CN105260587A - Method for removing interference signal in relevant signal - Google Patents

Abstract

The invention discloses a method for removing an interference signal in a relevant signal. The method comprises the following steps that: a collection module collects two groups of mutually associated data signals and sends the data signals to a central processing unit; the central processing unit forms characteristic vectors in a three-dimensional data space via the collected two groups of data signals, which are respectively (the formula is described in the specification) and (the formula is described in the specification); the central processing unit calculates a common part (the formula is described in the specification), an independent part (the formula is described in the specification) and an independent part (the formula is described in the specification) of (the formula is described in the specification) and (the formula is described in the specification); and a computer outputs calculated data. The method disclosed by the invention can be used for extracting independent parts of each relevant signal, and the independent parts are not mutually associated, so that the interference information in the signal is removed.

Description

A kind of method removing undesired signal in coherent signal

Technical field

The present invention relates to signal processing technology field, particularly relate to a kind of method removing undesired signal in coherent signal.

Background technology

Certain correlativity is often there is between the measurement data of same object different qualities.To complicated event in medical science or cognitive science or other research practice, such as cerebral apoplexy or aphasis or atmospheric pollution etc., never ipsilateral or direction image data is all needed when carrying out accurate analysis and describe, often mutually disturb between these data, you are part of me, and I am part of you, the independent information that very difficult assessment provides separately.Such as soil desertification and drought two kinds of phenomenons influence each other, and the words not adding process cannot determine that they independently affect and joint effect separately on environmental ecology.Usually the free diffusing degree of brain blood flow and brain water molecule will be measured in cerebral arterial thrombosis clinical diagnosis and assessment, but these two kinds of indexs can influence each other in the brain of cerebral apoplexy patient (or other patients), the words not adding process cannot determine that they assess the independent information provided separately to disease forecasting.For another example, in aphasis assessment, be difficult to accomplish mutually not disturb by the other side when measuring voice mistake and phonetic system mistake.

Traditional way these correlated variables pairwise orthogonals is mapped then to get remainder as respective independently composition, this extracting method just ensure that between the residual components (output) of each variable and other original variable (input variable) is in fact orthogonal and independently, still relevant and correlativity completely equal is-symbol between correlativity with raw data becomes anti-between these residual components.This conventional art can not obtain the common component of correlated variables simultaneously.Coherent signal independently composition and common composition is separately extracted like this with regard to needing the new technology of exploitation one to be used for.The technology two or more correlated variables being resolved into coincidence component and mutually orthogonal residual components can be referred to as the independent component extraction of coherent signal.

Summary of the invention

The object of the invention is to overcome existing method coherent signal pairwise orthogonal is mapped, then remainder is got as respective independent sector, only ensure that between the independent sector of each signal and other original signal it is independently, and the technical matters that is still mutually related between these independent sectors, provide a kind of method removing undesired signal in coherent signal, it can extract the independent sector of each coherent signal, and not interrelated between independent sector, removes the interfere information in signal.

A kind of method removing undesired signal in coherent signal of the present invention, comprises the following steps:

S1: acquisition module collects two groups of data-signals that are mutually related, and is sent to CPU (central processing unit);

S2: CPU (central processing unit) processes the data-signal received, forms an eigenvector by the often group data-signal collected in three-dimensional data space, and the eigenvector that these two groups of data-signals are formed is respectively with

S3: suppose existence three independently component meet following formula:

$\stackrel{\→}{x1}=\stackrel{\→}{uc}+\stackrel{\→}{u1};$

$\stackrel{\→}{x2}=\stackrel{\→}{uc}+\stackrel{\→}{u2};$

$\stackrel{\→}{u1}\⊥\stackrel{\→}{x2};$

$\stackrel{\→}{u2}\⊥\stackrel{\→}{x1};$

$\stackrel{\→}{u1}\⊥\stackrel{\→}{u2};$

⊥ represents vertical relation, then with common ground be independent sector be independent sector be

S4: suppose in three-dimensional data space, starting point be A, terminal is B, starting point A, terminal is C, starting point be A, terminal is D, starting point be D, terminal is B, starting point be D, terminal is C, straight line BC exists a some G, make straight line AG perpendicular to straight line BC, i.e. AG ⊥ BC, due to AD ⊥ DB and AD ⊥ DC, so straight line AD is perpendicular to plane DBC, because straight line BC is on plane DBC, so AD ⊥ BC, in conjunction with AG ⊥ BC, obtain straight line BC perpendicular to plane ADG, so DG ⊥ BC;

