CN108169722A - A kind of unknown disturbances influence the system deviation method for registering of lower sensor - Google Patents
A kind of unknown disturbances influence the system deviation method for registering of lower sensor Download PDFInfo
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- CN108169722A CN108169722A CN201711236179.0A CN201711236179A CN108169722A CN 108169722 A CN108169722 A CN 108169722A CN 201711236179 A CN201711236179 A CN 201711236179A CN 108169722 A CN108169722 A CN 108169722A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
- G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
- G01S7/418—Theoretical aspects
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Abstract
The technical solution adopted in the present invention is：The pseudo- measurement equation about sensing system deviation is constructed to the filter value of the state of same target by two sensors first；Next progress is combined with sensing system bias state equation of transfer, forms new sensor measurement system model；Estimation is filtered finally by using filtering algorithm corresponding with the system, so as to estimate the system deviation of each sensor.It can effectively solve the problems, such as that Dynamic Evolution Model in maneuvering target is unknown and is influenced during measurement by extraneous unknown disturbance, and the system deviation of sensor accurately can be calculated using technical solution proposed by the invention.
Description
Technical field
It is unknown dry the present invention relates to the multisensor deviation registration technique field in Multi-source Information Fusion more particularly to one kind
Disturbing influences the system deviation method for registering of lower sensor.
Background technology
Information fusion is a kind of multi-level, various processing procedures, including being detected to multi-source data, related, group
Close and estimation, precision so as to improve state and identity estimation and situation of battlefield and the significance level threatened are carried out it is in due course
Complete evaluation.In multi-sensor cooperation tracking, it is the key that data fusion institute to find suitable, optimal fusion method
.Can so that metric data is inaccurate because of many factors when being measured due to single sensor, to sensing system deviation into
Row registration is an important step.
Mainly it is divided to random deviation and system deviation two classes in emerging system, random deviation can be disappeared by filtering method
It removes or statistical property is obtained by mass data measurement and analysis, and then weaken influence of the random deviation to measurement result.And
System deviation belongs to deterministic deviation and can not be eliminated by filtering method, need to estimate it using related algorithm and according to
Result is calibrated or is compensated to practical target measurement according to estimates, and this method is known as system deviation registration.
The registration of sensor refers to required processing procedure during multi-sensor data " zero deflection " conversion, to solve pass more
The registration problems of sensor, traditional registration Algorithm mainly have the offline estimation technique, On-line Estimation method and Combined estimator method three classes.Tradition
Registration error estimation algorithm research usually assumes that it with certain Dynamic Evolution Model and detection target is with maneuver modeling, but by
In the different of the weather of extraneous different zones, landform and irradiation light, external human interference increases, system itself it is non-thread
Property, multi-model the problems such as presence, can all cause the target movement model of description system be difficult to set up and measurement occur
Compared with macromutation, therefore, many traditional registration error estimation algorithms, which are no longer applicable in, solves randomness system deviation under the above situation
Problem.Therefore, the registration error estimation algorithm containing unknown disturbances is particularly important.
It is difficult to set up in the Dynamic Evolution Model of maneuvering target and sensor is extraneous very by electromagnetism, enemy plane, environment etc.
In the case of advising the random unknown disturbance of external interference and influencing, the measurement information of multiple sensors is made full use of, and pass through will be containing not
Know that the state space measurement information of interference projects to sensing system deviation space, eliminate in original measurement information about system shape
The Measurement Biases that state and disturbed belt come avoid the association of target state and sensing system deviation, and then design a kind of nothing
Inclined wave filter realizes the unbiased esti-mator to sensing system deviation, reaches and the measuring track of goal systems to be measured is registrated in real time
Purpose.
Invention content
The purpose of the present invention is to provide the system deviation method for registering that a kind of unknown disturbances influence lower sensor, this method
Dynamic Evolution Model in maneuvering target is unknown and in the case of being influenced during measurement by extraneous unknown disturbance, Ke Yizhun
The system deviation of true estimated sensor.
