CN102353375A - Dynamic parameter adjustment method of motion attitude data and device thereof - Google Patents
Dynamic parameter adjustment method of motion attitude data and device thereof Download PDFInfo
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Abstract
The invention provides a dynamic parameter adjustment method of motion attitude data, comprising the following steps of: respectively calculating and analyzing the data change rate and percentage of outrange time of observed quantity data and attitude data when carrying out recursive autoregressive filtering; allowing the data change rate to be multiplied by the corresponding percentage of outrange time so as to respectively obtain a first dynamic adjustment coefficient and a second dynamic adjustment coefficient; allowing the first dynamic adjustment coefficient to be multiplied by the measurement noise covariance when the observed quantity data is obtained so as to obtain the updated measurement noise covariance; allowing the second adjustment coefficient to be multiplied by the excitation noise covariance when measuring the attitude data so as to obtain the updated excitation noise covariance; and feeding back the updated measurement noise covariance and excitation noise covariance to the recursive autoregressive filtering process. The invention also provides a dynamic parameter adjustment device of the motion attitude data. The method provided by the invention is used to endow the recursive autoregressive filtering process with strong adaptability.
Description
The application's patent is that application number is 201110116679.7 a patented claim (internal number: the dividing an application MP1101398 case); 201110116679.7 the applying date of case is: on May 6th, 2011, denomination of invention is: the athletic posture data are obtained, human motion attitude method for tracing and relevant device.
Technical field
The present invention relates to micromechanics (MEMS) field, relate in particular to dynamic parameter method of adjustment and equipment that a kind of athletic posture data are obtained.
Background technology
The attitude tracer technique of moving object extensively applies to each field, especially in fields such as space flight navigation, antenna radar, the modelings of human motion attitude.In recent years; Along with developing rapidly of MEMS; The athletic posture tracer technique is integrated into the theory of MEMS gradually; It is microminiaturized, integrated that athletic posture is followed the trail of required measurement component, forms the miniature athletic posture tracing equipment that integrates micro mechanism, microsensor, miniature actuator and signal Processing and control circuit etc.These equipment have characteristics such as cost is low, volume is little, in light weight, thereby favored by people.
The attitude tracking of moving object need at first obtain the attitude data of moving object.In the prior art; Miniature athletic posture data acquisition facility commonly used is collected attitude data; This equipment mainly is integrated with the gyroscope device; After being installed on this equipment on the target object, in running order gyro sensor will be collected the attitude data (roll angle, the angle of pitch, course angle) of moving object.The athletic posture data can utilize these data that the dummy model of this moving object is driven after obtaining, thereby the motion process of real-time reproduction of moving object is realized following the trail of.When carrying out the tracking of human motion attitude; A plurality of miniature athletic posture data acquisition facilities that are integrated with the gyroscope device are bundled in the main joint part of human body; Collect the attitude data of each motive position of human body through these devices; Utilize these data-driven manikin corresponding site motions then; And demonstrate the human motion process intuitively, thereby realize that the human motion attitude follows the trail of with the 3D picture.
Yet gyroscope device measured motion attitude data have error, could reflect the athletic posture of moving object after must revising truly.The roughly process that this error produces is: the direct data measured of gyroscope device is an angular speed; This magnitude of angular velocity is the moment amount; In most cases can not directly use; And need carry out time integral to this angular speed; Obtain angle variable quantity; Add that then initial angle is only the attitude data of moving object as last angle value; This integral process time of integration (dt) is more little; The angle value that obtains is accurate more; Because the gyroscope survey benchmark is himself but not external absolute object of reference; The time of integration, (dt) can not infinitely dwindle in addition; The accumulated error of integration will be passed in time and increased sharply, and then cause measured motion attitude data and real data generation deviation.A kind of method that solves gyroscope integration cumulative errors is in the athletic posture tracing equipment, to increase acceleration transducer; Be used to measure the acceleration value of gravity direction; Under no external force acceleration situation; The roll angle of output movement object and the angle of pitch comparatively exactly; If the external force accelerating effect is arranged; Kalman filter through having recurrence autoregression filter function is carried out data fusion, draws the athletic posture data of moving object at last.But; Because the gravity direction and the course angle quadrature of acceleration analysis; Can't use the gyroscope cumulative errors of acceleration transducer elimination of level direction, cause the course angle data and the actual value of athletic posture inconsistent, so when carrying out moving object and follow the trail of tracing movement object exactly.
Summary of the invention
In view of this; The present invention increases the geomagnetic field sensors part in existing system; Through terrestrial magnetic field data that this senser element is collected as carrying out data fusion in the observed reading input recurrence autoregression unit; Utilize the recurrence convergence function of recurrence autoregression unit to eliminate angular velocity appearance cumulative errors in the horizontal direction; Obtain optimum attitude data, and then realize the tracking of moving object preferably.