S5: straight line AG exists some P, make straight line DP perpendicular to straight line AG, i.e. DP ⊥ AG, because straight line BC is perpendicular to plane ADG, then straight line BC is perpendicular to straight line DP, i.e. DP ⊥ BC, so straight line DP is perpendicular to plane ABC;

S6: obtain following relationship by the relation between above-mentioned vector:

$\left|\right|\stackrel{\→}{u1}|{|}^2+|\left|\stackrel{\→}{u2}\right|{|}^2=\left|\right|\stackrel{\→}{x1}-\stackrel{\→}{x2}|{|}^2;$

$\left|\right|\stackrel{\→}{uc}|{|}^2+|\left|\stackrel{\→}{u1}\right|{|}^2=\left|\right|\stackrel{\→}{x1}|{|}^2;$

$\left|\right|\stackrel{\→}{uc}|{|}^2+|\left|\stackrel{\→}{u2}\right|{|}^2=\left|\right|\stackrel{\→}{x2}|{|}^2;$

Thus calculate:

$\left|\right|\stackrel{\→}{u1}\left|\right|=\sqrt{\left(\right|\left|\stackrel{\→}{x1}\right|{|}^2-\left|\right|\stackrel{\→}{x2}|{|}^2+||\stackrel{\→}{x1}-\stackrel{\→}{x2}|{|}^2)/2};$

$\left|\right|\stackrel{\→}{u2}\left|\right|=\sqrt{\left(\right|\left|\stackrel{\→}{x2}\right|{|}^2-\left|\right|\stackrel{\→}{x1}|{|}^2+||\stackrel{\→}{x1}-\stackrel{\→}{x2}|{|}^2)/2};$

$\left|\right|\stackrel{\→}{uc}\left|\right|=\sqrt{\left(\right|\left|\stackrel{\→}{x1}\right|{|}^2+\left|\right|\stackrel{\→}{x2}|{|}^2-||\stackrel{\→}{x1}-\stackrel{\→}{x2}|{|}^2)/2};$

$\left|BD\right|=\left|\right|\stackrel{\→}{u1}\left|\right|,\left|DC\right|=\left|\right|\stackrel{\→}{u2}\left|\right|,\left|AD\right|=\left|\right|\stackrel{\→}{uc}\left|\right|,$ || .|| represents 2 norms of vector, | .| represents the length of line segment;

S7: also obtain following relationship by the relation between above-mentioned vector:

$\frac{\left|DG\right|}{\left|DC\right|}=\frac{\left|BD\right|}{\left|BC\right|};$ Namely $\left|DG\right|=\frac{\left|\right|\stackrel{\→}{u1}\left|\right|\left|\right|\stackrel{\→}{u2}\left|\right|}{\sqrt{\left|\right|\stackrel{\→}{u1}|{|}^2+|\left|\stackrel{\→}{u2}\right|{|}^2}};$

$\left|AG\right|=\sqrt{\left|\right|\stackrel{\→}{uc}|{|}^2+{\left|DG\right|}^2};$

$\frac{\left|PG\right|}{\left|DG\right|}=\frac{\left|DG\right|}{\left|AG\right|};$

|AP|＝|AG|-|PG|；

$\left|DP\right|=\sqrt{{\left|DG\right|}^2-{\left|PG\right|}^2};$

$\left|CG\right|=\sqrt{\left|\right|\stackrel{\→}{u2}|{|}^2-{\left|DG\right|}^2};$

Calculate | AP|, | DP|, | the value of CG|, thus determine unique and $\stackrel{\→}{CG}=\left|CG\right|\frac{\stackrel{\→}{x1}-\stackrel{\→}{x2}}{\left|\right|\stackrel{\→}{x1}-\stackrel{\→}{x2}\left|\right|};\stackrel{\→}{AG}=\stackrel{\→}{x2}+\stackrel{\→}{CG};\stackrel{\→}{AP}=\left|AP\right|\frac{\stackrel{\→}{AG}}{\left|\right|\stackrel{\→}{AG}\left|\right|};$

S8: calculate with with vertical vector of unit length thus calculate according to $\stackrel{\→}{uc}=\stackrel{\→}{AP}+\stackrel{\→}{PD},$ Calculate $\stackrel{\→}{uc}=\left|AP\right|\frac{\stackrel{\→}{AG}}{\left|\right|\stackrel{\→}{AG}\left|\right|}+\left|DP\right|\stackrel{\→}{e0};\stackrel{\→}{u1}=\stackrel{\→}{x1}-\stackrel{\→}{uc};$ $\stackrel{\→}{u2}=\stackrel{\→}{x2}-\stackrel{\→}{uc};$

S9: the common ground that CPU (central processing unit) will calculate independent sector and independent sector export.

