CN106510763A - Blood relationship spectrum imaging method and device - Google Patents
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Abstract
The invention discloses a blood relationship spectrum imaging method and device. The method includes the steps: building a state space model of a system by a Kalman filtering algorithm; building a dynamic transfer equation according to the state space model and a linear prediction system and performing an adaptive Kalman filtering cycle iteration process for the dynamic transfer equation; generating a blood relationship spectrum in the adaptive Kalman filtering cycle iteration process. The state space model comprises a state equation and a measurement equation, a spectrum estimation gold standard algorithm (plural Kalman filtering estimation) is applied to blood flow spectrum imaging, and the Kalman filtering spectrum estimation algorithm has accurate speed estimation and direction estimation capacity and optimal variance performance.
Description
Technical field
A kind of the present embodiments relate to technical field of communication, more particularly to method and device of blood relationship spectral imaging.
Background technology
Ultrasonic Doppler blood flow imaging technology is to realize the important means of angiopathy Nondestructive, to ultrasonic doppler blood
The imaging analysis of stream echo-signal can provide important parameter foundation for medical diagnosis on disease.Ultrasonic doppler blood flow spectral imaging skill
Art be by being estimated picture to the Doppler frequency shift of ultrasonic scattering body in blood, reach detection blood flow rate, blood flow direction and
The purpose of the medical informations such as blood volume.Therefore, ultrasonic Doppler blood flow imaging is supervised in real time in diagnostic assessment cardiovascular disease, operation
Shield, medication effect many aspects such as are evaluated and all have important clinical value, are clinically indispensable important
One of diagnostic means.
DOPPLER ULTRASOUND SIGNAL is time dependent nonstationary random signal, and the frequency displacement of signal is straight with the speed of blood flow
Connect correlation.Research shows that the presence of angiopathy can cause the waveform of blood flow rate, such as Doppler signal Maximum frequency curve
Or average frequency changes, so as to the time-frequency distributions for affecting signal also change.
At present, in supersonic blood Doppler detector device at home and abroad on market, the spectral imaging method of main flow is base
In the Short Time Fourier Transform (Short-time Fourier transform, STFT) of classical spectrum estimate.The realization of the method,
It is that signal carries out adding window truncate when ultrasonic echo after to demodulation is slow, quick Fu is carried out to the signal in each short time
Vertical leaf transformation (Fast Fourier Transformation, FFT), realizes the estimation to frequency spectrum.But the method has a lot
Inherent shortcoming, algorithm variance poor performance；Resolution is determined to cause temporal resolution and frequency resolution to need folding by data length
In；But adding window is smooth increases estimation difference etc..
The content of the invention
The purpose of the embodiment of the present invention is to propose a kind of method and device of blood relationship spectral imaging, it is intended to how be solved will
Plural Kalman Filter Estimation is applied to blood flow frequency spectrum imaging.
It is that, up to this purpose, the embodiment of the present invention is employed the following technical solutions：
In a first aspect, a kind of method of blood relationship spectral imaging, methods described includes：
The state-space model of system is set up by Kalman filtering algorithm, the state-space model includes state equation
With measurement equation；
Dynamic transfer equation is built according to the state-space model and linear prediction system, and to the dynamic transfer side
Cheng Jinhang adaptive Kalman filter loop iteration processes；
Blood relationship frequency spectrum is generated during the adaptive Kalman filter loop iteration.
Preferably, the state-space model for setting up system by Kalman filtering algorithm, including：
The state-space model of signal and noise is set up respectively, using the state variable estimate of previous moment to present tense
Quarter is carried out according to a preliminary estimate, and parameter is estimated in the observation amendment with reference to present moment；
The state variable estimate is updated, the optimum state variable estimated value of present moment is solved.
