CN107092808A - A kind of formation target merges the tracking under maneuver model - Google Patents
A kind of formation target merges the tracking under maneuver model Download PDFInfo
- Publication number
- CN107092808A CN107092808A CN201710385783.3A CN201710385783A CN107092808A CN 107092808 A CN107092808 A CN 107092808A CN 201710385783 A CN201710385783 A CN 201710385783A CN 107092808 A CN107092808 A CN 107092808A
- Authority
- CN
- China
- Prior art keywords
- mrow
- mover
- msub
- mtd
- msubsup
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Classifications
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16Z—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS, NOT OTHERWISE PROVIDED FOR
- G16Z99/00—Subject matter not provided for in other main groups of this subclass
Landscapes
- Radar Systems Or Details Thereof (AREA)
Abstract
The present invention proposes the tracking under a kind of formation target merging maneuver model, and this method obtains the state updated value of target, the differentiation then merged based on overall maneuver tracking model, wherein the method for building up of overall maneuver tracking model is：The acquisition that the foundation and association of foundation, clutter deleting madel and the point mark pooled model of the acceleration of entirety formation, the extrapolation for interior target of forming into columns, followed by ripple door are measured first is asked for, is finally that dbjective state is updated.Traditional maneuvering Target Tracking Algorithm to echo complexity because considering not enough, it is difficult to obtain preferable tracking effect, and existing mobile formation target tracking algorism is only simple based on overall studied of forming into columns, the state that each target in mobile formation can not be completed accurately, in real time updates, and the present invention makes up above-mentioned deficiency.
Description
The application is the applying date for September in 2014 18 days, Application No. 2014104784959, entitled " typical machine
The divisional application of the patent application of dynamic formation target tracking modeling method ".
Technical field
The invention belongs to radar information integration technology field, there is provided several typical motor formation target tracking modeling sides
Method.
Background technology
In recent years, with the raising of sensor performance especially resolution ratio, how sharp increasing scholar begins to focus on
The integrated information obtained with multiple sensors improves the tracking performance of formation target, and this make it that formation target tracking field is permitted
There is key issue to be solved more.In formation target motion process, based on specific tactics or purpose, formation target at any time can
Generation is turned, climbed, diving etc. overall motor-driven, the distinctive maneuver model of formation target such as there is also division, merging, disperses,
In this case, form into columns in object construction will change, cause multisensor to form into columns in individual goal resolution state
Increasingly complex, the accurate tracking problem of multisensor mobile formation target becomes very difficult under clutter environment.Traditional many biographies
Sensor maneuvering target tracking technology is difficult to track motor-driven formation target, and main cause is：(1) when division, conjunction occur for formation target
And or it is scattered when, traditional maneuvering target tracking model is no longer matched；(2) target is general relatively near apart in forming into columns, thus echo
Cross influence is serious, along with the influence of clutter, when occurring motor-driven when forming into columns, easily occurs with losing, with phenomenons such as mistakes；(3) utilize
When networking sensor detects formation target, because sensor is different from the angle of target in formation, each sensor is to same motor-driven volume
The acquisition mode of team's target may be inconsistent, realizes the complementation of multi-sensor information and rejects more difficult.Existing mobile formation
Target tracking algorism is mostly based on formation entirety and division, merging is studied, and multi-sensor detection is issued when life is moved and compiled
The flight path replacement problem of target not yet has document report in team, and the Practical Project demand of target tracking domain can not be met.
In view of the above-mentioned problems, being necessary to analyse in depth the amount for the interior target of formation when occurring motor-driven of forming into columns under multi-sensor detection
Characteristic is surveyed, the formation how set up under the typical formation maneuvering patterns such as entirety of forming into columns is motor-driven, divides, merge, disperseing is studied and tracks
Model, is updated with the state for realizing interior target of being formed into columns under each maneuver model.