To achieve the above object, the present invention adopts the following technical scheme that, the system that a kind of unknown disturbances influence lower sensor
Deviation method for registering, which is characterized in that specifically include following steps：
Step 1：Goal systems to be measured is initialized；
Step 2：The system state space model of the object to be measured system is established, the system state space model includes
System deviation model, dbjective state model, measurement model；
Step 3：Measuring value and Kalman filtering algorithm in the measurement model, by the measuring value by the mesh
The corresponding dbjective state space of mark state model is transformed into the corresponding system deviation space of the system deviation model, is closed with constructing
In the pseudo- measurement equation of the system deviation of the sensor；
Step 4：According to the pseudo- measurement equation and system deviation state transition equation, it is filtered device design；
Step 5：The system deviation of the sensor is calculated using the wave filter；
Step 6：The system deviation obtains system deviation estimated value, according to the system deviation estimated value to the sensing
The measuring value of device carries out registration operation.
Further, in the present invention, in the step 1, the initialization includes inclined to the system of each sensor
Noise covariance value in difference, the systematic state transfer equation and system measurements equation is configured.
Further, in the present invention, in the step 2, the sensor in the system state space model corresponds to
The system deviation model, the measurement model and the systematic state transfer model equation be respectively：
X (k+1)=Fxx(k)+vx(k)
Wherein, k-th of sampling instant of k expressions, next sampling instant of k+1 k-th of sampling instant of expression, k=1,
2 ..., L, L are expressed as the sampling instant number of setting；I represents the sensor serial number, i=1,2 ..., m, and m represents the biography
The sum of sensor；For the sensor i, bi(k) be k-th of sampling instant system deviation vector,It is system deviation
State-transition matrix；It is that the measurement containing system deviation is vectorial, HiIt is measurement matrix, x (k) is system mode vector, GiIt is
The sensor i is driven u during measurement by the Unknown worm without any priorii(k) control matrix；X (k) tables
Show state of the moving target in k-th of sampling instant, FxRepresent the state-transition matrix of aims of systems movement；The sensor i
System noiseWith observation noise wiAnd state-noise vxCovariance is known and mutual indepedent.
Further, in the present invention, in the step 3, according to the measuring value and the Kalman filtering algorithm, by institute
It states measuring value and the system deviation space is transformed by the system state space, construct the system about the sensor
The pseudo- measurement equation of deviation, the mesh to be measured is defined using the sensor to the observational equation of the object to be measured system
The unbiased measurement equation of mark system is：Z (k)=Hix(k)+Giui(k)+wi(k), with the Kalman filtering algorithm to the biography
Sensor i is filtered, and the state filtering value for obtaining the k+1 moment is：
Wherein, Ki(k) it represents Kalman filtering gains of the sensor i at the k moment, will be obtained after formula (1) deformation：
Wherein,For Ki(k+1) generalized inverse,For the state estimation at k+1 moment, I is single
Bit matrix,State estimation for the k moment.
Further, in the present invention, for the same object to be measured system, there are the m sensors, appoint and take two
Sensor is set as sensor α, sensor β, then the sensor α is identical with the measurement environment of the sensor β, then Hα=Hβ,
Gα=Gβ；And the sensor α has the identical systematic state transfer equation with the sensor β, it is described so as to eliminate
The corresponding public amount of sensor α described in systematic state transfer equation and the sensor β are to get to the institute of the system deviation
State pseudo- measurement equation：
Wherein,
The object to be measured system when having the m sensors, hasA pseudo- measurement equation.
Further, in the present invention, in the step 4, using the obtained pseudo- measurement equation with reference to the sensor
The system deviation state transition equation, be filtered device design：
zb(k+1)=Hb(k+1)b(k+1)+W(k+1)
Using the Kalman filtering algorithm, it is worth to currently by the estimated value of previous moment and the observation at current time
The estimated value at moment, i.e., the registration error estimation value b of described sensori(k+1)。
Further, in the present invention, in the step 5, in cartesian coordinate system, the system deviation of the sensor i
System deviation in x-axis and y-axis direction is respectively xk(i) and yk(i), then bi(k)=x [k(i),yk(i)]T, wherein, ()TIt represents
Matrix transposition can obtain the system deviations of the sensor i in x-axis and y-axis direction using the wave filter designed by step 4 and estimate
EvaluationWith
Further, in the present invention, in the step 6, according to the registration error estimation value to the institute of the sensor
It states measuring value to be registrated, registration expression formula is：
Wherein,WithSensor i is illustrated respectively in cartesian coordinate system in k-th of sampling instant object to be measured
Measuring value after the x-axis of system and y-axis direction registration,WithSensor i is illustrated respectively in cartesian coordinate system
The x-axis and y-axis direction actual value of k sampling instant object to be measured system；WithThe Kalman filtering is represented respectively
The registration error estimation value that algorithm is obtained in x-axis and y-axis direction.