Preferably, the state-space model for setting up signal and noise respectively, is estimated using the state variable of previous moment
Evaluation is carried out according to a preliminary estimate to present moment, and parameter is estimated in the observation amendment with reference to present moment, including：
State equation x is set up by the Kalman filtering algorithmk+1=Axk+Buk+wkWith measurement equation zk=Hkxk+vk；
Wherein, the x and z are to be input into and export, and the A and B is systematic parameter, the xkIt is the state vector at k moment,
The ukIt is controlled quentity controlled variable of the k moment to system, the zkIt is the calculation matrix or output matrix at k moment, the H is measuring system
Parameter, the wkAnd vkRepresent process noise and measurement noise respectively, the standard deviation of the process noise is R, the measurement is made an uproar
The standard deviation of sound is Q.
Preferably, it is describedThe wdFor flow Doppler frequency displacement, the φt
For the phase place change of echo-signal, the nkFor random noise, amplitudes of the Amp for signal；
It is describedThe frequency response point that the h correspondences are estimated, h=e-jwt=cos (wt)-j sin (wt), the A are unit matrix；The ukIn the case of non-existent, the B is 0.
Preferably, it is described that dynamic transfer equation is built according to the state-space model and linear prediction system, and to institute
Stating dynamic transfer equation carries out adaptive Kalman filter loop iteration process, including：
The plural Kalman of initialization
Calculate the plural Kalman gain at K moment
Optimal estimation amendment
Update covariance matrix
Forward prediction
Wherein, the I be unit matrix, the GkWith the PkFor K when the Kalman gain inscribed and covariance matrix,
Subscript ^ be estimated value, subscript-For priori value, p0Initial value is inversely proportional to the signal to noise ratio snr of measurement process, x0For 0 or first
Data point.
Second aspect, a kind of device of blood relationship spectral imaging, described device include：
Module is set up, for setting up the state-space model of system, the state space mould by Kalman filtering algorithm
Type includes state equation and measurement equation；
Iteration module is built, for building dynamic transfer equation according to the state-space model and linear prediction system,
And adaptive Kalman filter loop iteration process is carried out to the dynamic transfer equation；
Generation module, for generating blood relationship frequency spectrum during the adaptive Kalman filter loop iteration.
Preferably, it is described to set up module, specifically for：
The state-space model of signal and noise is set up respectively, using the state variable estimate of previous moment to present tense
Quarter is carried out according to a preliminary estimate, and parameter is estimated in the observation amendment with reference to present moment；
The state variable estimate is updated, the optimum state variable estimated value of present moment is solved.
Preferably, it is described to set up module, also particularly useful for：
State equation x is set up by the Kalman filtering algorithmk+1=Axk+Buk+wkWith measurement equation zk=Hkxk+vk；
Wherein, the x and z are to be input into and export, and the A and B is systematic parameter, the xkIt is the state vector at k moment,
The ukIt is controlled quentity controlled variable of the k moment to system, the zkIt is the calculation matrix or output matrix at k moment, the H is measuring system
Parameter, the wkAnd vkRepresent process noise and measurement noise respectively, the standard deviation of the process noise is R, the measurement is made an uproar
The standard deviation of sound is Q.
Preferably, it is describedThe wdFor flow Doppler frequency displacement, the φt
For the phase place change of echo-signal, the nkFor random noise, amplitudes of the Amp for signal；
It is describedThe frequency response point that the h correspondences are estimated, h=e-jwt=cos (wt)-j sin (wt), the A are unit matrix；The ukIn the case of non-existent, the B is 0.
Preferably, the structure iteration module, specifically for：
The plural Kalman of initialization
Calculate the plural Kalman gain at K moment
Optimal estimation amendment
Update covariance matrix
Forward prediction
Wherein, the I be unit matrix, the GkWith the PkFor K when the Kalman gain inscribed and covariance matrix,
Subscript ^ be estimated value, subscript-For priori value, p0Initial value is inversely proportional to the signal to noise ratio snr of measurement process, x0For 0 or first
Data point.