In the technical program, the method of undesired signal in coherent signal of removing is exactly two groups of coherent signals are resolved into intersection and mutually orthogonal independent sector, extracted by independent sector, these independent sectors do not associate each other, do not have undesired signal in independent sector.Coherent signal decomposes on vector space, is equal to searching three vectors and meet following relation: $\stackrel{\→}{x1}=\stackrel{\→}{uc}+\stackrel{\→}{u1},\stackrel{\→}{x2}=\stackrel{\→}{uc}+\stackrel{\→}{u2},$ perpendicular to with perpendicular to just can find out that these three vectors are not same high dimensional plane from above vertical relation mutually, their intersection point does not also exist with on the high dimensional plane of structure.This method just by from with go outside the plane formed to find with common ground way solve coherent signal independent component extraction problem.Utilize vector algebra geometric knowledge, one can be resolved into be positioned at with component in plane and a component perpendicular to this plane, this method is first obtained the length of these two components and direction just and then their is added and draws common ground value, has had common ground value just can calculate independent sector and independent sector value.CPU (central processing unit) is by common ground independent sector and independent sector output to display screen display or output to next equipment and analyze.

As preferably, when acquisition module collect more than two be mutually related data-signal time, the data-signal of collection is sent to CPU (central processing unit) by acquisition module, CPU (central processing unit) extracts arbitrarily two groups of data-signals from two groups or more collecting is mutually related data-signal, perform step S2 to S8 and calculate common ground and two independent sectors, then from untreated data-signal, one group of data-signal is extracted arbitrarily, using this data-signal with the common ground that calculates as two coherent signals, perform the independent sector that step S2 to S8 calculates common ground and this data-signal made new advances, the common ground between up-to-date common ground and one group of untreated data-signal and independent sector that obtain are extracted in circulation like this at every turn, until all data-signals are all processed complete, then all independent sectors and the common ground that finally obtains export by CPU (central processing unit).

For two or more coherent signal, can appoint and get two signals, ask new common ground and the independent sector of this common ground and any one remaining untreated signal after obtaining common ground again, so circulation is until each signal is processed once.The common ground finally calculated is exactly the common ground of all signals, and all independent sectors and the common ground that finally obtains are outputted to display screen display or output to next equipment and analyze by CPU (central processing unit).

As preferably, when $\left|\left|\right|\stackrel{\&RightArrow;}{x1}|{|}^2-|\left|\stackrel{\&RightArrow;}{x2}\right|{|}^2\right|<\left|\right|\stackrel{\&RightArrow;}{x1}-\stackrel{\&RightArrow;}{x2}|{|}^2$ When being false, will by pre-service with be normalized, namely make their norm be 1, thus make set up.

As preferably, calculate in described step S8 with with vertical vector of unit length method comprise the following steps: get any one random vector utilize Gram-Schmidt orthogonalization procedure respectively orthonomalization to be distinguished in it and x1 and x2, comprise the following steps:

$\stackrel{\&RightArrow;}{eo}=\frac{\stackrel{\&RightArrow;}{ro}}{\left|\right|\stackrel{\&RightArrow;}{ro}|{|}^2};$

represent and the vector of unit length that direction is identical, represent and the vector of unit length that direction is identical, expression is asked with inner product, expression is asked with inner product.

As preferably, the independent sector obtained and common ground are carried out low-pass filtering treatment.Suppress the high frequency interference composition in the independent sector and common ground extracted, make data more level and smooth.

Substantial effect of the present invention is: the independent sector that can extract each coherent signal, and not interrelated between independent sector, removes the interfere information in signal.

Accompanying drawing explanation

Fig. 1 is the polar plot of coherent signal of the present invention in three-dimensional data space;

Fig. 2 is data collecting module collected is two groups of signals of 0.566 to related coefficient;

Fig. 3 adopts the common ground that in prior art process Fig. 2, two groups of signals obtain independent sector and independent sector

Fig. 4 adopts the common ground that in method process Fig. 2 of the present invention, two groups of signals obtain independent sector and independent sector

Fig. 5 is the signal obtained after carrying out low-pass filtering treatment to each signal in Fig. 4;

Fig. 6 is data collecting module collected is two groups of signals of 0.5331 to related coefficient;

Fig. 7 adopts the common ground that in prior art process Fig. 6, two groups of signals obtain independent sector and independent sector

Fig. 8 adopts the common ground that in method process Fig. 6 of the present invention, two groups of signals obtain independent sector and independent sector

Embodiment

Below by embodiment, and by reference to the accompanying drawings, technical scheme of the present invention is described in further detail.

