CN108710125A - For target following apart from method of bearing filtering - Google Patents
For target following apart from method of bearing filtering Download PDFInfo
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- CN108710125A CN108710125A CN201810313404.4A CN201810313404A CN108710125A CN 108710125 A CN108710125 A CN 108710125A CN 201810313404 A CN201810313404 A CN 201810313404A CN 108710125 A CN108710125 A CN 108710125A
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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
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/66—Radar-tracking systems; Analogous systems
- G01S13/72—Radar-tracking systems; Analogous systems for two-dimensional tracking, e.g. combination of angle and range tracking, track-while-scan radar
- G01S13/723—Radar-tracking systems; Analogous systems for two-dimensional tracking, e.g. combination of angle and range tracking, track-while-scan radar by using numerical data
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Abstract
The invention discloses a kind of for target following apart from method of bearing filtering, and which solve traditional alpha-betas and alpha-beta-γ filtering methods to consider the not comprehensive enough problem of residual error.The present invention first with coordinate transform method, according to the location parameter of target, the position of target is projected to the direction x, y in rectangular coordinate system, the direction x, y is filtered respectively again, range direction is utilized in filtering and the residual error apart from orthogonal direction is modified prediction result, finally the filter value in the direction x, y is synthesized, the location parameter of target after being filtered, target is more accurately tracked to realize.
Description
Technical field
The present invention relates to target followings and radar data processing technology field, and in particular to one kind being based on alpha-beta and alpha-beta-γ
Filtering apart from method of bearing filtering.
Background technology
Method for tracking target is the important link of radar data processing, and radar is to status number to the tracking of moving target
According to the process being filtered, its effect is to make estimation and prediction to target state.The task of target following is to pass through
It is relevant to be filtered the movement locus for establishing target.Radar system established according to target trajectory during to target state
The estimation and prediction made, to assess the security postures of target and the safe effect of mobility.Therefore, target following link works
The quality of performance directly influences the security effectiveness of radar system.
In view of important function of the target following in promoting radar performance, countries in the world are one in dual-use equal fields
It is straight to pay much attention to and develop this technology, for example, various weapon defence systems, air traffic control system (ATCS), satellite navigation system,
Robot motion's system etc..In traditional filtering algorithm, main linear autoregression filtering, 2 points of extrapolation filtering, Wiener filtering,
Weighted linear regression, Kalman filtering etc., wherein most widely used is Kalman filtering.
Since the calculation amount for the gain matrix for calculating Kalman filter is bigger, in order to reduce the calculating of gain matrix
Amount, generally use constant gain amplifier Kalman filter.It is general to use for uniform motion (CV, Constant Velocity) model
Alpha-beta filters；For uniformly accelerated motion (CA, Constant Acceleration) model, then alpha-beta-γ filtering is used.
Utilizing radar to target into when line trace, the method for use is typically first coordinately transformed, the position of target
It sets parameter (the relative distance R and orientation angles θ i.e. between target and radar observation point) to project in rectangular coordinate system, obtains mesh
It is marked on position, the speed in the directions x and the directions y, α-βfilter or alpha-beta-γ filters is recycled to filter position and speed
Wave processing, the location parameter of target after being filtered, to realize the tracking to target.Due to traditional under rectangular coordinate system
Alpha-beta and alpha-beta-γ filters only considered the residual error in a direction (directions x or the directions y), not account for this direction just
The residual error in the direction of friendship, accordingly, there exist certain filtering errors, affect filter effect.Because of the residual error in the directions x and distance R
Projection in the directions x of projection of the residual error in the directions x and the residual error apart from orthogonal direction it is related, similarly, the residual error in the directions y with
The projection of projection of the residual error of distance R in the directions y and the residual error apart from orthogonal direction in the directions y is related.So in order to reduce
Filtering error improves the filter effect of filter, not only to consider the residual error of range direction, it is also necessary to consider apart from orthogonal direction
Residual error.
Invention content
The present invention overcomes the deficiencies in the prior art, provide it is a kind of for target following apart from method of bearing filtering, use
In solving the problems, such as current technology, there are certain filtering error, filter effect are bad.