A kind of method and device of blood relationship spectral imaging provided in an embodiment of the present invention, is set up by Kalman filtering algorithm
The state-space model of system, the state-space model include state equation and measurement equation；According to the state space mould
Type and linear prediction system build dynamic transfer equation, and carry out adaptive Kalman filter circulation to the dynamic transfer equation
Iterative process；Blood relationship frequency spectrum is generated during the adaptive Kalman filter loop iteration.The present invention is by the gold of Power estimation
Canonical algorithm (plural Kalman Filter Estimation) is applied to blood flow frequency spectrum imaging, and Kalman filtering Power estimation algorithm has accurate
Velocity estimation and direction estimation ability, variance performance are optimal；The flexible adjustability of Kalman filtering Power estimation parameter model is set
Put, it is possible to achieve optional frequency Composition Estimation, do not affected by data length, and optional frequency composition is filtered or is compensated, use
Mode more flexibility and changeability；The self adaptation circulation of Kalman filtering Power estimation algorithm presses down with eliminating with splendid robustness
The ability of Gauss processed or its non-Gaussian noise, its structure can also realize the imaging mode of single-point input spectrum output, imaging
Time delay is minimum；Blood flow frequency spectrum imaging in, can fully use priori, with parameter setting go out optimal spectral resolution,
The mode of temporal resolution realizes realtime imaging.
Description of the drawings
Fig. 1 is that the embodiment of the present invention provides a kind of schematic flow sheet of the method for blood relationship spectral imaging；
Fig. 2 is a kind of estimation schematic diagram of state variable provided in an embodiment of the present invention；
Fig. 3 is a kind of method schematic diagram for realizing Power estimation with Kalman filtering algorithm provided in an embodiment of the present invention；
Fig. 4 is the method schematic diagram of the frequency bandwidth response under a kind of different R values provided in an embodiment of the present invention；
Fig. 5 is a kind of state transition diagram schematic diagram of system provided in an embodiment of the present invention；
Fig. 6 is a kind of blood flow frequency spectrum imaging schematic diagram provided in an embodiment of the present invention；
Fig. 7 is a kind of high-level schematic functional block diagram of the device of blood relationship spectral imaging provided in an embodiment of the present invention.
Specific embodiment
With reference to the accompanying drawings and examples the embodiment of the present invention is described in further detail.It is understood that this
The described specific embodiment in place is used only for explaining the embodiment of the present invention, rather than the restriction to the embodiment of the present invention.In addition also
It should be noted that for the ease of description, illustrate only the part related to the embodiment of the present invention rather than entire infrastructure in accompanying drawing.
With reference to Fig. 1, Fig. 1 is that the embodiment of the present invention provides a kind of schematic flow sheet of the method for blood relationship spectral imaging.
As shown in figure 1, the method for the blood relationship spectral imaging includes：
Step 101, sets up the state-space model of system by Kalman filtering algorithm, and the state-space model includes
State equation and measurement equation；
Specifically, Kalman filtering is the wave filter based on state estimation, and systematic survey problem is converted into state estimation
Update with state, you can estimate from existing observation and can not originally survey or the difficult estimated value for surveying variable.Algorithm has process non-
The ability of stationary signal, more meets the demand of dynamic Power estimation, and combines Power estimation model, using linear predictor coefficient structure
Into dynamic transfer equation, adaptive-filtering obtains more accurately estimated information.Filtering algorithm is estimated with recursion (Iteration)
What the form of meter was carried out, its basic thought is：The state-space model of signal and noise, estimating using previous moment is set up respectively
Evaluation is realized to present moment according to a preliminary estimate, estimates parameter with reference to the observation amendment of present moment, and update to state change
The estimation of amount, solves the optimal estimation value of present moment, as shown in Figure 2.