Embodiment: a kind of method removing undesired signal in coherent signal of the present embodiment, as shown in Figure 1, comprises the following steps:

S1: acquisition module collects two groups of data-signals that are mutually related, often organizes data-signal and comprises three sampled values, and be sent to CPU (central processing unit);

S2: CPU (central processing unit) processes the data-signal received, forms an eigenvector by the often group data-signal collected in three-dimensional data space, and the eigenvector that these two groups of data-signals are formed is respectively with

S3: suppose existence three independently component meet following formula:

$\stackrel{\&RightArrow;}{x1}=\stackrel{\&RightArrow;}{uc}+\stackrel{\&RightArrow;}{u1};$

$\stackrel{\&RightArrow;}{x2}=\stackrel{\&RightArrow;}{uc}+\stackrel{\&RightArrow;}{u2};$

$\stackrel{\&RightArrow;}{u1}\&perp;\stackrel{\&RightArrow;}{x2};$

$\stackrel{\&RightArrow;}{u2}\&perp;\stackrel{\&RightArrow;}{x1};$

$\stackrel{\&RightArrow;}{u1}\&perp;\stackrel{\&RightArrow;}{u2};$

⊥ represents vertical relation, then with common ground be independent sector be independent sector be

S4: suppose in three-dimensional data space, starting point be A, terminal is B, starting point A, terminal is C, starting point be A, terminal is D, starting point be D, terminal is B, starting point be D, terminal is C, straight line BC exists a some G, make straight line AG perpendicular to straight line BC, i.e. AG ⊥ BC, due to AD ⊥ DB and AD ⊥ DC, so straight line AD is perpendicular to plane DBC, because straight line BC is on plane DBC, so AD ⊥ BC, in conjunction with AG ⊥ BC, obtain straight line BC perpendicular to plane ADG, so DG ⊥ BC;

S5: straight line AG exists some P, make straight line DP perpendicular to straight line AG, i.e. DP ⊥ AG, because straight line BC is perpendicular to plane ADG, then straight line BC is perpendicular to straight line DP, i.e. DP ⊥ BC, so straight line DP is perpendicular to plane ABC;

S6: obtain following relationship by the relation between above-mentioned vector:

$\left|\right|\stackrel{\&RightArrow;}{u1}|{|}^2+|\left|\stackrel{\&RightArrow;}{u2}\right|{|}^2=\left|\right|\stackrel{\&RightArrow;}{x1}-\stackrel{\&RightArrow;}{x2}|{|}^2;$

$\left|\right|\stackrel{\&RightArrow;}{uc}|{|}^2+|\left|\stackrel{\&RightArrow;}{u1}\right|{|}^2=\left|\right|\stackrel{\&RightArrow;}{x1}|{|}^2;$

$\left|\right|\stackrel{\&RightArrow;}{uc}|{|}^2+|\left|\stackrel{\&RightArrow;}{u2}\right|{|}^2=\left|\right|\stackrel{\&RightArrow;}{x2}|{|}^2;$

Thus calculate:

$\left|\right|\stackrel{\&RightArrow;}{u1}\left|\right|=\sqrt{\left(\right|\left|\stackrel{\&RightArrow;}{x1}\right|{|}^2-\left|\right|\stackrel{\&RightArrow;}{x2}|{|}^2+||\stackrel{\&RightArrow;}{x1}-\stackrel{\&RightArrow;}{x2}|{|}^2)/2};$

$\left|\right|\stackrel{\&RightArrow;}{u2}\left|\right|=\sqrt{\left(\right|\left|\stackrel{\&RightArrow;}{x2}\right|{|}^2-\left|\right|\stackrel{\&RightArrow;}{x1}|{|}^2+||\stackrel{\&RightArrow;}{x1}-\stackrel{\&RightArrow;}{x2}|{|}^2)/2};$

$\left|\right|\stackrel{\&RightArrow;}{uc}\left|\right|=\sqrt{\left(\right|\left|\stackrel{\&RightArrow;}{x1}\right|{|}^2+\left|\right|\stackrel{\&RightArrow;}{x2}|{|}^2-||\stackrel{\&RightArrow;}{x1}-\stackrel{\&RightArrow;}{x2}|{|}^2)/2};$

$\left|BD\right|=\left|\right|\stackrel{\&RightArrow;}{u1}\left|\right|,\left|DC\right|=\left|\right|\stackrel{\&RightArrow;}{u2}\left|\right|,\left|AD\right|=\left|\right|\stackrel{\&RightArrow;}{uc}\left|\right|,$ || .|| represents 2 norms of vector, | .| represents the length of line segment;