In view of the above problem of the prior art, according to one aspect of the present disclosure, the present invention uses following technology
Scheme：
It is a kind of for target following apart from method of bearing filtering, including：
Step 1, the echo data moved according to target judge that target moves with uniform velocity or uniformly accelerated motion, establishes system mould
Type obtains the state equation of target and measures equation；
Step 2 is coordinately transformed, and the location parameter of target is projected to X in rectangular coordinate system, Y-direction；
Step 3 initializes the original state and error covariance of target；
Step 4, at the K-1 moment, the state of K moment targets is predicted by system model, K indicate it is any when
It carves；
Step 5, at the K-1 moment, error covariance is predicted by system model；
Step 6 obtains the smoothing parameter apart from orthogonal direction position, speed, acceleration；
Step 7, the residual error for obtaining X and Y-direction；
Step 8 introduces constant gain amplifier factor-alpha, β, γ, and filtering gain is calculated；
Step 9, the state estimation that K moment targets are calculated.
In order to which the present invention is better achieved, further technical solution is：
Further, in the case that the step 1 judges target for uniform motion, CV models, description target movement are established
State X be two-dimensional vector, i.e.,Wherein x andThe respectively position vector and velocity vector of moving target, then mesh
Target state equation is：
X (k+1)=AX (k)+Cw (k)
WhereinW (k) is the white Gaussian noise that mean value is zero；
Measure equation expression formula be：
Y (k)=DX (k)+v (k)
Wherein D=[1 0], v (k) is the white Gaussian noise that mean value is zero.
Further, the step 2 is coordinately transformed, and the relative distance R of target and orientation angles 0 are projected to right angle
On X, Y-direction in coordinate system：
The distance R of prediction：
Measurement distance R is projected into X, Y-direction：
Further, in the step 3, state variable initial value X0|0, covariance matrix P0|0。
Further, in the step 4, target moves with uniform velocity, X, Y-direction one-step prediction value：
State equation：
Further, in the step 5, one-step prediction covariance meets the prediction error formula of Kalman filter：
Φ in formulaK, K-1For state-transition matrix, ΓK, K-1For noise inputs matrix, QK-1For process noise matrix, PK-1For
Variance of estimaion error matrix, and have：
For two-dimensional moving target；
State-transition matrix
Noise inputs matrixItems are substituted into above formula to obtain：
Further, in the step 6：
Smoothing parameter apart from orthogonal direction position：
Smoothing parameter apart from orthogonal direction speed：
The step 7 obtains the residual error of X and Y-direction：
The residual error of range direction：RESIDR=R- (XK×sinθ+YK×cosθ)
Residual error apart from orthogonal direction：RESIDCR=XK×cosθ-YK×sinθ
The residual error RESID of range directionRAnd the residual error RESID apart from orthogonal directionCRProject to X-direction：
The residual error RESID of range directionRAnd the residual error RESID apart from orthogonal directionCRProject to Y-direction：
The step 8 introduces invariant α, β, filtering gain is calculated：
VAR is the variance of azimuth determination value in formula；
K moment Target state estimators are calculated in the step 9, and filter state estimation is calculated by alpha-beta filtering
The present invention can also be：
In the case where step 1 judges target for uniformly accelerated motion, CA models are established, the state X of description target movement is
Three-dimensional vector, i.e.,Wherein x,WithRespectively the position vector of moving target, velocity vector and acceleration to
Amount, then the state equation of target is：
X (k+1)=AX (k)+Cw (k)
WhereinW (k) is the white Gaussian noise that mean value is zero；
Measure equation expression formula be：
Y (k)=DX (k)+v (k)
Wherein D=[1 0 0], v (k) is the white Gaussian noise that mean value is zero.