Preferably, the state-space model for setting up system by Kalman filtering algorithm, including：
The state-space model of signal and noise is set up respectively, using the state variable estimate of previous moment to present tense
Quarter is carried out according to a preliminary estimate, and parameter is estimated in the observation amendment with reference to present moment；
The state variable estimate is updated, the optimum state variable estimated value of present moment is solved.
Preferably, the state-space model for setting up signal and noise respectively, is estimated using the state variable of previous moment
Evaluation is carried out according to a preliminary estimate to present moment, and parameter is estimated in the observation amendment with reference to present moment, including：
State equation x is set up by the Kalman filtering algorithmk+1=Axk+Buk+wkWith measurement equation zk=Hkxk+vk；
Wherein, the x and z are to be input into and export, and the A and B is systematic parameter, the xkIt is the state vector at k moment,
The ukIt is controlled quentity controlled variable of the k moment to system, the zkIt is the calculation matrix or output matrix at k moment, the H is measuring system
Parameter, the wkAnd vkRepresent process noise and measurement noise respectively, the standard deviation of the process noise is R, the measurement is made an uproar
The standard deviation of sound is Q.
Preferably, it is describedThe wdIt is for flow Doppler frequency displacement, described
φtFor the phase place change of echo-signal, the nkFor random noise, amplitudes of the Amp for signal；
It is describedThe frequency response point that the h correspondences are estimated, h=e-jwt=cos (wt)-j sin (wt), the A are unit matrix；The ukIn the case of non-existent, the B is 0.
Specifically, Power estimation is realized with Kalman filtering algorithm, need to set up the state-space model of system, with state
Equation and measurement equation are described, as shown in Figure 3：
State equation：xk+1=Axk+Buk+wk
Measurement equation：zk=Hkxk+vk
The x and z are to be input into and export, and the A and B is systematic parameter, the xkIt is the state vector at k moment, the uk
It is controlled quentity controlled variable of the k moment to system, the zkIt is the calculation matrix or output matrix at k moment, the H is the ginseng of measuring system
Number, the wkAnd vkProcess noise and measurement noise is represented respectively, and the standard deviation of the process noise is R, the measurement noise
Standard deviation is Q.
For the ultrasonic doppler echo-signal after quadrature phase (in-phase＆quadrature) demodulation, state vector
Can be expressed as：
wdFor flow Doppler frequency displacement, the φtFor the phase place change of echo-signal, the nkIt is for random noise, described
Amplitudes of the Amp for signal.
Design parameter model realization Power estimation, to determine the observing matrix of system, state vector controls vector, noise mark
Quasi- difference parameter etc..In Doppler frequency spectrum is estimated, observing matrix is zero to nyquist frequency fnComprehensive observation：
Each h corresponds to estimated frequency response point, can realize that optional frequency is estimated to arrange by ω：
H=e-jwt=cos (wt)-j sin (wt)
Additionally, A is unit matrix；The ukIn the case of non-existent, the B is 0；By the setting of parameter R, Ke Yiren
Meaning regulating frequency responsive bandwidth, as shown in figure 4, and the setting of parameter Q, then affect wave filter cross-spectrum optimization process.
Step 102, builds dynamic transfer equation according to the state-space model and linear prediction system, and to described dynamic
State equation of transfer carries out adaptive Kalman filter loop iteration process；
Specifically, it is determined that system state space, after parameter setting, you can change into adaptive Kalman filter circulation
For process.
Plural Kalman initial：
The plural Kalman gain at k moment is calculated：
Optimal estimation amendment:
Covariance matrix update:
Forward prediction:
Wherein, the I be unit matrix, the GkWith the PkFor K when the Kalman gain inscribed and covariance matrix,
Subscript ^ be estimated value, subscript-For priori value, p0Initial value is inversely proportional to the signal to noise ratio snr of measurement process, x0For 0 or first
Data point, its difference are on the premise of algorithmic statement and little.In sum, the state transition diagram of system, such as Fig. 5 can be obtained
It is shown.
