetween; />Representing two exercise courses->And exercise course->The method for obtaining the pearson correlation coefficient between the medium acceleration curves is a known technique, and will not be described in detail, if +.>Negative number, then in calculation +.>，/>The larger the value is, the more similar the forces applied to the mouse in the two movement processes are, the more similar the movement processes are, and the larger the corresponding reference index is; />Representing the course of exercise +.>Number of data points>Is a movement processNumber of data points>And->The smaller the difference, the greater the similarity of the movement course, the +.>For exercise course->The larger the reference index of (2); />Representing the course of exercise +.>The smaller the value of the overall interference degree, the larger the reference index;
analyzing the change characteristics of corresponding data points in different movement processes and combining with a reference indexNoise level of the obtained data point +.>Noise level->The calculation formula is as follows:
in the formula (i),representing the course of exercise +.>Noise range of data pointsDegree, noise degree->The larger the interference degree is, the smaller the filtering weight in the moving average filtering window is, and the larger the corresponding filtering window is, so as to ensure the filtering effect and obtain the noise degree +.>Normalization processing is carried out to obtain normalized noise degree->；/>Is->Reference index of individual locomotor processes, < >>Representing data points +.>Acceleration value of the point on the x-axis, < >>Representing data points +.>Point at +.>Acceleration values of corresponding data points on an x-axis in the motion process are obtained by the following steps: acquisition data point +.>The point adjacent to the data point can obtain the sequence of 9 data points and +.>Sequence calculation of adjacent data points of data points in motion processThe DTW distance, the minimum distance point is the corresponding data point, wherein the DTW algorithm is a known technology and is not described herein; />And (3) withThe greater the difference, the greater the degree of noise at the data point q point; />Indicating the number of motion processes.
The moving average filtering sub-module 33 is configured to perform moving average filtering on the data points according to the noise level, obtain filtered data points, and then obtain the filtered noise level corresponding to the filtered data points.
The moving average filtering sub-module 33 is further configured to:
carrying out moving average filtering on the data points according to the noise degree to obtain filtered data points, wherein the calculation method of the filtered data points is as follows;
in the formula (i),representing the course of exercise +.>Middle filtered data point values, +.>Representing the number of data points in the current filter window, +.>Is the +.>Acceleration value of data point +.>Is the +.>Normalized noise level for each data point; the method for setting the current filter window size may be: by the exercise course->For example, a->Assuming M data points in the filter window, the initial filter window size can be set to +.>=/>Wherein 200 is set according to an empirical value, and an implementer can adjust the setting; />Representing an odd integer function taken upwards, e.g.>=9，/>=7。
Based on the filtered data points, using the noise level analysis sub-module 32, a filtered noise level corresponding to the filtered data points is obtained, and the filtered noise level acquisition method is consistent with the noise level corresponding to the data points before filtering, i.e. the filtered noise level calculation method is as follows:
in the formula (i),representing the course of exercise +.>The filtered noise level of the medium filtered data point; />Is->Reference index of individual locomotor processes, < >>Representing filtered data points +.>Acceleration value of the point on the x-axis, < >>Representing filtered data points +.>Point at +.>Acceleration values of corresponding data points on an x-axis in the motion process; />Indicating the number of motion processes.
The adjustment index obtaining sub-module 34 is configured to obtain an adjustment index of the size of the filtering window corresponding to the data point according to the noise level of the data point and the noise level variation after filtering, and the noise level of the data point in the current window.
Adaptively constructing data points based on noise level variations before and after filtering and noise levels of the data points within a windowAnd (5) adjusting the index of the size of the filtering window corresponding to the point. The regulation indexes are as follows: calculating the ratio of the noise level after filtering to the absolute value of the difference between the noise level before filtering and the noise level after filtering, calculating the sum of the noise levels of all data points in the current filtering window, and comparing the ratio with the sumAnd the product obtains the adjustment index, namely:
in the formula (i),representing the course of exercise +.>Data points>The size of the filtering window corresponding to the point is adjusted; />Represents data point +.>Noise level before filtering corresponding to point, < ->Represents data point +.>The degree of noise after filtering corresponding to the point, < >>The larger the window size is, the worse the current window filtering effect is, and the size of the filtering window is adjusted; />And->The larger the difference is, the better the filtering effect is, and the smaller the adjustment index is; />The number of data points in the current filtering window is the number; />Is the +.>The greater the noise level of a data point, the greater its value, which indicates that the more noisy the data point within the window is disturbed, the more it should be to adjust the filter window.
The filter window adaptive adjustment module 40 is configured to adaptively adjust the size of the filter window according to the adjustment index, so as to obtain an optimal filter window.