Further, the step 2 is coordinately transformed, and the relative distance R of target and orientation angles θ are projected to right angle
On X, Y-direction in coordinate system：
The distance R of prediction：
Measurement distance R is decomposed into X, Y-direction：
Further, the step 3 initializes target original state and error covariance：
State variable initial value X0|0, covariance matrix P0|0。
Further, the step 4 predicts that target is done even by system model at the K-1 moment to K moment targets
Accelerate, X, Y-direction one-step prediction value：
State equation：
The step 5 predicts error covariance by system model at the K-1 moment；One-step prediction covariance
PK, K-1Meet the prediction error formula of Kalman filter：
Φ in formulaK, K-1For state-transition matrix, ΓK, K-1For noise inputs matrix, QK-1For process noise matrix, PK-1For
Variance of estimaion error matrix, and have：
For three-dimensional moving target：
State-transition matrix
Noise inputs matrixItems are substituted into above formula to obtain：
The step 6 obtains the smoothing parameter apart from orthogonal direction position, speed, acceleration：
Smoothing parameter apart from orthogonal direction position：
Smoothing parameter apart from orthogonal direction speed：
Smoothing parameter apart from orthogonal direction acceleration：
The step 7 obtains the residual error of X and Y-direction：
The residual error of range direction：RESIDR=R- (XK×sinθ+YK×cosθ)
Residual error apart from orthogonal direction：RESIDCR=XK×cosθ-YK×sinθ
Target is uniformly accelerated motion, the residual error RESID of range directionRAnd the residual error RESID apart from orthogonal directionCRProjection
To X-direction：
The residual error RESID of range directionRAnd the residual error RESID apart from orthogonal directionCRProject to Y-direction：
The step 8 introduces constant gain amplifier factor-alpha, β, γ, filtering gain is calculated：
VAR is the variance of azimuth determination value in formula；
K moment Target state estimators are calculated in the step 9, and filter state, which is calculated, by alpha-beta-γ filtering estimates
Meter
Compared with prior art, one of beneficial effects of the present invention are：
The present invention it is a kind of for target following apart from method of bearing filtering, be both utilized range direction in filtering
Residual error, but the residual error being utilized apart from orthogonal direction is modified prediction result, therefore can obtain filtering than traditional alpha-beta
Wave, the smaller filtering error of alpha-beta-γ filtering methods, it is more preferable to the filter effect of moving target, it is more acurrate to target to realize
Tracking.
Description of the drawings
It, below will be to embodiment in order to illustrate more clearly of present specification embodiment or technical solution in the prior art
Or attached drawing needed in the description of the prior art is briefly described, it should be apparent that, the accompanying drawings in the following description is only
It is the reference to some embodiments in present specification, for those skilled in the art, what is do not made the creative labor
In the case of, other attached drawings can also be obtained according to these attached drawings.
Fig. 1 is according to the moving target of one embodiment of the invention apart from azimuthal projection schematic diagram.
Fig. 2 is to be illustrated with the application condition apart from azimuth filtering according to traditional alpha-beta filtering of one embodiment of the invention
Figure.
Fig. 3 is to be filtered to show with the application condition apart from azimuth filtering according to traditional alpha-beta-γ of one embodiment of the invention
It is intended to.
Fig. 4 is the flow diagram apart from azimuth filtering according to one embodiment of the invention.
Specific implementation mode
The present invention is described in further detail with reference to embodiment, embodiments of the present invention are not limited thereto.
In conjunction with shown in Fig. 1~Fig. 4, it is a kind of for target following apart from method of bearing filtering, be as follows：
Step 1, the echo data (the relative distance R and orientation angles θ between target and observation point) moved according to target
Judge that target moves with uniform velocity or uniformly accelerated motion, to establish system model, obtain in this method the state equation of target and
Measure equation；
Step 2 is coordinately transformed, and the location parameter (relative distance R and orientation angles θ) of target, which is projected to right angle, to be sat
X, Y-direction in mark system；
Step 3 initializes the original state and error covariance of target；
Step 4, at the K-1 moment, the state of K moment targets is predicted by system model, K indicate any moment；
Step 5, at the K-1 moment, error covariance is predicted by system model；
Step 6 obtains the smoothing parameter apart from orthogonal direction position, speed, acceleration；
Step 7, the residual error for obtaining X and Y-direction；
Step 8 introduces the constant gain amplifier factor d, β, γ, and filtering gain is calculated；
Step 9, the state estimation that K moment targets are calculated.