Preferably, it is described that dynamic transfer equation is built according to the state-space model and linear prediction system, and to institute
Stating dynamic transfer equation carries out adaptive Kalman filter loop iteration process, including：
The plural Kalman of initialization
Calculate the plural Kalman gain at K moment
Optimal estimation amendment
Update covariance matrix
Forward prediction
Wherein, the I be unit matrix, the GkWith the PkFor K when the Kalman gain inscribed and covariance matrix,
Subscript ^ be estimated value, subscript-For priori value, p0Initial value is inversely proportional to the signal to noise ratio snr of measurement process, x0For 0 or first
Data point.
Step 103, generates blood relationship frequency spectrum during the adaptive Kalman filter loop iteration.
Specifically, algorithm can realize more accurate spectrum estimation, be applied in blood flow Doppler spectrum imaging, parameter
Setting can accomplish to estimate optional frequency that frequency response is compensated so that blood flow frequency spectrum axis information is more satisfied than STFT algorithm
It is full with it is coherent, as shown in Figure 6.
A kind of method of blood relationship spectral imaging provided in an embodiment of the present invention, sets up system by Kalman filtering algorithm
State-space model, the state-space model include state equation and measurement equation；According to the state-space model and line
Property prognoses system build dynamic transfer equation, and adaptive Kalman filter loop iteration mistake is carried out to the dynamic transfer equation
Journey；Blood relationship frequency spectrum is generated during the adaptive Kalman filter loop iteration.The goldstandard of Power estimation is calculated by the present invention
Method (plural Kalman Filter Estimation) is applied to blood flow frequency spectrum imaging, and there is Kalman filtering Power estimation algorithm accurate speed to estimate
Meter and direction estimation ability, variance performance are optimal；The flexible adjustability of Kalman filtering Power estimation parameter model is arranged, can be with
Optional frequency Composition Estimation is realized, is not affected by data length, and optional frequency composition is filtered or is compensated, occupation mode is more
Flexibility and changeability；The self adaptation circulation of Kalman filtering Power estimation algorithm with splendid robustness, with eliminate suppress Gauss or
The ability of its non-Gaussian noise, its structure can also realize the imaging mode of single-point input spectrum output, and the time delay of imaging is minimum；
In blood flow frequency spectrum imaging, priori can be fully used, optimal spectral resolution, temporal resolution are gone out with parameter setting
Mode realize realtime imaging.
With reference to Fig. 7, Fig. 7 is that a kind of functional module of the device of blood relationship spectral imaging provided in an embodiment of the present invention is illustrated
Figure.
As shown in fig. 7, described device includes：
Module 701 is set up, for setting up the state-space model of system, the state space by Kalman filtering algorithm
Model includes state equation and measurement equation；
Iteration module 702 is built, for dynamic transfer side being built according to the state-space model and linear prediction system
Journey, and adaptive Kalman filter loop iteration process is carried out to the dynamic transfer equation；
Generation module 703, for generating blood relationship frequency spectrum during the adaptive Kalman filter loop iteration.
Preferably, it is described to set up module 701, specifically for：
The state-space model of signal and noise is set up respectively, using the state variable estimate of previous moment to present tense
Quarter is carried out according to a preliminary estimate, and parameter is estimated in the observation amendment with reference to present moment；
The state variable estimate is updated, the optimum state variable estimated value of present moment is solved.
Preferably, it is described to set up module 701, also particularly useful for：
State equation x is set up by the Kalman filtering algorithmk+1=Axk+Buk+wkWith measurement equation zk=Hkxk+vk；
Wherein, the x and z are to be input into and export, and the A and B is systematic parameter, the xkIt is the state vector at k moment,
The ukIt is controlled quentity controlled variable of the k moment to system, the zkIt is the calculation matrix or output matrix at k moment, the H is measuring system
Parameter, the wkAnd vkRepresent process noise and measurement noise respectively, the standard deviation of the process noise is R, the measurement is made an uproar
The standard deviation of sound is Q.