The filter window adaptation module 40 is further configured to:
when the adjustment index is larger than a first preset adjustment threshold value of 0.2, the size of the filter window is adaptively adjusted, the adjusted size of the filter window is obtained, and the adjusted size of the filter window is as follows:
when the adjustment index is greater than or equal to a second preset adjustment threshold, the size of the adjusted filter window is as follows: 1 adding a normalized adjustment index and then taking an odd integer upwards by the product of the normalized adjustment index and the original window size;
when the adjustment index is smaller than a second preset adjustment threshold, the size of the adjusted filter window is as follows: 1 subtracting the normalized adjustment index and then taking an odd integer upwards by the product of the original window size; namely:
in the formula (i),the size of the filtering window after being adjusted; />The original window size; />An adjustment index after normalization of the size of the filtering window; />When the filter effect is poor, the window should be enlarged, and the smoothing effect is improved; when->The method shows that the filtering effect is relatively good at the moment, and the method can adapt to the reduced window to improve the filtering speed; />Representing an odd integer function taken upwards, e.g.>=9，/>=7; wherein the second preset adjustment threshold is adjustable by an practitioner according to the empirical value.
And repeating the self-adaptive adjustment of the size of the filter window until the size adjustment index of the filter window is smaller than or equal to a first preset adjustment threshold value of 0.2, wherein the acquired size of the filter window is the optimal filter window or reaches an adjustment frequency threshold value of 50, and considering the filter window corresponding to the minimum size adjustment index of the filter window as the optimal filter window. Wherein the first preset adjustment threshold is adjustable by an practitioner according to an empirical value.
The adaptive filtering module 50 is configured to adaptively filter the data points using the optimal filtering window as a window size of the moving average filtering.
All the data points have the corresponding optimal filtering window, the optimal filtering window size is used as the size of the moving average filtering window, and the data points of the acceleration of the x axis, the acceleration of the y axis, the acceleration of the z axis and the angular velocity of each axis are subjected to self-adaptive filtering respectively, so that the filtered data points are obtained, the data quality is improved, and the noise and other interferences are reduced. And further, the efficiency and the precision of data fusion are improved, so that the efficiency and the precision of mouse displacement regulation and control are improved.
The mouse displacement regulating and controlling module 60 is used for carrying out data fusion by using a sensor data fusion technology of Kalman filtering to complete the estimation of the mouse displacement track and realize the regulation and control of the mouse displacement.
The data fusion technology of the sensor is used for carrying out data fusion on the data output by the gyroscope and the acceleration sensor, so that the estimation of the mouse displacement track is completed, and the information of a plurality of sensors is fused into a target quantity by the data fusion technology, so that the certainty and the perception accuracy of the system are effectively improved. And the real-time performance and the stability of the mouse displacement regulating and controlling system are ensured. The kalman filtering algorithm is a well-known technique and will not be described herein. And regulating and controlling the mouse displacement according to the estimated mouse displacement track obtained by Kalman filtering. The control method is a known technique and will not be described in detail.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments.
It is noted that unless specified and limited otherwise, relational terms such as "first" and "second", and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. The terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a circuit structure, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such article or apparatus. Without further limitation, the statement "comprises one … …" does not exclude that an additional identical element is present in an article or device that comprises the element. In addition, the term "and/or" as used herein includes any and all combinations of one or more of the associated listed items.
Other embodiments of the application will be apparent to those skilled in the art from consideration of the specification and practice of the application disclosed herein. This application is intended to cover any variations, uses, or adaptations of the application following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the application pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the application being indicated by the following claims.
It is to be understood that the application is not limited to the precise arrangements and instrumentalities shown in the drawings, which have been described above, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the application is limited only by the appended claims.
Claims (10)
1. A mouse displacement regulation and control system based on multi-sensor fusion, the system comprising: the device comprises a mouse displacement data acquisition module (10), a motion process segmentation module (20), a motion process analysis module (30), a filter window self-adaptive adjustment module (40), a self-adaptive filter module (50) and a mouse displacement regulation and control module (60);
the motion process segmentation module (20) is used for analyzing the motion characteristics of the displacement signals and segmenting the displacement signals into different motion processes;
the course of motion analysis module (30) includes:
the disturbed degree analysis submodule (31) is used for analyzing the possibility that all data points in the motion process are disturbed data points and acquiring the whole disturbed degree of the motion process;
a noise level analysis sub-module (32) for analyzing the similarity of the overall disturbed levels of different motion processes and combining the variation characteristics of corresponding data points in different motion processes to obtain the noise level of the data points;
a moving average filtering sub-module (33) configured to perform moving average filtering on the data points according to the noise level, obtain filtered data points, and then obtain filtered noise levels corresponding to the filtered data points;
an adjustment index obtaining sub-module (34) for obtaining an adjustment index of the size of the filtering window corresponding to the data point according to the noise degree of the data point, the noise degree change after filtering and the noise degree of the data point in the current window;
and the filter window self-adaptive adjusting module (40) is used for carrying out self-adaptive adjustment on the size of the filter window according to the adjustment index to obtain an optimal filter window.