Athletic posture Data Acquisition method provided by the invention comprises:
Geomagnetic field intensity when record moving object is in the first measurement course angle, this geomagnetic field intensity are as first magnetic field intensity, and the numerical value of the said first measurement course angle is identical with the numerical value at the true course angle of said moving object;
When moving object moves to the second measurement course angle; The direction revolution α angle of the geomagnetic field intensity that said moving object position is corresponding; The numerical value of said α is that the second measurement course angle and first is measured the poor of course angle; The geomagnetic field intensity of the position after the record revolution α angle, this geomagnetic field intensity is as second magnetic field intensity;
Said first magnetic field intensity and second magnetic field intensity are obtained the magnetic field intensity observed quantity after according to the preset rules computing;
The measure attitude data of said magnetic field intensity observed quantity and said moving object are carried out recurrence autoregression filtering, obtain the attitude data of said moving object.
Preferably, the said first measurement course angle is the zero degree course angle.
Preferably, when said magnetic field intensity observed quantity and moving object measure attitude data were carried out recurrence autoregression filtering, said method also comprised:
Extract the data variation rate and the outrange percentage of time of said magnetic field intensity observed quantity and measure attitude data respectively;
Said data variation rate and outrange percentage of time multiplied each other obtain the first dynamic dynamic adjustment coefficient of adjustment coefficient and second respectively;
With said first dynamically the adjustment coefficient multiply by the observation noise covariance after the observation noise covariance that produces when obtaining said magnetic field intensity observed quantity obtains upgrading, and will said second dynamically adjust the excitation noise covariance after the excitation noise covariance that produces when coefficient multiply by said measure attitude data obtains upgrading;
Observation noise covariance after the said renewal and excitation noise covariance are fed back to said recurrence autoregression filtering.
Preferably, the recurrence autoregression of observed quantity of said moving object magnetic field intensity and moving object measure attitude data is filtered into Kalman filtering.
A kind of human motion attitude method for tracing provided by the invention comprises:
Geomagnetic field intensity when record human body motive position is in the first measurement course angle, this geomagnetic field intensity are as first magnetic field intensity, and the numerical value of the said first measurement course angle is identical with the numerical value at the true course angle of said moving object;
When the human motion position moves to the second measurement course angle; The direction revolution α angle of the geomagnetic field intensity that position, said human motion position is corresponding; The numerical value of said α is that the second measurement course angle and first is measured the poor of course angle; The geomagnetic field intensity of the position after the record revolution α angle, this geomagnetic field intensity is as second magnetic field intensity;
Said first magnetic field intensity and second magnetic field intensity are obtained the magnetic field intensity observed quantity after according to the preset rules computing;
The measure attitude data at said magnetic field intensity observed quantity and said human motion position are carried out the attitude data that recurrence autoregression filtering obtains said human motion position;
The attitude data of said each motive position of human body is transferred to the attitude data processing enter, uses the corresponding site of said data-driven manikin to move by this processing enter, and human motion attitude virtual reappearance is come out.
Preferably, said attitude data with the partes corporis humani position is transferred to the attitude data processing enter and comprises wireless transmission.
A kind of athletic posture Data Acquisition device provided by the invention comprises: measuring unit, geomagnetic field intensity sensing unit and recurrence autoregression filter unit; Said measuring unit, geomagnetic field intensity sensing unit are electrically connected with recurrence autoregression filter unit respectively, wherein:
Said measuring unit is used to measure the attitude data of moving object;
Said geomagnetic field intensity sensing unit; Be used to write down moving object and be in first the geomagnetic field intensity when measuring course angle; This geomagnetic field intensity is as first magnetic field intensity, and the numerical value of the said first measurement course angle is identical with the numerical value at the true course angle of said moving object; When moving object moves to the second measurement course angle; The direction revolution α angle of the geomagnetic field intensity that said moving object position is corresponding; The numerical value of said α is that the said second measurement course angle and first is measured the poor of course angle; The geomagnetic field intensity of the position after the record revolution α angle, this geomagnetic field intensity is as second magnetic field intensity; Said first magnetic field intensity and second magnetic field intensity are obtained the magnetic field intensity observed quantity after according to the preset rules computing;
Said recurrence autoregression filter unit is used for the measure attitude data of said magnetic field intensity observed quantity and said moving object are carried out the attitude data that recurrence autoregression filtering obtains said moving object.
Preferably, the said first measurement course angle is the zero degree course angle.
Preferably; Said device further comprises dynamic adjustment unit; When being used for that said magnetic field intensity observed quantity and moving object measure attitude data are carried out recurrence autoregression filtering, the data variation rate and the outrange percentage of time of said magnetic field intensity observed quantity of statistical study and measure attitude data respectively; Said data variation rate and outrange percentage of time multiplied each other obtain the first dynamic dynamic adjustment coefficient of adjustment coefficient and second respectively; With said first dynamically the adjustment coefficient multiply by the observation noise covariance after the observation noise covariance that produces when obtaining said magnetic field intensity observed quantity obtains upgrading; Excitation noise covariance after the excitation noise covariance that produces when dynamically the adjustment coefficient multiply by said measure attitude with said second obtains upgrading; Observation noise covariance after the said renewal and excitation noise covariance are fed back to said recurrence autoregression filtering.
Preferably, said recurrence autoregression filter unit is a Kalman filter.