S7: also obtain following relationship by the relation between above-mentioned vector:

$\frac{\left|DG\right|}{\left|DC\right|}=\frac{\left|BD\right|}{\left|BC\right|};$ Namely $\left|DC\right|=\frac{\left|\right|\stackrel{\&RightArrow;}{u1}\left|\right|\left|\right|\stackrel{\&RightArrow;}{u2}\left|\right|}{\sqrt{\left|\right|\stackrel{\&RightArrow;}{u1}|{|}^2+|\left|\stackrel{\&RightArrow;}{u2}\right|{|}^2}};$

$\left|AG\right|=\sqrt{\left|\right|\stackrel{\&RightArrow;}{uc}|{|}^2+{\left|DG\right|}^2};$

$\frac{\left|PG\right|}{\left|DG\right|}=\frac{\left|DG\right|}{\left|AG\right|};$

|AP|＝|AG|-|PG|；

$\left|DP\right|=\sqrt{{\left|DG\right|}^2-{\left|PG\right|}^2};$

$\left|CG\right|=\sqrt{\left|\right|\stackrel{\&RightArrow;}{u2}|{|}^2-{\left|DG\right|}^2};$

Calculate | AP|, | DP|, | the value of CG|, thus determine unique and $\stackrel{\&RightArrow;}{CG}=\left|CG\right|\frac{\stackrel{\&RightArrow;}{x1}-\stackrel{\&RightArrow;}{x2}}{\left|\right|\stackrel{\&RightArrow;}{x1}-\stackrel{\&RightArrow;}{x2}\left|\right|};\stackrel{\&RightArrow;}{AG}=\stackrel{\&RightArrow;}{x2}+\stackrel{\&RightArrow;}{CG};\stackrel{\&RightArrow;}{AP}=\left|AP\right|\frac{\stackrel{\&RightArrow;}{AG}}{\left|\right|\stackrel{\&RightArrow;}{AG}\left|\right|};$

S8: calculate with with vertical vector of unit length thus calculate according to $\stackrel{\&RightArrow;}{uc}=\stackrel{\&RightArrow;}{AP}+\stackrel{\&RightArrow;}{PD},$ Calculate $\stackrel{\&RightArrow;}{uc}=\left|AP\right|\frac{\stackrel{\&RightArrow;}{AG}}{\left|\right|\stackrel{\&OverBar;}{AG}\left|\right|}+\left|DP\right|\stackrel{\&RightArrow;}{e0};\stackrel{\&RightArrow;}{u1}=\stackrel{\&RightArrow;}{x1}-\stackrel{\&RightArrow;}{uc};$

S9: the common ground that CPU (central processing unit) will calculate independent sector and independent sector export.

When $\left|\left|\right|\stackrel{\&RightArrow;}{x1}|{|}^2-|\left|\stackrel{\&RightArrow;}{x2}\right|{|}^2\right|<\left|\right|\stackrel{\&RightArrow;}{x1}-\stackrel{\&RightArrow;}{x2}|{|}^2$ When being false, will by pre-service with be normalized, namely make their norm be 1, thus make $\left|\left|\right|\stackrel{\&RightArrow;}{x1}|{|}^2-|\left|\stackrel{\&RightArrow;}{x2}\right|{|}^2\right|<\left|\right|\stackrel{\&RightArrow;}{x1}-\stackrel{\&RightArrow;}{x2}|{|}^2$ Set up.

Calculate in step S8 with with vertical vector of unit length method comprise the following steps: get any one random vector utilize Gram-Schmidt orthogonalization procedure respectively orthonomalization to be distinguished in it and x1 and x2, comprise the following steps:

$\stackrel{\&RightArrow;}{eo}=\frac{\stackrel{\&RightArrow;}{ro}}{\left|\right|\stackrel{\&RightArrow;}{ro}|{|}^2};$

represent and the vector of unit length that direction is identical, represent and the vector of unit length that direction is identical, expression is asked with inner product, expression is asked with inner product.