The content of the invention
The technical problem to be solved
In the flight path maintenance stage, various motor-driven, the relative position knot for interior target echo of now forming into columns can occur for formation target
Scaling, shearing, rotation equiaffine conversion occur for structure, and traditional maneuvering Target Tracking Algorithm is because complicated to mobile formation target echo
Property consider not enough, it is difficult to obtain preferable tracking effect, and existing mobile formation target tracking algorism is only simple based on forming into columns
Entirety is studied, it is impossible to which state that is accurate, completing each target in mobile formation in real time updates.To make up above-mentioned deficiency,
Measurement characteristic when the present invention is based on formation maneuvering, sets up overall motor-driven, division, merges, disperses four kinds of typical mobile formations
Target following model.
Technical scheme
The foundation of typical motor formation target tracking model of the present invention, including following techniqueflow：Form into columns overall
The asking for of acceleration, form into columns in the extrapolation of target, the foundation of association ripple door, clutter deleting madel and point mark pooled model build
The vertical, acquisition of POI mark, the state estimation for interior target of forming into columns.
Beneficial effect
(1) multiframe correlation model is based on, the state updated value of each target in the k-1 moment is taken full advantage of, saves one
In the cycle, shorten the time of track confirmation and point-boat association；
(2) with U1(k-1) each target in is flight path head, it is ensured that the number of flight path and original U after scattered1(k-1) mesh in
Number is marked to coincide；
(3) to clutter robustness preferably, most clutters are eliminated using 3/4 track initiation model, it is ensured that form into columns
The accuracy of interior target following.
Brief description of the drawings
Fig. 1 is form into columns overall, division, merging, scattered maneuver tracking model flow figure；
Embodiment
The present invention is described in further detail below in conjunction with Figure of description.With reference to Figure of description, tool of the invention
The following content of body embodiment point：
First, the foundation of the overall maneuver tracking model of formation
(1) computing device 1 receives to measure collection obtained by k moment detecting devicesUtilize cycle threshold model
Obtain Z1(k), and using U (k-1), formation target U (k) is obtained because occurring acceleration that is overall motor-driven and producing
In formula, T is the sampling period.
(2) computing device 2 receives the output result from computing device 1Obtain each targetpath in U (k-1)
One-step prediction value setWherein, TU(k) it is the target number in formation U (k), ordinary circumstance
Lower TU(k)=TU(k-1)；Then
In formula, F (k) ∈ Rn,nFor state-transition matrix；
(3) computing device 3 receives the output result from computing device 2, withCentered on set up association ripple
Door, if Z1(k) the measurement z ini(k) formula (4) is met, then judges zi(k) fall intoRipple door in.
In formula, l is constant factor, is mainly influenceed by noise and clutter density is measured, and measures noise and clutter density is got over
Greatly, l is bigger.(4) computing device 4 receives the output result of computing device 3, if falling intoAssociate the measurement collection of ripple door
It is combined intoIt is right according to the source difference of sensorClassified, thenIt can be written as
In formula,For(k) sensor s measurement number is derived from；NiFor(k) sensor of measurement source in
Number.
Because each sensor is reported to the point trace set of fusion center to include the true echo and clutter of target in formation,
This is according to NiIt is divided into following three kinds of situations and rejects clutter, it is determined thatAssociation measure
If 1. Ni>=2, it is necessary first to set up point mark pooled model, this trifle is carried out quiet using the broad sense S-D principles distributed
State is interconnected, and each sensor is measured according to static association result and is combined, and eliminates the superfluous of the same target of multiple sensor correspondences
Remaining information, and Effective judgement is carried out to each combination, it then will be received all measuring points in combination and carry out the compression of point mark to obtain
An equivalent measuring point is obtained, realizes that multisensor point mark is interconnected, rejects simultaneouslyIn other marks, reach elimination clutter
Purpose, finally choose equivalent measuring point for interconnection measure
If 2. Ni=1, it is not necessary to set up point mark pooled model, choose hereinIn withSpace length is most
Near measurementMeasured for associationWherein
If 3. Ni=0, it is based onVirtual measurement is obtained to measure for associationAnd
In formula, H (k) is measurement matrix.
(5) computing device 5 receives the output result of computing device 4Afterwards, using interacting multiple algorithm to formation U
(k) i-th of target in is filtered.
In formula, M is Number of Models；For probability of the k moment to the model j of the target i filtering in formation U (k)；Pij(k | k) it is respectively state updated value and covariance updated value based on model j.