A kind of human motion attitude tracing system provided by the invention comprises: at least one human motion position attitude data deriving means, sending module, receiver module, human body attitude reconstructed module and human body attitude present module, wherein:
Said human motion position attitude data deriving means is given sending module with this data transmission after being used to collect the attitude data of each motive position of human body; This device comprises measuring unit, geomagnetic field intensity sensing unit and recurrence autoregression filter unit, and said measuring unit, geomagnetic field intensity sensing unit are electrically connected with recurrence autoregression filter unit respectively, wherein:
Said measuring unit is used to measure the attitude data at human motion position;
Said geomagnetic field intensity sensing unit; Geomagnetic field intensity when being used to write down the human body motive position and being in first course angle; This magnetic field intensity is as first magnetic field intensity, and the numerical value of the said first measurement course angle is identical with the numerical value at the true course angle of said moving object; When the human motion position moves to the second measurement course angle; The direction revolution α angle of the geomagnetic field intensity that said moving object position is corresponding; The numerical value of said α is that the second measurement course angle and first is measured the poor of course angle; The geomagnetic field intensity of the position after the record revolution α angle, this geomagnetic field intensity is as second magnetic field intensity; First magnetic field intensity and second magnetic field intensity are obtained the magnetic field intensity observed quantity after according to the preset rules computing;
Said recurrence autoregression filter unit is used for the measure attitude data at said magnetic field intensity observed quantity and said human motion position are carried out the attitude data that recurrence autoregression filtering obtains said human motion position;
Said sending module, the attitude data of each motive position of human body that is used for said human motion attitude data deriving means is obtained sends to said human body attitude reconstructed module;
Said receiver module is used to receive the attitude data of each motive position of human body of said human motion position attitude data deriving means;
Said human body attitude reconstructed module, the attitude data of each motive position of human body that receiver module is received is used to drive the motion of manikin corresponding site;
Said human body attitude presents module, is used for the motion of virtual reappearance human body.
Preferably, this system further comprises first wireless communication unit and second wireless communication unit, and said sending module sends to said human body attitude reconstructed module through first wireless communication unit with the attitude data of said each motive position of human body; Said receiver module receives the attitude data that said human motion attitude data deriving means obtains through second wireless communication unit.
Further preferably, said first wireless communication unit is integrated in the said attitude data sending module; Said second wireless communication unit is integrated in the said attitude data receiver module.
The present invention introduces magnetic field intensity on the prior art basis; Magnetic field strength date is carried out recurrence autoregression filtering as the priori estimates of observed reading and moving object attitude; Utilize the cumulative errors of the recurrence convergence function elimination gyrohorizon direction of recurrence autoregression filtering; Obtain course angle data comparatively accurately thus, thereby realize the tracking of moving object attitude.
Description of drawings
Fig. 1 carries out the coordinate system synoptic diagram that the athletic posture data aggregation is adopted for method of the present invention;
Fig. 2 is the process flow diagram of method embodiment 1 of the present invention;
Fig. 3 is the Kalman filtering process synoptic diagram of method embodiment 2 of the present invention;
Fig. 4 is the dynamic adjustment synoptic diagram of method embodiment 4 recurrence autoregression filterings of the present invention;
Fig. 5 is the composition frame chart of device embodiment 5 of the present invention;
Fig. 6 is the composition frame chart of the dynamic adjustment unit of device embodiment 6 of the present invention;
Fig. 7 is the composition frame chart of system embodiment 7 of the present invention.
Embodiment
Main thought of the present invention is: on the prior art basis, collect the geomagnetic field intensity data and carry out recurrence autoregression filtering as observed reading and moving object measure attitude data; Utilize the cumulative errors of the recurrence convergence function elimination gyrohorizon direction of recurrence autoregression filtering; Obtain course angle data comparatively accurately thus, thereby realize the tracking of moving object attitude.
The recurrence autoregression filtering of magnetic field strength date and moving object measure attitude data has multiple implementation, and the present invention preferably uses the kalman filter method with recurrence autoregression filter function to carry out this processing procedure.For ease of explained in detail technical scheme of the present invention, the principle of work to Kalman filtering briefly introduces earlier.Kalman filtering is a kind of recurrence autoregression data processing algorithm, and its method estimation procedure state through FEEDBACK CONTROL is to corrections that circulate of the state outcome of each output, until the process-state data that obtains optimum.Kalman filtering can be divided into two cyclic processes: time renewal process and measurement renewal process, and the estimated value that the former is responsible in time calculating current state variable and error covariance forward is to construct the prior estimate of next time state; The latter combines prior estimate and measurand to estimate to construct improved posteriority; The time renewal process can be considered the process of estimating, and measures renewal process and can be considered trimming process, and whole algorithm for estimating essence is a kind of estimating-correcting algorithm of numerical solution that have.The Kalman filtering process can be used five following equation expressions.
Formula 1: by previous moment system state estimation present moment system state
X(k|k-1)＝AX(k-1|k-1)+BU(k)
In the formula; X (k|k-1) is a k system state constantly of utilizing system (k-1) system state estimation constantly; Be called prior estimate; The optimum state value of etching system when X (k-1|k-1) is (k-1); U (k) is k system's control input quantity constantly; A, B are systematic parameters, represent system state transition matrix and external drive input matrix respectively.
Formula 2: the systematic error covariance of estimating present moment by previous moment systematic error covariance
P(k|k-1)＝AP(k-1|k-1)A
T+Q
In the formula, P (k|k-1) is a k systematic error covariance constantly of utilizing system (k-1) error covariance constantly to estimate, and Q is the excitation noise covariance.