It is specifically described a kind of detailed process apart from method of bearing filtering for target following below, one kind being used for target
Tracking apart from method of bearing filtering be on the basis of alpha-beta filtering, alpha-beta-γ filtering, first with the method for coordinate transform, root
Distance R is projected on the direction x, y of rectangular coordinate system according to azimuth, then predictive estimation is carried out to the direction x, y respectively, is being filtered
When the residual error of range direction had not only been utilized, but also the residual error being utilized apart from orthogonal direction is modified prediction result, to real
Now to the tracking of moving target.A kind of to be divided into two kinds of situations apart from method of bearing filtering for target following, one kind is suitable for
The target of uniform motion, another kind are suitable for the target of uniformly accelerated motion.
For uniform motion target the specific implementation process is as follows：
Step 1：System model is established according to echo data.
Target moves with uniform velocity, then is CV models.The state X for describing target movement is two-dimensional vector, i.e.,Its
Middle x andThe respectively position vector and velocity vector of moving target, then the state equation of target be：
X (k+1)=AX (k)+Cw (k) (1)
WhereinW (k) is the white Gaussian noise that mean value is zero.
Measure equation expression formula be：
Y (k)=DX (k)+v (k) (2)
Wherein D=[1 0], v (k) is the white Gaussian noise that mean value is zero.
Step 2：It is coordinately transformed, the position (relative distance R and orientation angles θ) of target is projected into rectangular coordinate system
In X, in Y-direction.
The distance R of prediction：
Measurement distance R is projected into X, Y-direction：
Step 3：The original state and error covariance of target are initialized.
State variable initial value X0|0, covariance matrix P0|0。
Step 4：At the K-1 moment, K moment targets are predicted by system model.
Target moves with uniform velocity, X, Y-direction one-step prediction value：
State equation：
Step 5：At the K-1 moment, error covariance is predicted by system model.
It is a Kalman filter apart from azimuth filtering device, one-step prediction varivance matrix (predicts covariance
PK, K-1) meet the prediction error formula of Kalman filter：
Φ in formulaK, K-1For state-transition matrix, ΓK, K-1For noise inputs matrix, QK-1For process noise matrix, PK-1For
Variance of estimaion error matrix (i.e. the covariance of error), and have：
For two-dimensional moving target；
State-transition matrix
Noise inputs matrixItems are substituted into above formula to obtain：
Step 6：Obtain the smoothing parameter apart from orthogonal direction position, speed.
Smoothing parameter apart from orthogonal direction position：
Smoothing parameter apart from orthogonal direction speed：Step 7：Obtain X
With the residual error of Y-direction.
The residual error of range direction：RESIDR=R- (XK×sinθ+YK×cosθ) (13)
Residual error apart from orthogonal direction：RESIDCR=XK×cosθ-YKThe residual error of × sin θ (14) range direction
RESIDRAnd the residual error RESID apart from orthogonal directionCRProject to X-direction：
The residual error RESID of range directionRAnd the residual error RESID apart from orthogonal directionCRProject to Y-direction：
Step 8：Invariant α, β is introduced, filtering gain is calculated.
VAR is the variance of azimuth determination value in formula.
Step 9：K moment Target state estimators are calculated.
Filter state estimation is calculated finally by alpha-beta filtering
Below for uniformly accelerated motion target the specific implementation process is as follows：
Step 1：System model is established according to echo data.
Target does uniformly accelerated motion, then is CA models.The state X for describing target movement is three-dimensional vector, i.e.,
Wherein x,WithThe respectively position vector of moving target, velocity vector and vector acceleration, then the state equation of target be：
X (k+1)=AX (k)+Cw (k) (21)
WhereinW (k) is the white Gaussian noise that mean value is zero.
Measure equation expression formula be：
Y (k)=DX (k)+v (k) (22)
Wherein D=[1 0 0], v (k) is the white Gaussian noise that mean value is zero.
Step 2：It is coordinately transformed, the position (relative distance R and orientation angles θ) of target is projected into rectangular coordinate system
In X, in Y-direction.