Preferably, it is describedThe wdFor flow Doppler frequency displacement, institute
State φtFor the phase place change of echo-signal, the nkFor random noise, amplitudes of the Amp for signal；
It is describedThe frequency response point that the h correspondences are estimated, h=e-jwt=cos (wt)-j sin (wt), the A are unit matrix；The ukIn the case of non-existent, the B is 0.
Preferably, the structure iteration module 702, specifically for：
The plural Kalman of initialization
Calculate the plural Kalman gain at K moment
Optimal estimation amendment
Update covariance matrix
Forward prediction
Wherein, the I be unit matrix, the GkWith the PkFor K when the Kalman gain inscribed and covariance matrix,
Subscript ^ be estimated value, subscript-For priori value, p0Initial value is inversely proportional to the signal to noise ratio snr of measurement process, x0For 0 or first
Data point.
A kind of device of blood relationship spectral imaging provided in an embodiment of the present invention, sets up system by Kalman filtering algorithm
State-space model, the state-space model include state equation and measurement equation；According to the state-space model and line
Property prognoses system build dynamic transfer equation, and adaptive Kalman filter loop iteration mistake is carried out to the dynamic transfer equation
Journey；Blood relationship frequency spectrum is generated during the adaptive Kalman filter loop iteration.The goldstandard of Power estimation is calculated by the present invention
Method (plural Kalman Filter Estimation) is applied to blood flow frequency spectrum imaging, and there is Kalman filtering Power estimation algorithm accurate speed to estimate
Meter and direction estimation ability, variance performance are optimal；The flexible adjustability of Kalman filtering Power estimation parameter model is arranged, can be with
Optional frequency Composition Estimation is realized, is not affected by data length, and optional frequency composition is filtered or is compensated, occupation mode is more
Flexibility and changeability；The self adaptation circulation of Kalman filtering Power estimation algorithm with splendid robustness, with eliminate suppress Gauss or
The ability of its non-Gaussian noise, its structure can also realize the imaging mode of single-point input spectrum output, and the time delay of imaging is minimum；
In blood flow frequency spectrum imaging, priori can be fully used, optimal spectral resolution, time resolution are gone out with parameter setting
The mode of rate realizes realtime imaging.
The know-why of the embodiment of the present invention is described above in association with specific embodiment.These descriptions are intended merely to explain this
The principle of inventive embodiments, and the restriction to embodiment of the present invention protection domain can not be construed to by any way.Based on herein
Explanation, those skilled in the art associate by need not paying performing creative labour the embodiment of the present invention other are concrete
Embodiment, these modes are fallen within the protection domain of the embodiment of the present invention.
Claims (10)
1. a kind of method of blood relationship spectral imaging, it is characterised in that methods described includes：
The state-space model of system is set up by Kalman filtering algorithm, the state-space model includes state equation and survey
Amount equation；
Dynamic transfer equation is built according to the state-space model and linear prediction system, and the dynamic transfer equation is entered
Row adaptive Kalman filter loop iteration process；
Blood relationship frequency spectrum is generated during the adaptive Kalman filter loop iteration.
2. method according to claim 1, it is characterised in that the state for setting up system by Kalman filtering algorithm
Spatial model, including：
The state-space model of signal and noise is set up respectively, present moment is entered using the state variable estimate of previous moment
Go according to a preliminary estimate, and parameter is estimated in the observation amendment with reference to present moment；
The state variable estimate is updated, the optimum state variable estimated value of present moment is solved.