According to the system k priori estimates X (k|k-1) constantly that formula 1 obtains, combine k measured value Z (k) constantly can extrapolate k system state optimal value X (k|k) constantly again, prediction equation is:
Formula 3: calculate the system state optimal value by priori estimates and measured value
X(k|k)＝X(k|k-1)+K(k)[Z(k)-HX(k|k-1)]
Wherein H is a matrix, is the systematic survey parameter, the gain of expression state variable, and the H matrix associates observational variable and state variable; K (k) is a kalman gain, is obtained by formula 4:
K(k)＝P(k|k-1)H
T[HP(k|k-1)H
T+R]
-1
R in the formula is the observation noise covariance.
Formula 5: the estimate covariance and the kalman gain reckoning that are produced by the structure prior estimate will be used for k+1 error covariance constantly:
P(k|k)＝[I-K(k)H]P(k|k-1)
In the formula, I is a matrix, measures I=1 for the single model list.
For making those skilled in the art can further understand characteristic of the present invention and technology contents,, technical scheme of the present invention is described in detail below in conjunction with accompanying drawing and embodiment.
Embodiment one
Realize that the moving object tracking need know the parametric description of moving object attitude (be moving object with reference to the orientation in the space).The moving object attitude usually through with the motion reference coordinate system OX of moving object Joint
CY
CZ
CAnd the angle of deciding between the reference frame OXYZ is represented.The initial point of two coordinate systems all is taken at the moving object barycenter, and decide reference frame X-axis level and point to east, the horizontal energized north of Y-axis, Z axle vertical ground points to zenith; X with the motion reference coordinate system of moving object Joint
CThe vertical movement movement direction of object points to right, Y
CAxle is along moving object direction of motion directed forward, Z
CAlong moving object longitudinal axis points upwards.The relation of deciding reference frame and motion reference coordinate system as shown in Figure 1.Overlap with deciding the reference frame initial point when supposing that the moving object coordinate system is initial, according to above-mentioned definition, any attitude of moving object all can obtain through following three rotations: (1) is around Y-axis rotary luffing angle θ; (2) rotate roll angle Ψ around X-axis; (3) around Z axle rotation course angle ψ.Thus, realize that the tracking of moving object attitude only needs to obtain above-mentioned three data and gets final product.Three-axis gyroscope can be used for measuring these data, but there is error problem in gyroscope as previously mentioned, and the moving object attitude data of measuring (θ, Ψ, ψ) can depart from actual value at short notice.Add three axis accelerometer on this basis and can eliminate the pitching angle theta that gyroscope records and the cumulative errors of two aspects of roll angle Ψ to a certain extent.The method that above-mentioned use three-axis gyroscope is collected data locking moving object attitude is called " Three Degree Of Freedom localization method ", and the method for using three-axis gyroscope and three axis accelerometer to collect data locking moving object attitude is called " six degree of freedom localization method ".Adopt " six degree of freedom localization method " although can eliminate the fractional error that gyroscope self rotation brings, do not eliminate the cumulative errors of horizontal direction, promptly course angle ψ can depart from actual value gradually in moving object attitude data measuring process.Present embodiment is introduced the magnetic field appearance on this basis; Be used to measure geomagnetic field intensity; And with the measure attitude data of this magnetic field strength date as correction motion object in the observed reading input recurrence autoregressive filter; And then reduce and eliminate gyrostatic cumulative errors; Obtain attitude data comparatively accurately, realize the tracking of moving object.
Referring to accompanying drawing 2, the moving object attitude data acquisition methods that present embodiment provides comprises:
Step 101: the geomagnetic field intensity when record moving object is in the first measurement course angle, this geomagnetic field intensity are as first magnetic field intensity, and the numerical value of the said first measurement course angle is identical with the numerical value at the true course angle of said moving object;
The first measurement course angle here is the datum course angle, and the course angle ψ in the measure attitude data that this position angle speed instrument integration draws with real course angle numerical value deviation does not take place; The zero degree course angle is selected at this datum course angle usually for use, in fact also can be that other can precalibrated course angle, as long as guarantee that the course angle that angular velocity sensing appearance measures is consistent with actual heading angle numerical value.
Step 102: when said moving object moves to the second measurement course angle; The direction revolution α angle of the terrestrial magnetic field that said moving object position is corresponding; The numerical value of said α is that the second measurement course angle and first is measured the poor of course angle; The geomagnetic field intensity of the position after the record revolution α angle, this geomagnetic field intensity is as second magnetic field intensity;
Second to measure course angle be the course angle that angular velocity sensing appearance records, and this course angle is because the integral process the during work of angular velocity sensing appearance makes the numerical value generation deviation at true course angle of numerical value and this position of this measurement course angle; The magnetic field sensing appearance can write down the magnetic field data of moving object position objectively; The direction that comprises this position magnitude of field intensity and magnetic field intensity; Obtain after the direction of this location, position magnetic field intensity this direction revolution certain angle; This angle is above-mentioned two and measures the poor of course angle numerical value; With actual course angle deviation is arranged because second measures course angle; Must have with the first measurement course angle after rotation is gone back and depart from, this departure degree can react the cumulative errors that angular velocity sensing appearance integration causes.