The distance R of prediction：
Measurement distance R is decomposed into X, Y-direction：
Step 3：Target original state and error covariance are initialized.
State variable initial value X0|0, covariance matrix P0|0。
Step 4：At the K-1 moment, K moment targets are predicted by system model.
Target does uniformly accelerated motion, X, Y-direction one-step prediction value：
State equation：
Step 5：At the K-1 moment, error covariance is predicted by system model.
It is a Kalman filter apart from azimuth filtering device, one-step prediction varivance matrix (predicts covariance
PK, K-1) meet the prediction error formula of Kalman filter：
Φ in formulaK, K-1For state-transition matrix, ΓK, K-1For noise inputs matrix, QK-1For process noise matrix, PK-1For
Variance of estimaion error matrix (i.e. the covariance of error), and have：
For three-dimensional moving target；
State-transition matrix
Noise inputs matrixItems are substituted into above formula to obtain：
Step 6：Obtain the smoothing parameter apart from orthogonal direction position, speed, acceleration.
Smoothing parameter apart from orthogonal direction position：
Smoothing parameter apart from orthogonal direction speed：
Smoothing parameter apart from orthogonal direction acceleration：
Step 7：Obtain the residual error of X and Y-direction.
The residual error of range direction：RESIDR=R- (XK×sinθ+YK×cosθ) (34)
Residual error apart from orthogonal direction：RESIDCR=XK×cosθ-YK×sinθ (35)
Target is uniformly accelerated motion, the residual error RESID of range directionRAnd the residual error RESID apart from orthogonal directionCRProjection
To X-direction：
The residual error RESID of range directionRAnd the residual error RESID apart from orthogonal directioncRProject to Y-direction：
Step 8：Constant gain amplifier factor-alpha, β, γ are introduced, filtering gain is calculated.
VAR is the variance of azimuth determination value in formula.
Step 9：K moment Target state estimators are calculated.
Filter state estimation is calculated finally by alpha-beta-γ filtering
For to sum up, the present invention solves traditional alpha-beta and alpha-beta-γ filtering methods consider that residual error is not comprehensive enough, only examines
The residual error for having considered range direction, the problem of not accounting for the residual error apart from orthogonal direction；The present invention first with coordinate transform side
Method projects to the position of target according to the location parameter (the relative distance R and orientation angles θ between observation point) of target
The direction x, y in rectangular coordinate system, then the direction x, y is filtered respectively, range direction and distance are being utilized in filtering just
It hands over the residual error in direction to be modified prediction result, finally synthesizes the filter value in the direction x, y, target after being filtered
Location parameter more accurately tracks target to realize.
What each embodiment stressed is all the difference with other embodiments in this specification, each embodiment it
Between identical similar portion cross-reference." one embodiment " for being spoken of in the present specification, " another embodiment ",
" embodiment " etc. refers to that combining specific features, structure or the feature of embodiment description to be included in the application generality retouches
In at least one embodiment stated.It is not centainly to refer to the same implementation that statement of the same race, which occur, in multiple places in the description
Example.Furthermore, it is understood that when describing a specific features, structure or feature in conjunction with any embodiment, what is advocated is to combine
Other embodiments realize that this feature, structure or feature are also fallen within the scope of the present invention.
Although reference be made herein to invention has been described for multiple explanatory embodiments of the invention, however, it is to be understood that
Those skilled in the art can be designed that many other modification and implementations, these modifications and implementations will be fallen in this Shen
It please be within disclosed scope and spirit.It more specifically, within the scope of the present disclosure and claims, can be to master
The building block and/or layout for inscribing composite configuration carry out a variety of variations and modifications.In addition to what is carried out to building block and/or layout
Outside variations and modifications, to those skilled in the art, other purposes also will be apparent.