3. method according to claim 2, it is characterised in that the state space mould for setting up signal and noise respectively
Type, is carried out according to a preliminary estimate to present moment using the state variable estimate of previous moment, and with reference to the observation of present moment
Parameter is estimated in amendment, including：
State equation x is set up by the Kalman filtering algorithmk+1=Axk+Buk+wkWith measurement equation zk=Hkxk+vk；
Wherein, the x and z are to be input into and export, and the A and B is systematic parameter, the xkIt is the state vector at k moment, it is described
ukIt is controlled quentity controlled variable of the k moment to system, the zkIt is the calculation matrix or output matrix at k moment, the H is the ginseng of measuring system
Number, the wkAnd vkProcess noise and measurement noise is represented respectively, and the standard deviation of the process noise is R, the measurement noise
Standard deviation is Q.
4. method according to claim 3, it is characterised in that described
The wdFor flow Doppler frequency displacement, the φtFor the phase place change of echo-signal, the nkFor random noise, the Amp is
The amplitude of signal；
It is describedThe frequency response point that the h correspondences are estimated, h=e-jwt=
Cos (wt)-j sin (wt), the A are unit matrix；The ukIn the case of non-existent, the B is 0.
5. method according to claim 2, it is characterised in that described according to the state-space model and linear prediction system
System builds dynamic transfer equation, and carries out adaptive Kalman filter loop iteration process to the dynamic transfer equation, including：
The plural Kalman of initialization
Calculate the plural Kalman gain at K moment
Optimal estimation amendment
Update covariance matrix
Forward prediction
Wherein, the I be unit matrix, the GkWith the PkFor K when the Kalman gain inscribed and covariance matrix, subscript ^
For estimated value, subscript-For priori value, p0Initial value is inversely proportional to the signal to noise ratio snr of measurement process, x0For 0 or first data
Point.
6. a kind of device of blood relationship spectral imaging, it is characterised in that described device includes：
Module is set up, for setting up the state-space model of system, the state-space model bag by Kalman filtering algorithm
Include state equation and measurement equation；
Iteration module is built, it is for building dynamic transfer equation according to the state-space model and linear prediction system and right
The dynamic transfer equation carries out adaptive Kalman filter loop iteration process；
Generation module, for generating blood relationship frequency spectrum during the adaptive Kalman filter loop iteration.
7. device according to claim 6, it is characterised in that described to set up module, specifically for：
The state-space model of signal and noise is set up respectively, present moment is entered using the state variable estimate of previous moment
Go according to a preliminary estimate, and parameter is estimated in the observation amendment with reference to present moment；
The state variable estimate is updated, the optimum state variable estimated value of present moment is solved.
8. device according to claim 7, it is characterised in that described to set up module, also particularly useful for：
State equation x is set up by the Kalman filtering algorithmk+1=Axk+Buk+wkWith measurement equation zk=Hkxk+vk；
Wherein, the x and z are to be input into and export, and the A and B is systematic parameter, the xkIt is the state vector at k moment, it is described
ukIt is controlled quentity controlled variable of the k moment to system, the zkIt is the calculation matrix or output matrix at k moment, the H is the ginseng of measuring system
Number, the wkAnd vkProcess noise and measurement noise is represented respectively, and the standard deviation of the process noise is R, the measurement noise
Standard deviation is Q.
9. device according to claim 8, it is characterised in that describedInstitute
State wdFor flow Doppler frequency displacement, the φtFor the phase place change of echo-signal, the nkFor random noise, the Amp is letter
Number amplitude；
It is describedThe frequency response point that the h correspondences are estimated, h=e-jwt=
Cos (wt)-j sin (wt), the A are unit matrix；The ukIn the case of non-existent, the B is 0.
10. device according to claim 7, it is characterised in that the structure iteration module, specifically for：
The plural Kalman of initialization
Calculate the plural Kalman gain at K moment
Optimal estimation amendment
Update covariance matrix
Forward prediction
Wherein, the I be unit matrix, the GkWith the PkFor K when the Kalman gain inscribed and covariance matrix, subscript ^
For estimated value, subscript-For priori value, p0Initial value is inversely proportional to the signal to noise ratio snr of measurement process, x0For 0 or first data
Point.
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