Step 103: said first magnetic field intensity and second magnetic field intensity is poor, obtain the magnetic field intensity observed quantity;
Above-mentioned two magnetic field intensitys ask poor result can weigh angular velocity sensing appearance horizontal direction cumulative errors; In fact; Except that making the difference mode, also can adopt other operation rules to carry out the processing of two magnetic field strength date, such as asking the difference of two squares, mean square deviation etc. all can realize weighing the purpose of error.
Step 104: the measure attitude data of said magnetic field intensity observed quantity and said moving object are carried out the attitude data that recurrence autoregression filtering obtains said moving object;
The measure attitude data of said moving object are the attitude datas of the moving object that obtains of angular velocity sensing appearance integration, and these data had been eliminated the error of the roll angle and the angle of pitch by the acceleration sensing appearance before carrying out the said step of present embodiment.
The present invention introduces magnetic field intensity on the prior art basis; Magnetic field intensity when measuring course angle with first is a benchmark; To be in the magnetic field intensity that second direction of measuring the corresponding geomagnetic field intensity in the moving object position of course angle rotates back to this position of record behind the certain angle; Ask difference to obtain the magnetic field intensity observed quantity above-mentioned two magnetic field intensities then; And then use this magnetic field intensity observed quantity input recurrence autoregression filter unit to carry out data fusion; The fusion process correction moving object attitude data that obtains of angular speed sensing appearance integration, eliminated the accumulated error of horizontal direction.
Embodiment two
Mention the data fusion process of recurrence autoregression filtering in the foregoing description step 104; In fact; The concrete implementation of carrying out recurrence autoregression filtering between moving object attitude data and the magnetic field strength date has multiple, and the present invention preferably adopts Kalman filtering algorithm to realize this process.Referring to accompanying drawing 3, the data fusion process of kalman filter method is:
Moving object M, in k-1 attitude parameter Quaternion Representation constantly, hypercomplex number is to utilize a kind of supercomplex to wait the validity response vector to rotate.Any vector can be represented as a composite of real and complex quaternions, for example:
(where w is a constant), the parameters of the formulas satisfy the following relationship:
q＝[w?x?y?z]
2
|q|
2＝w
2+x
2+y
2+z
2＝1
The hypercomplex number structure foundation data of moving object attitude come from the measurement data of three axis angular rate sensing appearance.Can construct a hypercomplex number in the following formula of three parameters (θ, Ψ, ψ) any one substitution with the moving object attitude that measures:
w＝cos(α/2)
x＝sin(α/2)cos(β
x)
y＝sin(α/2)cos(β
y)
z＝sin(α/2)cos(β
z)
Wherein: α is the angle that moving object is rotated around coordinate axis, cos (β
x), cos (β
y) cos (β
z) be that above-mentioned attitude parameter is at each axial component.Moving object M at k-1 state constantly with hypercomplex number quarter (q
1 K-1|k-1, q
2 K-1|k-1, q
3 Sk-1|k-1, q
4 K-1|k-1).Cause the measured value biasing because of there is error in a variety of causes when three-axis gyroscope carries out the athletic posture DATA REASONING, the biasing of three direction of measurement of three-axis gyroscope is estimated as (q
Bias1 K-1|k-1, q
Bias2 K-1|k-1, q
Bias3 K-1|k-1).Estimate to constitute jointly state variable by moving object 3 d pose data with to the biasing of these data; Be that state vector X (k-1|k-1) is the 7 degree of freedom vector in the present embodiment; The initial value of this state variable when carrying out Kalman filtering first can be chosen arbitrarily; Because the Kalman filtering process has the recurrence convergence function, the original state of choosing does not arbitrarily produce significant influence to the output result of Kalman filtering.
Thus, according to the formula 1 of Kalman filtering, can estimate moving object M in k state X (k|k-1)=X (k|k-1)=AX (k-1|k-1)+BU (k) constantly, i.e. prior estimate.The impossible absolutely accurate of the process of structure prior estimate itself, this uncertain size is on the one hand because the numerical value that the rotation of gyroscope self causes departs from and drift causes, and one side is caused by the excitation noise of introducing in the prior estimate.These deviation use error covariances represent, therefore, next need the estimating system error covariance being used to calculate kalman gain, and then are used for the update mode variable.According to the formula 2 of Kalman filtering, extrapolate k systematic error covariance P (k|k-1)=P (k|k-1)=AP (k-1|k-1) A constantly
T+ Q.This error covariance value can be chosen arbitrarily when carrying out Kalman filter first, because Kalman filtering recurrence convergence function, the original state of choosing does not arbitrarily produce significant influence to the output result of Kalman filter.After obtaining k error covariance constantly, the observation noise error covariance during in conjunction with Magnetic Sensor numerical value can calculate kalman gain K (k)=P (k|k-1) H according to the formula 4 of Kalman filtering
T[HP (k|k-1) H
T+ R]
-1After obtaining k kalman gain K (k) constantly, can update mode variable X (k|k)=X (k|k)=X (k|k-1)+K (k) [Z (k)-HX (k|k-1)] in observed reading Z (k) the substitution Kalman filtering formula 3 of the magnetic field vector that Magnetic Sensor is obtained.Arrive this, accomplished the once circulation of Kalman filtering, obtained advancing the moving object attitude data of revising.But in order to carry out next one circulation, also need the error covariance of update system, promptly calculate that according to the formula 5 of Kalman filtering being used for k+1 error constantly defences poor P (k|k)=[I-K (k) H] P (k|k-1) jointly.