In removal coherent signal, the method for undesired signal often will be organized relevant data signals exactly in three-dimensional data space, form an eigenvector (vector algebra method), two groups of relevant data signals are resolved into intersection and mutually orthogonal independent sector, independent sector is extracted, these independent sectors do not associate each other, do not have undesired signal in independent sector.Coherent signal decomposes on vector space, is equal to searching three vectors and meet following relation: $\stackrel{\&RightArrow;}{x1}=\stackrel{\&RightArrow;}{uc}+\stackrel{\&RightArrow;}{u1},\stackrel{\&RightArrow;}{x2}=\stackrel{\&RightArrow;}{uc}+\stackrel{\&RightArrow;}{u2},\stackrel{\&RightArrow;}{uc}$ Perpendicular to with perpendicular to just can find out that these three vectors are not same high dimensional plane from above vertical relation mutually, their intersection point does not also exist with on the high dimensional plane of structure.This method just by from with go outside the plane formed to find with common ground way solve coherent signal independent component extraction problem.Utilize vector algebra geometric knowledge, one can be resolved into be positioned at with component in plane and a component perpendicular to this plane, this method is first obtained the length of these two components and direction just and then their is added and draws common ground value, has had common ground value just can calculate independent sector and independent sector value.CPU (central processing unit) is by common ground independent sector and independent sector output to display screen display or output to next equipment and analyze.

In the present embodiment, data collecting module collected is two groups of signals of 0.566 to related coefficient, as shown in Figure 2.

Adopt the common ground that prior art obtains independent sector and independent sector as shown in Figure 3, the related coefficient between them, as shown in Table 1:

Table one

The direct correlativity of former two groups of data independent sector separately that prior art draws is the negative of former two groups of data dependences, directly proves that this method based on linear regression can not extract separate independent sector at all.We can see also there is very strong correlativity between common ground and independent sector simultaneously.

Adopt the common ground that this method obtains independent sector and independent sector as shown in Figure 4, the related coefficient between them, as shown in Table 2:

Table two

The common ground that this method is extracted can be found out from table two independent sector and independent sector between there is no correlativity.

As can be seen from Figure 4 the result that this method obtains has the composition of the higher-order of oscillation along X direction, this is due to for when more than 3 data collection points, and the possibility of result of complete orthogonal decomposition has several groups.In actual applications, we may wish have a certain component to be smoother along transverse axis in the result of decomposing, therefore, the independent sector obtained and common ground are carried out low-pass filtering treatment, suppress the high frequency interference composition in the independent sector and common ground extracted, make data more level and smooth, the common ground obtained independent sector and independent sector as shown in Figure 5, the related coefficient between them, as shown in Table 3:

Table three

When acquisition module collect more than two be mutually related data-signal time, the data-signal of collection is sent to CPU (central processing unit) by acquisition module, CPU (central processing unit) extracts arbitrarily two groups of data-signals from two groups or more collecting is mutually related data-signal, perform step S2 to S8 and calculate common ground and two independent sectors, then from untreated data-signal, one group of data-signal is extracted arbitrarily, using this data-signal with the common ground that calculates as two coherent signals, perform the independent sector that step S2 to S8 calculates common ground and this data-signal made new advances, the common ground between up-to-date common ground and one group of untreated data-signal and independent sector that obtain are extracted in circulation like this at every turn, until all data-signals are all processed complete, then all independent sectors and the common ground that finally obtains export by CPU (central processing unit).

For two or more coherent signal, can appoint and get two signals, ask new common ground and the independent sector of this common ground and any one remaining untreated signal after obtaining common ground again, so circulation is until each signal is processed once.The common ground finally calculated is exactly the common ground of all signals, and all independent sectors and the common ground that finally obtains are outputted to display screen display or output to next equipment and analyze by CPU (central processing unit).

In the present embodiment, data collecting module collected is two groups of signals of 0.5331 to related coefficient, as shown in Figure 6, in Fig. 6, be the language testing data extracted from 50 people, horizontal ordinate represents number, and ordinate represents and repeats to read aloud the ratio that not understanding Chinese characters a period of time misreads; be the language testing data extracted from 50 people, horizontal ordinate represents number, and ordinate represents that playback distinguishes the error rate that said word occurs; The relative coefficient of these two kinds of different pieces of informations is 0.5331.

Adopt the common ground that prior art obtains independent sector and independent sector as shown in Figure 7, the related coefficient between them, as shown in Table 4,

Table four

The direct correlativity of former two groups of data independent sector separately that prior art draws is the negative of former two groups of data dependences, directly proves that this method based on linear regression can not extract separate independent sector at all.We can see also there is very strong correlativity between common ground and independent sector simultaneously.

Adopt the common ground that this method obtains independent sector and independent sector as shown in Figure 8, the related coefficient between them, as shown in Table 5,

Table five

Can find out from table five, common ground independent sector and independent sector related coefficient is between any two all 0, so the common ground that this method is extracted independent sector and independent sector between there is no correlativity.

be used to assess voice extract and function of pronunciation, that assessment is not having the speech identifying function under vision help.Common ground characterize function words and voice combined together of brain.Independent sector characterize the function finding and express this contact, independent sector what represent is the function setting up voice this connection procedure semantic.