Claims (10)
1. it is a kind of for target following apart from method of bearing filtering, it is characterised in that including：
Step 1, the echo data moved according to target judge that target moves with uniform velocity or uniformly accelerated motion, establishes system model,
It obtains the state equation of target and measures equation；
Step 2 is coordinately transformed, and the location parameter of target is projected to X in rectangular coordinate system, Y-direction；
Step 3 initializes the original state and error covariance of target；
Step 4, at the K-1 moment, the state of K moment targets is predicted by system model, K indicate any moment；
Step 5, at the K-1 moment, error covariance is predicted by system model；
Step 6 obtains the smoothing parameter apart from orthogonal direction position, speed, acceleration；
Step 7, the residual error for obtaining X and Y-direction；
Step 8 introduces constant gain amplifier factor-alpha, β, γ, and filtering gain is calculated；
Step 9, the state estimation that K moment targets are calculated.
2. it is according to claim 1 for target following apart from method of bearing filtering, it is characterised in that the step 1 judges
In the case that target is uniform motion, CV models are established, the state X of description target movement is two-dimensional vector, i.e.,
Wherein x andThe respectively position vector and velocity vector of moving target, then the state equation of target be：
X (k+1)=AX (k)+Cw (k)
WhereinW (k) is the white Gaussian noise that mean value is zero；
Measure equation expression formula be：
Y (k)=DX (k)+v (k)
Wherein D=[1 0], v (k) is the white Gaussian noise that mean value is zero.
3. it is according to claim 2 for target following apart from method of bearing filtering, it is characterised in that the step 2,
It is coordinately transformed, the relative distance R of target and orientation angles θ is projected into the X in rectangular coordinate system, in Y-direction：
The distance R of prediction：
Measurement distance R is projected into X, Y-direction：
4. it is according to claim 2 for target following apart from method of bearing filtering, it is characterised in that the step 3,
State variable initial value X0|0, covariance matrix P0|0。
5. it is according to claim 2 for target following apart from method of bearing filtering, it is characterised in that the step 4,
Target moves with uniform velocity, X, Y-direction one-step prediction value：
State equation：
6. it is according to claim 2 for target following apart from method of bearing filtering, it is characterised in that the step 5,
One-step prediction covariance meets the prediction error formula of Kalman filter：
Φ in formulaK,K-1For state-transition matrix, ΓK,K-1For noise inputs matrix, QK-1For process noise matrix, PK-1It is missed for estimation
The variance matrix of difference, and have：
For two-dimensional moving target；
State-transition matrixNoise inputs matrix
Items are substituted into above formula to obtain：
7. it is according to claim 2 for target following apart from method of bearing filtering, it is characterised in that the step 6：
Smoothing parameter apart from orthogonal direction position：
Smoothing parameter apart from orthogonal direction speed：
The step 7 obtains the residual error of X and Y-direction：
The residual error of range direction：RESIDR=R- (XK×sinθ+YK×cosθ)
Residual error apart from orthogonal direction：RESIDCR=XK×cosθ-YK×sinθ
The residual error RESID of range directionRAnd the residual error RESID apart from orthogonal directionCRProject to X-direction：
The residual error RESID of range directionRAnd the residual error RESID apart from orthogonal directionCRProject to Y-direction：
The step 8 introduces invariant α, β, filtering gain is calculated：
VAR is the variance of azimuth determination value in formula；
K moment Target state estimators are calculated in the step 9, and filter state estimation is calculated by alpha-beta filtering
8. it is according to claim 1 for target following apart from method of bearing filtering, it is characterised in that the step 1 sentences
In the case that disconnected target is uniformly accelerated motion, CA models are established, the state X of description target movement is three-dimensional vector, i.e.,Wherein x,WithThe respectively position vector of moving target, velocity vector and vector acceleration, then target
State equation is：
X (k+1)=AX (k)+Cw (k)
WhereinW (k) is the white Gaussian noise that mean value is zero；
Measure equation expression formula be：
Y (k)=DX (k)+v (k)
Wherein D=[1 0 0], v (k) is the white Gaussian noise that mean value is zero.