Top process constantly circulates; The athletic posture data of the magnetic field strength date angle speed sensing appearance integration output constantly that obtains through the magnetic field sensing appearance are carried out feedback modifiers; The horizontal direction cumulative errors that is produced by angular velocity sensing appearance integration is eliminated, and the course angle number and the actual value of moving object attitude are approaching.
Embodiment three
In the foregoing description, what the moving object attitude parameter in the state variable was described use is the Quaternion Representation method.In fact can directly use Eulerian angle to represent for the moving object attitude parameter, even other method for expressing, different method for expressing only is formal difference, all can change each other through these method for expressing of mathematical programming.Provide the conversion formula between hypercomplex number and the Eulerian angle below.
Quaternary is counted to the conversion formula of Eulerian angle:
Above formula arctan and arcsin result is
For other angles need to use atan2 instead arctan.Promptly realize conversion with following formula:
Eulerian angle are to the conversion formula of hypercomplex number:
So the concrete mathematical form of not constrained motion of the present invention attitude description, as long as do not hinder the present invention to realize solving the purpose of prior art problem.
Embodiment four
The present invention introduces the problem that geomagnetic field intensity sensing appearance can solve the gyroscope cumulative errors like the method for above-mentioned description, but geomagnetic field intensity sensing appearance has certain instability, is subjected to influence that external magnetic field changes greatly.Such as when bigger variation takes place suddenly in the external magnetic field; Very big variation appears in the raw data that the magnetic field sensing appearance obtains; Wave filter is owing to not knowing that this is that the external magnetic field changes the result who causes; Can cause error occurring according to update mode equation under the normal situation through the moving object attitude data after the recurrence autoregression.Also such as, if error appears in the moving object attitude data that Magnetic Sensor also possibly cause obtaining when outrange takes place.For making recurrence autoregression filtering have stronger adaptive characteristic, embodiments of the invention further take dynamically to adjust measure, eliminate the above-mentioned error that possibly occur.Kalman filtering is still adopted in the recurrence autoregression filtering of present embodiment.
Participate in shown in the accompanying drawing 4.The dynamic adjustment process of present embodiment is realized through the observation noise covariance matrix R of renewal Kalman filter and the mode of excitation noise covariance matrix Q and then adjusting fiducial interval degree scope.Dynamically the detailed process of adjustment is following:
Excitation noise covariance matrix Q and observation noise covariance matrix R need multiply by dynamically-adjusting parameter separately respectively at each circulation time, the data variation rate of dynamically-adjusting parameter and sensing appearance and above the product relation of being inversely proportional to of the percentage of range time.Represent this process with mathematical expression below: the data variation rate of establishing the attitude data of angular velocity sensing appearance measurement is that α 1 is β 1 with the outrange percentage of time; The data variation rate of the magnetic field strength date of magnetic field sensing appearance observation is α 2; The outrange percentage of time is β 2, then:
D1＝1/α1β1 D2＝1/α2β2
Q′
k＝D1Q
k R′
k＝D2R
k
In the formula, α 1, α 2 are through obtaining said data differentiate; β 1, β 2 for Preset Time at interval in sensing appearance measurement data reach or surpass the time ratio of range, through the statistics Preset Time at interval in measurement data time of meeting or exceeding range account for the percentage of this predetermined interval T.T. and obtain.
With excitation noise covariance matrix Q and the observation noise covariance matrix R substitution Kalman filter that above-mentioned process is dynamically adjusted, the recurrence convergence function of process Kalman filter can overcome above-mentioned technical matters.
In fact; The technical matters that runs into during sensing appearance measurement data in the present embodiment exists in the acceleration sensing appearance in the athletic posture measuring system equally: (1) is jumped to changing fast or when opposite by slow variation when the target object motion state; The filter status adaptations lags behind, and the direct measuring result error of motion state system increases like this; (2) when the target object athletic posture is in big variation range, very likely near in addition exceed the maximum range scope of sensor, the raw data measured of sensing appearance will depart from the real motion situation like this, causes measuring and interrupts or the measurement result inefficacy.These technical matterss can take the method for present embodiment to solve equally: with the data variation rate and the time ratio that meets or exceeds full scale of sensing appearance is parameter to the fiducial interval [D of inductive sensing appearance mutually; + D] carry out FEEDBACK CONTROL: the rate of change of sensing appearance data is big more; It is more little to put the letter space, and degree of confidence is low more; Reach or the time of outrange many more, it is more little to put the letter space, degree of confidence is low more.
Embodiment five
Accompanying drawing 5 is composition frame charts of the embodiment of apparatus of the present invention 500.In the present embodiment; The athletic posture data acquisition facility comprises measuring unit 501; This unit comprises three axis angular rate sensing units 5011; 3-axis acceleration sensing unit 5012; Three betwixt mountains magnetic field intensity sensing units 502 and recurrence autoregression filter unit 503 are formed; Measuring unit 501; Three betwixt mountains magnetic field intensity sensing units 502 are electrically connected with recurrence autoregression filter unit 503 respectively; Measuring unit 501 is used to measure the attitude data of moving object; And send these data to recurrence autoregression unit 503; Three betwixt mountains magnetic field intensity sensing units 502 are used to obtain magnetic field strength date, and send these data to recurrence autoregression unit 503.