Claims (5)

1. remove a method for undesired signal in coherent signal, it is characterized in that, comprise the following steps:

S1: acquisition module collects two groups of data-signals that are mutually related, and is sent to CPU (central processing unit);

S2: CPU (central processing unit) processes the data-signal received, forms an eigenvector by the often group data-signal collected in three-dimensional data space, and the eigenvector that these two groups of data-signals are formed is respectively with

S3: suppose existence three independently component meet following formula:

$\stackrel{\&RightArrow;}{x1}=\stackrel{\&RightArrow;}{uc}+\stackrel{\&RightArrow;}{u1};$

$\stackrel{\&RightArrow;}{x2}=\stackrel{\&RightArrow;}{uc}+\stackrel{\&RightArrow;}{u2};$

⊥ represents vertical relation, then with common ground be independent sector be independent sector be

S4: suppose in three-dimensional data space, starting point be A, terminal is B, starting point A, terminal is C, starting point be A, terminal is D, starting point be D, terminal is B, starting point be D, terminal is C, straight line BC exists a some G, make straight line AG perpendicular to straight line BC, i.e. AG ⊥ BC, due to AD ⊥ DB and AD ⊥ DC, so straight line AD is perpendicular to plane DBC, because straight line BC is on plane DBC, so AD ⊥ BC, in conjunction with AG ⊥ BC, obtain straight line BC perpendicular to plane ADG, so DG ⊥ BC;

S5: straight line AG exists some P, make straight line DP perpendicular to straight line AG, i.e. DP ⊥ AG, because straight line BC is perpendicular to plane ADG, then straight line BC is perpendicular to straight line DP, i.e. DP ⊥ BC, so straight line DP is perpendicular to plane ABC;

S6: obtain following relationship by the relation between above-mentioned vector:

$\left|\right|\stackrel{\&RightArrow;}{u1}|{|}^2+|\left|\stackrel{\&RightArrow;}{u2}\right|{|}^2=\left|\right|\stackrel{\&RightArrow;}{x1}-\stackrel{\&RightArrow;}{x2}|{|}^2;$

$\left|\right|\stackrel{\&RightArrow;}{uc}|{|}^2+|\left|\stackrel{\&RightArrow;}{u1}\right|{|}^2=\left|\right|\stackrel{\&RightArrow;}{x1}|{|}^2;$

$\left|\right|\stackrel{\&RightArrow;}{uc}|{|}^2+|\left|\stackrel{\&RightArrow;}{u2}\right|{|}^2=\left|\right|\stackrel{\&RightArrow;}{x2}|{|}^2;$

Thus calculate:

$\left|\right|\stackrel{\&RightArrow;}{u1}\left|\right|=\sqrt{\left(\right|\left|\stackrel{\&RightArrow;}{x1}\right|{|}^2-\left|\right|\stackrel{\&RightArrow;}{x2}|{|}^2+||\stackrel{\&RightArrow;}{x1}-\stackrel{\&RightArrow;}{x2}|{|}^2)/2};$

$\left|\right|\stackrel{\&RightArrow;}{u2}\left|\right|=\sqrt{\left(\right|\left|\stackrel{\&RightArrow;}{x2}\right|{|}^2-\left|\right|\stackrel{\&RightArrow;}{x1}|{|}^2+||\stackrel{\&RightArrow;}{x1}-\stackrel{\&RightArrow;}{x2}|{|}^2)/2};$

$\left|\right|\stackrel{\&RightArrow;}{uc}\left|\right|=\sqrt{\left(\right|\left|\stackrel{\&RightArrow;}{x1}\right|{|}^2+\left|\right|\stackrel{\&RightArrow;}{x2}|{|}^2-||\stackrel{\&RightArrow;}{x1}-\stackrel{\&RightArrow;}{x2}|{|}^2)/2};$

$\left|BD\right|=\left|\right|\stackrel{\&RightArrow;}{u1}\left|\right|,$ $\left|DC\right|=\left|\right|\stackrel{\&RightArrow;}{u2}\left|\right|,$ $\left|AD\right|=\left|\right|\stackrel{\&RightArrow;}{uc}\left|\right|,$ || .|| represents 2 norms of vector, | .| represents the length of line segment;

S7: also obtain following relationship by the relation between above-mentioned vector:

$\frac{\left|DG\right|}{\left|DC\right|}=\frac{\left|BD\right|}{\left|BC\right|};$ Namely $\left|DG\right|=\frac{\left|\right|\stackrel{\&RightArrow;}{u1}\left|\right|\left|\right|\stackrel{\&RightArrow;}{u2}\left|\right|}{\sqrt{\left|\right|\stackrel{\&RightArrow;}{u1}|{|}^2+|\left|\stackrel{\&RightArrow;}{u2}\right|{|}^2}};$