9. it is according to claim 8 for target following apart from method of bearing filtering, it is characterised in that the step 2,
It is coordinately transformed, the relative distance R of target and orientation angles θ is projected into the X in rectangular coordinate system, in Y-direction：
The distance R of prediction：
Measurement distance R is decomposed into X, Y-direction：
The step 3 initializes target original state and error covariance：
State variable initial value X0|0, covariance matrix P0|0。
10. it is according to claim 8 for target following apart from method of bearing filtering, it is characterised in that the step 4,
At the K-1 moment, K moment targets are predicted by system model, target does uniformly accelerated motion, X, Y-direction one-step prediction
Value：
State equation：
The step 5 predicts error covariance by system model at the K-1 moment；One-step prediction covariance PK,K-1It is full
The prediction error formula of sufficient Kalman filter：
Φ in formulaK,K-1For state-transition matrix, ΓK,K-1For noise inputs matrix, QK-1For process noise matrix, PK-1It is missed for estimation
The variance matrix of difference, and have：
For three-dimensional moving target：
State-transition matrix
Noise inputs matrixItems are substituted into above formula to obtain：
The step 6 obtains the smoothing parameter apart from orthogonal direction position, speed, acceleration：
Smoothing parameter apart from orthogonal direction position：
Smoothing parameter apart from orthogonal direction speed：
Smoothing parameter apart from orthogonal direction acceleration：
The step 7 obtains the residual error of X and Y-direction：
The residual error of range direction：RESIDR=R- (XK×sinθ+YK×cosθ)
Residual error apart from orthogonal direction：RESIDCR=XK×cosθ-YK×sinθ
Target is uniformly accelerated motion, the residual error RESID of range directionRAnd the residual error RESID apart from orthogonal directionCRProject to X
Direction：
The residual error RESID of range directionRAnd the residual error RESID apart from orthogonal directionCRProject to Y-direction：
The step 8 introduces constant gain amplifier factor-alpha, β, γ, filtering gain is calculated：
VAR is the variance of azimuth determination value in formula；
K moment Target state estimators are calculated in the step 9, and filter state estimation is calculated by alpha-beta-γ filtering
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Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN110231620A (en) * | 2019-07-08 | 2019-09-13 | 哈尔滨工业大学 | A kind of noise correlation system tracking filter method |
| CN111722213A (en) * | 2020-07-03 | 2020-09-29 | 哈尔滨工业大学 | Pure distance extraction method for motion parameters of maneuvering target |
| CN112200830A (en) * | 2020-09-11 | 2021-01-08 | 山东信通电子股份有限公司 | Target tracking method and device |
| CN112630774A (en) * | 2020-12-29 | 2021-04-09 | 北京润科通用技术有限公司 | Target tracking data filtering processing method and device |
| CN112698323A (en) * | 2020-12-10 | 2021-04-23 | 中国航空工业集团公司沈阳飞机设计研究所 | Full-automatic landing radar guiding noise suppression method based on alpha-beta-gamma filter |
| CN113077492A (en) * | 2021-04-26 | 2021-07-06 | 北京华捷艾米科技有限公司 | Position tracking method, device, equipment and storage medium |
| CN114460572A (en) * | 2020-11-10 | 2022-05-10 | 西安开阳微电子有限公司 | Vehicle-mounted millimeter wave radar Kalman target tracking method |
| WO2023024264A1 (en) * | 2021-08-23 | 2023-03-02 | 五邑大学 | Trajectory filtering method and apparatus based on numerical control machining system, and electronic device |
| CN118244631A (en) * | 2024-02-26 | 2024-06-25 | 中国电子科技集团公司第三十八研究所 | Target tracking method and device under maneuvering platform |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2000147106A (en) * | 1998-11-13 | 2000-05-26 | Mitsubishi Electric Corp | Tracking device and its tracking processing method |