(6) the state updated value and covariance updated value that the storage of storage device 6 computing device 5 is exported.
2nd, form into columns and divide the foundation of trace model
Computing device 7 receives the data U (k-1) of k moment storage device 6, because to U (k-1) and U1Or U (k)2(k) for,
Formation there occurs that entirety is motor-driven, so U1And U (k)2(k) the state renewal of each target can be straight based on formation maneuvering trace model in
Obtain.Because U1And U (k)2(k) by U (k-1) divides, so generally
But this is in the k moment and is based respectively on Z1And Z (k)2(k) all flight paths in U (k-1) are continued, so
U1And U (k)2(k) false track is certainly existed in, it is necessary to further delete.But the deletion process of false track is in a detection
It is difficult to complete in cycle, therefore by setting up flight path quality to each moment flight path, utilizes multiframe interconnection mode termination false track
And the division of formation is completed, it is specifically described as：
(1) setFor k moment formation U1(k) the state updated value of the target i in, defining its flight path quality is
In formula,For k-1 moment formation U1(k) flight path quality of the target i in, if the k moment, which is formation, starts hair
It is estranged at the time of split, definitionNiFor formation U1(k) target i associates the sensor of Bo Mennei measurement sources in
Number.
(2) foundation of sliding window
The sliding window of one [k, k+h] is set up, if
Then judge formation U1(k) flight path i is false track in, is deleted；A is relevant with clutter density to delete parameter,
Clutter density is bigger, and a value is smaller.
(3) the k+h moment is located at, ifThen stop the judgement of false track；Otherwise
Increase length of window to continue to differentiate.
3rd, form into columns and merge the foundation of trace model
Computing device 8 receives the data U of storage device 61And U (k-1)2(k-1), based on Z (k), respectively to formation U1(k-1)
And U2(k-1) all targetpaths in carry out state renewal, obtainWith
As formation U1And U (k-1)2(k-1) it is merged into after U (k), U1And U (k)2(k) target in belongs to same formation, each mesh
Space length and motion mode between mark should meet the definition of formation, so firstly the need of based on U1And U (k)2(k) re-start
The segmentation of formation.IfWith
For U1And U (k)2(k) the state updated value of any two target in, if
Then judge that the two targets belong to same formation.In formula, d0For constant threshold；γ is that the obedience free degree is nx's
χ2The threshold value of distribution, here nxFor the dimension of state estimation vector；And
In formula,WithFor the state estimation error covariance of two targets.
What the cycle threshold model completion k moment in being split at this based on formation was formed into columns re-recognizes, if being obtained after identification
One new formationIfThe merging then formed into columns finishes；Otherwise it is sharp
Repeated the above steps with the association formation measurement at k+1 moment, the merging for proceeding to form into columns differentiates.
4th, form into columns and disperse the foundation of trace model
(1) computing device 9 receives the data U of storage device 81(k-1), with U1(k-1) swept for the first time as track initiation process
Resulting measurement set is retouched,
The measurement set that three scanning is obtained after respectively；WithCentered on set up ripple door, ifIt is full
Foot
d′ij[Ri(k-1)+Rj(k)]-1dij≤γ (16)
Then judgeCan be with zj(k) interconnect, and set up potential track D1.In formula, Rj(k) it is corresponding to zj(k)
Measure noise covariance；γ is constant threshold, can be by χ2Distribution table is looked into.
In formula,WithSpeed maximums and minimum value of the respectively target i on x, y direction；ForCovariance.
(2) computing device 10 receives the output result of computing device 9, to potential track D1Linear extrapolation is carried out, and in addition
Centered on pushing away a little, association ripple door Ω (k+1) is set up, it is determined by course extrapolation error covariance.If measuring zi(k+1) pass is fallen into
In Lian Bomen Ω (k+1), it is assumed that ziAnd z (k+1)j(k) angle of line and the flight path is α, if (σ is typically by measurement essence by α≤σ
Degree is determined, in order to ensure that, with the flight path of very high probability initial target, larger σ can be selected), then it is assumed that zi(k+1) can be with D1
Interconnection.Required if there are multiple points and meet, choose the measurement interconnection nearest from extrapolation point.