The course of work of the device 500 that present embodiment provides is following: the work coordinate system of moving object attitude measurement is set up in preliminary election, and such coordinate system is as as described in the embodiment one, here not in superfluous words.Collecting and changing the moving object attitude data then: three-axis gyroscope 5011 is collected three axial data of moving object XYZ in above-mentioned coordinate system; 3-axis acceleration sensing unit 5012 is collected acceleration information on the moving object gravity direction, and these six data are with the parametric description (course angle, the angle of pitch, roll angle) of hypercomplex number method conversion back as the moving object attitude; Magnetic field vector when three betwixt mountains magnetic field intensity sensing units 502 are set the first measurement course angle is vector as a reference, collects magnetic field strength date as benchmark.To be in the second corresponding big or small angle of terrestrial magnetic field two course angle differences of direction rotation of moving object of measuring the course angle position; And writing down the magnetic field strength date of this postrotational position, the difference of these two magnetic field intensitys has been reacted the possible deviation of angular velocity sensing unit 5011.Next these data are input in the recurrence autoregression filter unit 503; Carry out Data Fusion; Because recurrence autoregression filter unit 503 has the recurrence convergence function, import the data of this unit and progressively optimized, at last the optimum moving object attitude data of output.
Present embodiment is introduced three betwixt mountains magnetic field intensity sensing units on the prior art basis; The magnetic field intensity of moving object position correspondence is as benchmark when measuring course angle with first; The angular dimension of the difference of two course angles of direction rotation in the earth's magnetic field of moving object position correspondence when measuring course angle with second; The magnetic field intensity of the position after the record revolution; With this magnetic field intensity with respect to the size of reference magnetic field intensity as the magnetic field intensity observation; And this magnetic field intensity observation is imported recurrence autoregression unit carry out data fusion; Thereby eliminated the accumulated error of angular speed sensing unit; The data of recurrence autoregression unit output have also been reacted the attitude of moving object comparatively truly, and then can realize the accurate tracking of moving object.
Angular velocity sensing unit in the present embodiment comprises gyro sensor, and the acceleration sensing unit comprises accelerometer, and the geomagnetic field intensity sensing unit comprises magnetometer.Recurrence autoregression unit in the foregoing description can be all electronic components with same filter function, and preference card Thalmann filter of the present invention is realized this process.The Kalman filtering process is shown in above-mentioned embodiment 4, here not at repeated description.
Embodiment six
As previously mentioned, angular velocity sensing unit and geomagnetic field intensity sensing unit have certain instability, are subjected to influence that external magnetic field changes greatly.For making recurrence autoregression filter unit have stronger adaptive characteristic, embodiments of the invention further take dynamically to adjust measure.In the foregoing description, can also comprise dynamic adjustment unit 604, be used for dynamically adjusting the observation noise covariance matrix and the excitation noise covariance matrix of Kalman filtering process, with adjusted covariance matrix input recurrence autoregression filter unit 603.Shown in accompanying drawing 6, the dynamic adjustment unit 604 of present embodiment comprises data statistic analysis unit 6041, performance coeffcient computing unit 6042 and Covariance Transformation unit 6043, wherein:
Data statistic analysis unit 6041 is used for the said magnetic field intensity observed quantity of statistical study respectively and the measure attitude data obtain the data variation rate and surpass the range percentage of time;
Performance coeffcient computing unit 6042 is used for said data variation rate and outrange percentage of time multiplied each other and obtains the first dynamic dynamic adjustment coefficient of adjustment coefficient and second respectively;
Covariance Transformation unit 6043 be used for said first dynamically the adjustment coefficient multiply by the observation noise covariance after the observation noise covariance that produces when obtaining said magnetic field intensity observed quantity obtains upgrading; Excitation noise covariance after the excitation noise covariance that produces when dynamically the adjustment coefficient multiply by said measure attitude data with said second obtains upgrading; Observation noise covariance after the said renewal and excitation noise covariance are fed back to said recurrence autoregressive process.
With excitation noise covariance matrix Q and the observation noise covariance matrix R substitution Kalman filter that above-mentioned process is dynamically adjusted, overcome the instability of angular velocity sensing unit and magnetic field sensing unit through the recurrence convergence function of data fusion unit.
In fact, the technical matters that runs into when angular velocity sensing unit and magnetic field sensing unit measurement data in the present embodiment exists in the acceleration sensing unit in the athletic posture measuring system equally, and mode and the said process of solution are similar, here no longer repetition.