Further, in the present invention, there is step 7：Repeating said steps 2 are to the step 6, until in all samplings
It carves, to the object to be measured system, completes the estimation of the system deviation of the sensor, carry out matching for measuring track
Standard is registrated in real time with the measuring track for realizing the object to be measured system.
Present invention is generally directed to systematic state transfer equation excessively complexity or there is no systematic state transfer equation, and
The Multisensor Measurement system that sensor is influenced during measurement by external disturbance.First by two sensors to same
The filter value of the state of one target constructs the pseudo- measurement equation about sensing system deviation；Then progress and sensing system
The joint of bias state equation of transfer forms new sensor measurement system model；It is corresponding with the system finally by utilizing
Filtering algorithm is filtered estimation, so as to estimate the system deviation of each sensor.
Description of the drawings
Fig. 1 is the flow chart of the present invention.
Specific embodiment
Lower mask body combination attached drawing, is described in detail technical scheme of the present invention.
As shown in Figure 1, unknown disturbances of the present invention influence the system deviation method for registering of lower sensor, including following
Step：
(1) goal systems to be measured is initialized；
Specifically the system deviation and covariance value of the object to be measured system of foundation are initialized.
Specifically, the initialization includes system deviation value to each sensor, the systematic state transfer side
Noise covariance value in journey and system measurements equation is configured.
(2) the system state space module of the object to be measured system is established, the system state space model is including being
System buggy model, dbjective state model, measurement model；
The corresponding system deviation models of the sensor i, the measurement mould in the system state space model
Type and the systematic state transfer model equation are respectively：
X (k+1)=Fxx(k)+vx(k)
Wherein, k-th of sampling instant of k expressions, next sampling instant of k+1 k-th of sampling instant of expression, k=1,
2 ..., L, L are expressed as the sampling instant number of setting；I represents the sensor serial number, i=1,2 ..., m, and m represents the biography
The sum of sensor；For the sensor i, bi(k) be k-th of sampling instant system deviation vector,It is system deviation
State-transition matrix；It is that the measurement containing system deviation is vectorial, HiIt is measurement matrix, x (k) is system mode vector, GiIt is
The sensor i is driven u during measurement by the Unknown worm without any priorii(k) control matrix；X (k) tables
Show state of the moving target in k-th of sampling instant, FxRepresent the state-transition matrix of aims of systems movement；The sensor i
System noiseWith observation noise wiAnd state-noise vxCovariance is known and mutual indepedent.
(3) measuring value and Kalman filtering algorithm in the measurement model, by the measuring value by the target
The corresponding dbjective state space of state model block is transformed into the corresponding system deviation space of the system deviation model, is closed with constructing
In the pseudo- measurement equation of the system deviation of the sensor；
The unbiased measurement equation that can define object to be measured system to the observational equation of target according to sensor is：Z (k)=
Hix(k)+Giui(k)+wi(k), sensor i is filtered with kalman filtering theory, obtains the state filtering value at k+1 moment
For：
Wherein, Ki(k) represent the Kalman filtering gain of the sensor i at the k moment, by above formula (1) deform with
After can obtain：
Wherein,For Ki(k+1) generalized inverse,For the state estimation at k+1 moment, I is single
Position battle array,State estimation for the k moment.
Same target is observed in cartesian coordinate system with two sensors and is assumed in identical measuring environment
In measurement model for, for the same object to be measured system, there are the m sensors, appoint and take two sensors, if
For sensor α, sensor β, then the sensor α is identical with the measurement environment of the sensor β, then Hα=Hβ, Gα=Gβ；And
The sensor α has the identical systematic state transfer equation with the sensor β, so as to eliminate the system mode
Sensor α described in equation of transfer is measured with the corresponding public amount of the sensor β to get the puppet to the system deviation
Equation：
Wherein,
The object to be measured system when having the m sensors, hasA pseudo- measurement equation.
Number of sensors is the more higher to target tracking accuracy, when number of sensors is more than 2, makes using the above method
Sensor combination of two is so as to obtainA puppet measurement equation.