Fallen into if not measuring in Ω (k+1), by D1Continue linear extrapolation, centered on extrapolating a little, set up follow-up close
Lian Bomen Ω (k+2), its size is determined by course extrapolation error covariance.If measuring zi(k+2) association ripple door Ω (k+2) is fallen into
It is interior, it is assumed that ziAnd z (k+2)i(k+1) angle of line and the flight path is β, if β≤σ, judges zi(k+2) can be with D1Interconnection.
Required if there are multiple points and meet, choose the measurement interconnection nearest from extrapolation point.
If in the 4th time scans, not measuring and falling into subsequent association ripple door Ω (k+2), then delete the potential track.
(3) it is not used for starting a new potential track with the measurement of any Trace Association in each cycle, goes to step
(1)。
Be located at the k+2 moment withIt is combined into for the measurement collection of starting pointThen say
It is z that interconnections of the improving eyesight mark i at k moment and k+1 moment, which is measured,jAnd z (k)m(k+1)。
(4) computing device 11 receives the output result of computing device 10 --- and it is z that interconnections of the target i at the k moment, which is measured,j
(k) after, target is filtered using the thought of IMM models.
Claims (3)
1. a kind of formation target merges the tracking under maneuver model, it is characterised in that obtained based on overall maneuver tracking model
Obtain the state updated value of targetWithThen the differentiation merged, specifically
It is described as：
As formation U1And U (k-1)2(k-1) it is merged into after U (k), U1And U (k)2(k) target in belongs to same formation, each mesh
Space length and motion mode between mark should meet the definition of formation, it is necessary first to based on U1And U (k)2(k) volume is re-started
The segmentation of team；IfWithFor
U1And U (k)2(k) the state updated value of any two target in, if
<mfenced open = "{" close = "">
<mtable>
<mtr>
<mtd>
<mrow>
<mi>d</mi>
<mrow>
<mo>(</mo>
<msup>
<mrow>
<mo>&lsqb;</mo>
<msub>
<mover>
<mi>x</mi>
<mo>^</mo>
</mover>
<mn>1</mn>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<mo>,</mo>
<msub>
<mover>
<mi>y</mi>
<mo>^</mo>
</mover>
<mn>1</mn>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<mo>&rsqb;</mo>
</mrow>
<mi>T</mi>
</msup>
<mo>,</mo>
<msup>
<mrow>
<mo>&lsqb;</mo>
<msub>
<mover>
<mi>x</mi>
<mo>^</mo>
</mover>
<mn>2</mn>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<mo>,</mo>
<msub>
<mover>
<mi>y</mi>
<mo>^</mo>
</mover>
<mn>2</mn>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<mo>&rsqb;</mo>
</mrow>
<mi>T</mi>
</msup>
<mo>)</mo>
</mrow>
<mo><</mo>
<msub>
<mi>d</mi>
<mn>0</mn>
</msub>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msub>
<mover>
<mi>V</mi>
<mo>^</mo>
</mover>
<mn>12</mn>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>|</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<mrow>
<mo>(</mo>
<msubsup>
<mover>
<mi>P</mi>
<mo>^</mo>
</mover>
<mi>v</mi>
<mn>1</mn>
</msubsup>
<mo>(</mo>
<mrow>
<mi>k</mi>
<mo>|</mo>
<mi>k</mi>
</mrow>
<mo>)</mo>
<mo>+</mo>
<msubsup>
<mover>
<mi>P</mi>
<mo>^</mo>
</mover>
<mi>v</mi>
<mn>2</mn>
</msubsup>
<mo>(</mo>
<mrow>
<mi>k</mi>
<mo>|</mo>
<mi>k</mi>
</mrow>
<mo>)</mo>
<mo>)</mo>
</mrow>
<msubsup>
<mover>
<mi>V</mi>
<mo>^</mo>
</mover>
<mn>12</mn>
<mi>T</mi>
</msubsup>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>|</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<mo><</mo>
<mi>&gamma;</mi>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