$\left|AG\right|=\sqrt{\left|\right|\stackrel{\&RightArrow;}{uc}|{|}^2+{\left|DG\right|}^2};$

$\frac{\left|PG\right|}{\left|DG\right|}=\frac{\left|DG\right|}{\left|AG\right|};$

|AP|＝|AG|-|PG|；

$\left|DP\right|=\sqrt{{\left|DG\right|}^2-{\left|PG\right|}^2};$

$\left|CG\right|=\sqrt{\left|\right|\stackrel{\&RightArrow;}{u2}|{|}^2-{\left|DG\right|}^2};$

Calculate | AP|, | DP|, | the value of CG|, thus determine unique and $\stackrel{\&RightArrow;}{CG}=\left|CG\right|\frac{\stackrel{\&RightArrow;}{x1}-\stackrel{\&RightArrow;}{x2}}{\left|\right|\stackrel{\&RightArrow;}{x1}-\stackrel{\&RightArrow;}{x2}\left|\right|};$ $\stackrel{\&RightArrow;}{AG}=\stackrel{\&RightArrow;}{x2}+\stackrel{\&RightArrow;}{CG}$ $\stackrel{\&RightArrow;}{AP}=\left|AP\right|\frac{\stackrel{\&RightArrow;}{AG}}{\left|\right|\stackrel{\&RightArrow;}{AG}\left|\right|};$

S8: calculate with with vertical vector of unit length thus calculate according to $\stackrel{\&RightArrow;}{uc}=\stackrel{\&RightArrow;}{AP}+\stackrel{\&RightArrow;}{PD},$ Calculate $\stackrel{\&RightArrow;}{uc}=\left|AP\right|\frac{\stackrel{\&RightArrow;}{AG}}{\left|\right|\stackrel{\&RightArrow;}{AG}\left|\right|}+\left|DP\right|\stackrel{\&RightArrow;}{e0};$ $\stackrel{\&RightArrow;}{u1}=\stackrel{\&RightArrow;}{x1}-\stackrel{\&RightArrow;}{uc};$ $\stackrel{\&RightArrow;}{u2}=\stackrel{\&RightArrow;}{x2}-\stackrel{\&RightArrow;}{uc};$

S9: the common ground that CPU (central processing unit) will calculate independent sector and independent sector export.

2. a kind of method removing undesired signal in coherent signal according to claim 1, it is characterized in that: when acquisition module collect more than two be mutually related data-signal time, the data-signal of collection is sent to CPU (central processing unit) by acquisition module, CPU (central processing unit) extracts arbitrarily two groups of data-signals from two groups or more collecting is mutually related data-signal, perform step S2 to S8 and calculate common ground and two independent sectors, then from untreated data-signal, one group of data-signal is extracted arbitrarily, using this data-signal with the common ground that calculates as two coherent signals, perform the independent sector that step S2 to S8 calculates common ground and this data-signal made new advances, the common ground between up-to-date common ground and one group of untreated data-signal and independent sector that obtain are extracted in circulation like this at every turn, until all data-signals are all processed complete, then all independent sectors and the common ground that finally obtains export by CPU (central processing unit).

3. a kind of method removing undesired signal in coherent signal according to claim 1, is characterized in that: when when being false, will by pre-service with be normalized, namely make their norm be 1, thus make $\left|\left|\right|\stackrel{\&RightArrow;}{x1}|{|}^2-|\left|\stackrel{\&RightArrow;}{x2}\right|{|}^2\right|<\left|\right|\stackrel{\&RightArrow;}{x1}-\stackrel{\&RightArrow;}{x2}|{|}^2$ Set up.

4. a kind of method removing undesired signal in coherent signal according to claim 1 or 2 or 3, is characterized in that, calculate in described step S8 with with vertical vector of unit length method comprise the following steps: get any one random vector utilize Gram-Schmidt orthogonalization procedure respectively orthonomalization to be distinguished in it and x1 and x2, comprise the following steps:

$\stackrel{\&RightArrow;}{eo}=\frac{\stackrel{\&RightArrow;}{ro}}{\left|\right|\stackrel{\&RightArrow;}{ro}|{|}^2};$

represent and the vector of unit length that direction is identical, represent and the vector of unit length that direction is identical, expression is asked with inner product, expression is asked with inner product.

5. a kind of method removing undesired signal in coherent signal according to claim 1 or 2 or 3, is characterized in that: the independent sector obtained and common ground are carried out low-pass filtering treatment.