(4) the sensing system bias state transfer side of pseudo- measurement equation joint step (2) newly obtained using step (3)
Journey is filtered device design；
By the sensing system bias state equation of transfer of pseudo- measurement equation joint step (2) obtained in step (3), into
Line filter designs：
Using the Kalman filtering algorithm, it is worth to currently by the estimated value of previous moment and the observation at current time
The estimated value at moment, i.e., the registration error estimation value b of described sensori(k+1)。
Specifically, the crucial derivation used in the step 4 is as follows：
1. calculate the k+1 moment system deviation predicted values of the sensor iAnd its covariance matrix Pi(k+1|
k)：
2. calculate gain matrix：
3. calculate the k+1 moment registration error estimation values of the sensor iAnd its covariance matrix Pi(k+1)：
Wherein,TThe transposition of representing matrix,-1Representing matrix it is inverse, in addition, for sensor i,Represent etching system during k
Estimation of deviation value,Represent k+1 moment registration error estimation values,Represent k+1 moment system deviation predicted values；
Pi(k) k moment system deviation covariances, P are representedi(k+1) k+1 moment system deviation covariances, P are representedi(k+1 | k) represent k+1
Moment system deviation predicts covariance；Represent measuring value of the k+1 moment containing system deviation,When representing k+1
Carve the estimated value measured；Represent system deviation state-transition matrix, HbRepresent the measurement matrix of pseudo- measurement equation, Ki(k+1)
Represent optimum gain matrix, in addition QbAnd QwCorresponding noise covariance is represented respectively.
(5) system deviation of the sensor is calculated using the wave filter；
Since sensor measures target in cartesian coordinate system, it is assumed that sensor i system deviations are included in x-axis and y
The system deviation x of axis directionk(i) and yk(i), i.e. bi(k)=[xk(i),yk(i)]T.Utilize the wave filter designed by step (4)
It can obtain registration error estimation values of the sensor i in x-axis and y-axis directionWith
(6) system deviation obtains system deviation estimated value, according to the system deviation estimated value to the sensor
The measuring value carry out registration operation, registration expression formula be：
Wherein,WithBe illustrated respectively in sensor i in cartesian coordinate system detected in k-th of sampling instant it is motor-driven
Measuring value after the x-axis of target and y-axis direction registration,WithSensor i in cartesian coordinate system is illustrated respectively in exist
K-th of sampling instant detects the x-axis of maneuvering target and the actual value in y-axis direction；WithIt represents to carry algorithm respectively
In the registration error estimation value that x-axis and y-axis direction obtain.
(7) repeating said steps 2 are to the step 6, until in all sampling instants, to the object to be measured system,
The estimation of the system deviation of the sensor is completed, carries out the registration of measuring track, to realize the object to be measured system
Measuring track be registrated in real time.
The present invention can be difficult to set up and be measured by extraneous random unknown disturbances in the Dynamic Evolution Model in maneuvering target
Under the complex situations of influence, multisensor syste deviation is accurately estimated, realizes and target measuring track is registrated in real time.
Above example is merely illustrative of the invention's technical idea, it is impossible to protection scope of the present invention is limited with this, it is every
According to technological thought proposed by the present invention, any change done on the basis of technical solution each falls within protection model of the invention
Within enclosing.
Claims (9)
1. a kind of unknown disturbances influence the system deviation method for registering of lower sensor, which is characterized in that include the following steps：
Step 1：Goal systems to be measured is initialized；
Step 2：The system state space model of the object to be measured system is established, the system state space model includes system
Buggy model, dbjective state model, measurement model；
Step 3：Measuring value and Kalman filtering algorithm in the measurement model, by the measuring value by the target-like
The corresponding dbjective state space of states model is transformed into the corresponding system deviation space of the system deviation model, to construct about institute
State the pseudo- measurement equation of the system deviation of sensor；
Step 4：According to the pseudo- measurement equation and system deviation state transition equation, it is filtered device design；
Step 5：The system deviation of the sensor is calculated using the wave filter；
Step 6：The system deviation obtains system deviation estimated value, according to the system deviation estimated value to the sensor
The measuring value carries out registration operation.
2. a kind of unknown disturbances according to claim 1 influence the system deviation method for registering of lower sensor, feature exists
In：In the step 1, the initialization includes system deviation value to each sensor, the systematic state transfer side
Noise covariance value in journey and system measurements equation is configured.