Then judge that the two targets belong to same formation；In formula, d0For constant threshold；γ is that the obedience free degree is nxχ2Point
The threshold value of cloth, here nxFor the dimension of state estimation vector；And
<mfenced open = "{" close = "">
<mtable>
<mtr>
<mtd>
<mrow>
<msub>
<mover>
<mi>V</mi>
<mo>^</mo>
</mover>
<mn>12</mn>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>|</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
<msup>
<mrow>
<mo>&lsqb;</mo>
<msubsup>
<mover>
<mi>v</mi>
<mo>^</mo>
</mover>
<mn>1</mn>
<mi>x</mi>
</msubsup>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<mo>,</mo>
<msubsup>
<mover>
<mi>v</mi>
<mo>^</mo>
</mover>
<mn>1</mn>
<mi>y</mi>
</msubsup>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<mo>&rsqb;</mo>
</mrow>
<mi>T</mi>
</msup>
<mo>-</mo>
<msup>
<mrow>
<mo>&lsqb;</mo>
<msubsup>
<mover>
<mi>v</mi>
<mo>^</mo>
</mover>
<mn>2</mn>
<mi>x</mi>
</msubsup>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<mo>,</mo>
<msubsup>
<mover>
<mi>v</mi>
<mo>^</mo>
</mover>
<mn>2</mn>
<mi>x</mi>
</msubsup>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<mo>&rsqb;</mo>
</mrow>
<mi>T</mi>
</msup>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msubsup>
<mover>
<mi>P</mi>
<mo>^</mo>
</mover>
<mi>v</mi>
<mn>1</mn>
</msubsup>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>|</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
<mfenced open = "[" close = "]">
<mtable>
<mtr>
<mtd>
<mrow>
<msub>
<mover>
<mi>P</mi>
<mo>^</mo>
</mover>
<mn>1</mn>
</msub>
<mrow>
<mo>(</mo>
<mn>2</mn>
<mo>,</mo>
<mn>2</mn>
<mo>)</mo>
</mrow>
</mrow>
</mtd>
<mtd>
<mrow>
<msub>
<mover>
<mi>P</mi>
<mo>^</mo>
</mover>
<mn>1</mn>
</msub>
<mrow>
<mo>(</mo>
<mn>2</mn>
<mo>,</mo>
<mn>4</mn>
<mo>)</mo>
</mrow>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msub>
<mover>
<mi>P</mi>
<mo>^</mo>
</mover>
<mn>1</mn>
</msub>
<mrow>
<mo>(</mo>
<mn>4</mn>
<mo>,</mo>
<mn>2</mn>
<mo>)</mo>
</mrow>
</mrow>
</mtd>
<mtd>
<mrow>
<msub>
<mover>
<mi>P</mi>
<mo>^</mo>
</mover>
<mn>1</mn>
</msub>
<mrow>
<mo>(</mo>
<mn>4</mn>
<mo>,</mo>
<mn>4</mn>
<mo>)</mo>
</mrow>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msubsup>
<mover>
<mi>P</mi>
<mo>^</mo>
</mover>
<mi>v</mi>
<mn>2</mn>
</msubsup>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>|</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
<mfenced open = "[" close = "]">
<mtable>
<mtr>
<mtd>
<mrow>
<msub>
<mover>
<mi>P</mi>
<mo>^</mo>
</mover>
<mn>2</mn>
</msub>
<mrow>
<mo>(</mo>
<mn>2</mn>
<mo>,</mo>
<mn>2</mn>
<mo>)</mo>
</mrow>
</mrow>
</mtd>
<mtd>
<mrow>
<msub>
<mover>
<mi>P</mi>
<mo>^</mo>
</mover>
<mn>2</mn>
</msub>
<mrow>
<mo>(</mo>
<mn>2</mn>
<mo>,</mo>
<mn>4</mn>
<mo>)</mo>
</mrow>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msub>
<mover>
<mi>P</mi>
<mo>^</mo>
</mover>
<mn>2</mn>
</msub>
<mrow>
<mo>(</mo>
<mn>4</mn>
<mo>,</mo>
<mn>2</mn>
<mo>)</mo>
</mrow>
</mrow>
</mtd>
<mtd>
<mrow>
<msub>
<mover>
<mi>P</mi>
<mo>^</mo>
</mover>
<mn>2</mn>
</msub>
<mrow>
<mo>(</mo>
<mn>4</mn>
<mo>,</mo>
<mn>4</mn>
<mo>)</mo>
</mrow>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
In formula,WithFor the state estimation error covariance of two targets；
Re-recognizing of forming into columns of k moment completed based on the cycle threshold model in segmentation of forming into columns, if obtaining one after identification newly
Form into columnsIfThe merging then formed into columns finishes；Otherwise the k+1 moment is utilized
Association formation measurement repeat the above steps, proceed form into columns merging differentiate.