| CN102064799A (en) * | 2010-12-31 | 2011-05-18 | 南京理工大学 | Method for designing DCMFK (Debiased Converted Measurement Kalman filter) based on FPGA (Field Programmable Gate Array) |
| US20150219758A1 (en) * | 2014-01-31 | 2015-08-06 | Applied Concepts, Inc. | Mobile radar and visual tracking coordinate transformation |
| CN104833967A (en) * | 2015-05-11 | 2015-08-12 | 重庆大学 | Radar target tracking method based on moving horizon estimation |
| CN104849697A (en) * | 2015-05-15 | 2015-08-19 | 重庆大学 | Alpha-beta filter method based on depolarization coordinate transformation |
| CN107045125A (en) * | 2017-03-17 | 2017-08-15 | 电子科技大学 | A kind of Interactive Multiple-Model radar target tracking method based on predicted value measurement conversion |
-
2018
- 2018-04-09 CN CN201810313404.4A patent/CN108710125A/en not_active Withdrawn
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2000147106A (en) * | 1998-11-13 | 2000-05-26 | Mitsubishi Electric Corp | Tracking device and its tracking processing method |
| CN102064799A (en) * | 2010-12-31 | 2011-05-18 | 南京理工大学 | Method for designing DCMFK (Debiased Converted Measurement Kalman filter) based on FPGA (Field Programmable Gate Array) |
| US20150219758A1 (en) * | 2014-01-31 | 2015-08-06 | Applied Concepts, Inc. | Mobile radar and visual tracking coordinate transformation |
| CN104833967A (en) * | 2015-05-11 | 2015-08-12 | 重庆大学 | Radar target tracking method based on moving horizon estimation |
| CN104849697A (en) * | 2015-05-15 | 2015-08-19 | 重庆大学 | Alpha-beta filter method based on depolarization coordinate transformation |
| CN107045125A (en) * | 2017-03-17 | 2017-08-15 | 电子科技大学 | A kind of Interactive Multiple-Model radar target tracking method based on predicted value measurement conversion |
Non-Patent Citations (3)
| Title |
|---|
| 于翔川: "基于非线性滤波的目标跟踪算法研究", 《中国优秀硕士学位论文全文数据库信息科技辑》 * |
| 尚志龙: "基于多传感器信息融合的目标跟踪技术研究", 《中国优秀硕士学位论文全文数据库信息科技辑》 * |
| 郝中豪: "CAS防撞算法的目标跟踪技术的研究与实现", 《中国优秀硕士学位论文全文数据库工程科技Ⅱ辑》 * |
Cited By (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110231620A (en) * | 2019-07-08 | 2019-09-13 | 哈尔滨工业大学 | A kind of noise correlation system tracking filter method |
| CN111722213A (en) * | 2020-07-03 | 2020-09-29 | 哈尔滨工业大学 | Pure distance extraction method for motion parameters of maneuvering target |
| CN111722213B (en) * | 2020-07-03 | 2023-11-03 | 哈尔滨工业大学 | Pure distance extraction method for maneuvering target motion parameters |
| CN112200830A (en) * | 2020-09-11 | 2021-01-08 | 山东信通电子股份有限公司 | Target tracking method and device |
| CN114460572A (en) * | 2020-11-10 | 2022-05-10 | 西安开阳微电子有限公司 | Vehicle-mounted millimeter wave radar Kalman target tracking method |
| CN112698323A (en) * | 2020-12-10 | 2021-04-23 | 中国航空工业集团公司沈阳飞机设计研究所 | Full-automatic landing radar guiding noise suppression method based on alpha-beta-gamma filter |
| CN112698323B (en) * | 2020-12-10 | 2024-03-19 | 中国航空工业集团公司沈阳飞机设计研究所 | Full-automatic landing radar guiding noise suppression method based on alpha-beta-gamma filter |
| CN112630774A (en) * | 2020-12-29 | 2021-04-09 | 北京润科通用技术有限公司 | Target tracking data filtering processing method and device |
| CN113077492A (en) * | 2021-04-26 | 2021-07-06 | 北京华捷艾米科技有限公司 | Position tracking method, device, equipment and storage medium |
| WO2023024264A1 (en) * | 2021-08-23 | 2023-03-02 | 五邑大学 | Trajectory filtering method and apparatus based on numerical control machining system, and electronic device |
| CN118244631A (en) * | 2024-02-26 | 2024-06-25 | 中国电子科技集团公司第三十八研究所 | Target tracking method and device under maneuvering platform |
| CN118244631B (en) * | 2024-02-26 | 2024-10-29 | 中国电子科技集团公司第三十八研究所 | Target tracking method and device under maneuvering platform |
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