3. a kind of unknown disturbances according to claim 1 influence the system deviation method for registering of lower sensor, feature exists
In：It is the corresponding system deviation model of the sensor in the system state space model, described in the step 2
Measurement model and the systematic state transfer model equation are respectively：
X (k+1)=Fxx(k)+vx(k)
Wherein, k-th of sampling instant of k expressions, next sampling instant of k+1 k-th of sampling instant of expression, k=1,2 ..., L,
L is expressed as the sampling instant number of setting；I represents the sensor serial number, i=1,2 ..., m, and m represents the total of the sensor
Number；For the sensor i, bi(k) be k-th of sampling instant system deviation vector,It is the transfer of system deviation state
Matrix；It is that the measurement containing system deviation is vectorial, HiIt is measurement matrix, x (k) is system mode vector, GiIt is the sensing
Device i is driven u during measurement by the Unknown worm without any priorii(k) control matrix；X (k) represents movement mesh
It is marked on the state of k-th of sampling instant, FxRepresent the state-transition matrix of aims of systems movement；The system noise of the sensor i
SoundWith observation noise wiAnd state-noise vxCovariance is known and mutual indepedent.
4. a kind of unknown disturbances according to claim 1 influence the system deviation method for registering of lower sensor, feature exists
In：In the step 3, according to the measuring value and the Kalman filtering algorithm, by the measuring value by the system mode
Space is transformed into the system deviation space, constructs the pseudo- measurement equation of the system deviation about the sensor,
The unbiased measurement equation of the object to be measured system is defined to the observational equation of the object to be measured system using the sensor
For：Z (k)=Hix(k)+Giui(k)+wi(k), the sensor i is filtered with the Kalman filtering algorithm, obtains k+
The state filtering value at 1 moment is：
Wherein, Ki(k) it represents Kalman filtering gains of the sensor i at the k moment, will be obtained after formula (1) deformation：
Wherein,For Ki(k+1) generalized inverse,For the state estimation at k+1 moment, I is unit square
Battle array,State estimation for the k moment.
5. a kind of unknown disturbances according to claim 4 influence the system deviation method for registering of lower sensor, feature exists
In：For the same object to be measured system, there are the m sensors, appoint and take two sensors, be set as sensor α, sense
Device β, then the sensor α is identical with the measurement environment of the sensor β, then Hα=Hβ, Gα=Gβ；And the sensor α and institute
Sensor β is stated with the identical systematic state transfer equation, so as to eliminate described in the systematic state transfer equation
The corresponding public amount of sensor α and the sensor β are to get to the pseudo- measurement equation of the system deviation：
Wherein,
The object to be measured system when having the m sensors, hasA pseudo- measurement equation.
6. a kind of unknown disturbances according to claim 1 influence the system deviation method for registering of lower sensor, feature exists
In：In the step 4, using the obtained pseudo- measurement equation with reference to the system deviation state transfer side of the sensor
Journey is filtered device design：
zb(k+1)=Hb(k+1)b(k+1)+W(k+1)
Using the Kalman filtering algorithm, current time is worth to by the estimated value of previous moment and the observation at current time
Estimated value, i.e., the registration error estimation value b of described sensori(k+1)。
7. a kind of unknown disturbances according to claim 1 influence the system deviation method for registering of lower sensor, feature exists
In：In the step 5, in cartesian coordinate system, the system deviation of the sensor i is in the system deviation in x-axis and y-axis direction
Respectively xk(i) and yk(i), then bi(k)=[xk(i),yk(i)]T, wherein, OTRepresenting matrix transposition, using designed by step 4
Wave filter can obtain the registration error estimation values of the sensor i in x-axis and y-axis directionWith
8. a kind of unknown disturbances according to claim 1 influence the system deviation method for registering of lower sensor, feature exists
In：In the step 6, the measuring value of the sensor is registrated according to the registration error estimation value, is registrated table
It is up to formula：
Wherein,WithSensor i in cartesian coordinate system is illustrated respectively in k-th sampling instant object to be measured system
Measuring value after x-axis and y-axis direction registration,WithSensor i in cartesian coordinate system is illustrated respectively in adopt at k-th
The x-axis of sample moment object to be measured system and y-axis direction actual value；WithRepresent that the Kalman filtering algorithm exists respectively
The registration error estimation value that x-axis and y-axis direction obtain.
9. a kind of unknown disturbances according to claim 1 influence the system deviation method for registering of lower sensor, feature exists
In：With step 7：Repeating said steps 2 are to the step 6, until in all sampling instants, to the object to be measured system,
The estimation of the system deviation of the sensor is completed, carries out the registration of measuring track, to realize the object to be measured system
The measuring track of system is registrated in real time.
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