Embodiment seven
Accompanying drawing 7 is composition frame charts of human motion attitude tracing system embodiment provided by the invention.The system 700 of present embodiment comprises that at least one human motion position attitude data deriving means 701, sending module 702, receiver module 703 and human body attitude reconstructed module 704 and human body attitude present module 705; Wherein, human motion position attitude data deriving means 701 is used to realize obtaining of human motion position attitude data as as described in the embodiment three; Sending module 702 is electrically connected with human motion position attitude data deriving means 701, is used for the attitude data that human motion attitude data deriving means 701 obtains is sent to human body attitude reconstructed module 704 through receiver module 703; Receiver module 703 is electrically connected with human body attitude reconstructed module 704, is used to receive the attitude data that human body athletic posture data acquisition facility 701 sends through sending module 702; Human body attitude reconstructed module 704 is used to drive the motion of manikin corresponding site with the attitude data of each motive position of human body of receiving; Human body attitude presents module 705, is used for the motion of virtual reappearance human body.The job step of native system is following:
Step S701: attitude data deriving means 701 methods according to claim 1 in human motion position are obtained the attitude data of each motive position of human body; This device 701 comprises one at least, anchors in advance on each motive position of human body, and the position of concrete set is selected according to actual needs, can be the main articulation point of human body, and these articulation points can reflect the athletic posture of human body;
Step S702: the athletic posture data of each motive position of human body that step S701 is obtained are transferred to human body attitude reconstructed module 704 through sending module 702;
Step S703: after human body attitude reconstructed module 704 receives said partes corporis humani position athletic posture data through receiving element 703, drive each the corresponding site motion in the manikin;
Step S704: human motion presents module 705 the human body attitude virtual reappearance is come out.
Attitude data transmits except that being wired mode between human motion attitude data deriving means 701 and human body attitude reconstructed module in the present embodiment, and preferred wireless mode of the present invention is transmitted.The system of present embodiment further comprises first wireless communication unit 706 and second wireless communication unit 707 during wireless transmission, and the wireless transmission of data is realized in two unit thus.First wireless communication unit 706 can be used as independent module and is electrically connected with attitude data transmitting element 702, also can be integrated in the attitude data sending module 702.Likewise; Second wireless communication unit 707 in the present embodiment can be used as independent module and is electrically connected with attitude data receiver module 703; Can be integrated in the attitude data receiver module 703, embodiment provided by the invention does not limit the connected mode of these two modules yet.Implementation as for concrete wireless connections can adopt bluetooth, WIFI or next-generation communication network LTE etc.; Employed host-host protocol can be based on the star-like agreement in upper strata of IEEE802.15.4; Also can be other host-host protocols of realizing the wireless connections mode, all not hinder the realization of present embodiment goal of the invention.
Present embodiment has provided the holonomic system that the human motion attitude is followed the trail of, and this system has adopted embodiment three described devices, has solved the problem of gyroscope cumulative errors.Simultaneously, present embodiment has been realized the comprehensive unrestricted measurement of athletic posture through the wireless connections between sending module 702 and the receiver module 703, has enlarged the usable range of athletic posture tracing system.
The above only is preferred embodiment of the present invention, and is in order to restriction the present invention, not all within spirit of the present invention and principle, any modification of being done, is equal to replacement, improvement etc., all should be included within the protection domain of invention.
Claims (5)
1. the dynamic parameter method of adjustment of athletic posture data is characterized in that, this method comprises:
When the athletic posture data that will measure are carried out recurrence autoregression filtering with the observed quantity data that observe, the data variation rate and the outrange percentage of time of difference said observed quantity data of statistical study and attitude data;
Said data variation rate and corresponding outrange percentage of time are multiplied each other, obtain first respectively and dynamically adjust dynamically adjustment coefficient of coefficient and second;
With said first dynamically the adjustment coefficient multiply by the observation noise covariance that produces when obtaining the observed quantity data, the observation noise covariance after obtaining upgrading; The excitation noise covariance that produces when dynamically the adjustment coefficient multiply by the measure attitude data with said second, the excitation noise covariance after obtaining upgrading;
Observation noise covariance after the said renewal and excitation noise covariance are fed back to said recurrence autoregression filtering.
2. method according to claim 1 is characterized in that, said observed quantity data comprise the geomagnetic field intensity data, and/or, acceleration information.
3. method according to claim 1 and 2 is characterized in that, said recurrence autoregression is filtered into Kalman filtering.
4. the dynamic parameter of athletic posture data adjustment equipment is characterized in that this equipment comprises data statistic analysis unit, performance coeffcient computing unit and Covariance Transformation unit, wherein:
Said data statistic analysis unit; Be used for when the athletic posture data that will measure are carried out recurrence autoregression filtering with the observed quantity data that observe, respectively the data variation rate and the outrange percentage of time of said observed quantity data of statistical study and attitude data;
Said performance coeffcient computing unit is used for said data variation rate and corresponding outrange percentage of time are multiplied each other, and obtains first respectively and dynamically adjusts dynamically adjustment coefficient of coefficient and second;
Said Covariance Transformation unit, be used for said first dynamically the adjustment coefficient multiply by the observation noise covariance that produces when obtaining the observed quantity data, the observation noise covariance after obtaining upgrading; The excitation noise covariance that produces when dynamically the adjustment coefficient multiply by the measure attitude data with said second, the excitation noise covariance after obtaining upgrading; Observation noise covariance after the said renewal and excitation noise covariance are fed back to said recurrence autoregression filtering.
5. equipment according to claim 4 is characterized in that, said observed quantity data comprise the geomagnetic field intensity data, and/or, acceleration information.
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