2. The mouse displacement control system based on multi-sensor fusion according to claim 1, wherein the mouse displacement data acquisition module (10) is configured to acquire a displacement signal of a mouse, the displacement signal including linear accelerations in x, y, and z axis directions，/>，/>And the angular velocity of the mouse rotating around the x, y and z axes>，/>，/>。
3. The multi-sensor fusion-based mouse displacement regulation system of claim 1, wherein the overall degree of interference is: all data points in the motion process are the sum of the probability of interfering data points.
4. The multi-sensor fusion-based mouse displacement regulation system of claim 3 wherein the likelihood acquisition formula is:
in the formula (i),represents the probability of the data point being an interfering data point, +.>Representing the distance of a data point from a feature point in its local neighbors,/->Representing the acceleration difference between a data point and its local neighbors and the next data point +.>，/>Acceleration maxima for locally adjacent data points of the data points,acceleration minima for locally adjacent data points that are data points;
the acquisition method of the local adjacent data points of the data points comprises the following steps: the first 4 data points that are closest in time sequence to the data point time and the last 4 data points that are closest in time sequence to the data point time;
the method for acquiring the characteristic points comprises the following steps:the data points corresponding to the sign changes are characteristic points.
5. The multi-sensor fusion-based mouse displacement regulation system of claim 1, wherein the noise level analysis sub-module (32) is further configured to:
analyzing the similarity of the whole disturbed degrees of different movement processes to obtain a reference index, wherein the calculation formula of the reference index is as follows:
in the formula (i),representing the course of exercise +.>And->Reference index between->Representing two exercise courses->And course of movementPearson correlation coefficient between medium acceleration curves, +.>Representing the course of exercise +.>Number of data points>For exercise course->Number of data points>Representing the course of exercise +.>Is subject to interference in its entirety;
analyzing the change characteristics of corresponding data points in different motion processes, and combining the reference indexes to obtain the noise degree of the data points, wherein the noise degree calculation formula is as follows:
in the formula (i),representing the course of exercise +.>Noise level of data points>Is->A reference index for the course of the movement,representing data points +.>Acceleration value of the point on the x-axis, +.>Representing data points +.>Point at +.>Acceleration values of corresponding data points in the course of the movement on the x-axis, +.>Indicating the number of motion processes.
6. The multi-sensor fusion based mouse displacement regulation system of claim 1, wherein the moving average filtering sub-module (33) is further configured to:
carrying out moving average filtering on the data points according to the noise degree to obtain filtered data points, wherein the calculation method of the filtered data points is as follows;
in the formula (i),representing the course of exercise +.>Middle filtered data point values, +.>Representing the number of data points in the current filter window, +.>Is the +.>Acceleration value of data point +.>Is the +.>Noise level of the data points;
based on the filtered data points, a filtered noise level corresponding to the filtered data points is obtained using the noise level analysis sub-module (32).
7. The multi-sensor fusion-based mouse displacement regulation and control system according to claim 1, wherein the regulation index is: the ratio of the noise degree after filtering to the absolute value of the difference between the noise degrees before and after filtering is calculated, the sum of the noise degrees of all data points in the current filtering window is calculated, and then the product of the ratio and the sum is obtained to obtain the adjustment index.
8. The multi-sensor fusion based mouse displacement regulation system of claim 1, wherein the filter window adaptation adjustment module (40) is further configured to:
when the adjustment index is larger than a first preset adjustment threshold, adaptively adjusting the size of the filter window to obtain the adjusted size of the filter window;
and repeating the self-adaptive adjustment of the size of the filter window until the size adjustment index of the filter window is smaller than or equal to a first preset adjustment threshold value, wherein the acquired size of the filter window is the optimal filter window or reaches the adjustment frequency threshold value, and considering the filter window corresponding to the minimum size adjustment index of the filter window as the optimal filter window.
9. The multi-sensor fusion-based mouse displacement regulation system of claim 8, wherein when the regulation index is greater than or equal to a second preset regulation threshold, the regulated filter window size is: 1 adding a normalized adjustment index and then taking an odd integer upwards by the product of the normalized adjustment index and the original window size;
when the adjustment index is smaller than a second preset adjustment threshold, the adjusted filter window size is: and subtracting the normalized adjustment index from 1, and taking the product of the normalized adjustment index and the original window size to an odd integer.
10. The multi-sensor fusion-based mouse displacement regulation system of claim 1, wherein the adaptive filtering module (50) is configured to adaptively filter data points using the optimal filtering window as a window size for moving average filtering;
the mouse displacement regulating and controlling module (60) is used for carrying out data fusion by using a sensor data fusion technology of Kalman filtering to finish the estimation of a mouse displacement track and realize the regulation and control of the mouse displacement.
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