2. according to the method described in claim 1, it is characterised in that the method for building up of the overall maneuver tracking model is：
First ask for the acceleration of overall formation, the extrapolation for interior target of forming into columns, followed by foundation, clutter deleting madel and the point of ripple door
The acquisition that the foundation and association of mark pooled model are measured, is finally that dbjective state is updated.
3. method according to claim 2, it is characterised in that the clutter deleting madel and the foundation for putting mark pooled model
And the specific method of the acquisition of association measurement is：
According to NiIt is divided into following three kinds of situations and rejects clutter, it is determined thatAssociation measureWherein
For the one-step prediction value of i-th of targetpath in formation U (k)；NiForThe number of probes of middle measurement source,For
Fall intoAssociate the measurement set of ripple door；
If 1. Ni>=2, it is necessary first to set up point mark pooled model, static interconnection is carried out using the broad sense S-D principles distributed, according to
Static association result is measured to each sensor and is combined, and eliminates the redundancy of the same target of multiple sensor correspondences, and right
Each combination carries out Effective judgement, then will be received all measuring points in combination and carry out the compression of point mark to obtain an equivalent
Measuring point, realizes that multisensor point mark is interconnected, rejects simultaneouslyIn other marks, reach eliminate clutter purpose, finally select
Equivalent measuring point is taken to be measured for association
If 2. Ni=1, it is not necessary to set up point mark pooled model, chooseIn withThe measurement of space length recentlyMeasured for associationWhereinForMiddle measurement number；
If 3. Ni=0, it is based onVirtual measurement is obtained to measure for associationAnd
<mrow>
<msubsup>
<mi>z</mi>
<mi>i</mi>
<mo>*</mo>
</msubsup>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
<mi>H</mi>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<msub>
<mover>
<mi>X</mi>
<mo>^</mo>
</mover>
<mi>i</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>|</mo>
<mi>k</mi>
<mo>-</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
</mrow>
In formula, H (k) is measurement matrix.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710385783.3A CN107092808B (en) | 2014-09-18 | 2014-09-18 | Tracking method for formation target in combination maneuvering mode |
Applications Claiming Priority (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710385783.3A CN107092808B (en) | 2014-09-18 | 2014-09-18 | Tracking method for formation target in combination maneuvering mode |
| CN201410478495.9A CN104182652B (en) | 2014-09-18 | 2014-09-18 | Typical motor formation target tracking modeling method |
Related Parent Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201410478495.9A Division CN104182652B (en) | 2014-09-18 | 2014-09-18 | Typical motor formation target tracking modeling method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN107092808A true CN107092808A (en) | 2017-08-25 |
| CN107092808B CN107092808B (en) | 2020-09-08 |
Family
ID=51963687
Family Applications (3)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710386479.0A Active CN107169299B (en) | 2014-09-18 | 2014-09-18 | Tracking method for formation target in decentralized maneuvering mode |
| CN201410478495.9A Active CN104182652B (en) | 2014-09-18 | 2014-09-18 | Typical motor formation target tracking modeling method |
| CN201710385783.3A Active CN107092808B (en) | 2014-09-18 | 2014-09-18 | Tracking method for formation target in combination maneuvering mode |
Family Applications Before (2)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710386479.0A Active CN107169299B (en) | 2014-09-18 | 2014-09-18 | Tracking method for formation target in decentralized maneuvering mode |
| CN201410478495.9A Active CN104182652B (en) | 2014-09-18 | 2014-09-18 | Typical motor formation target tracking modeling method |
Country Status (1)
| Country | Link |
|---|---|
| CN (3) | CN107169299B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112068121A (en) * | 2020-09-09 | 2020-12-11 | 中国航空工业集团公司雷华电子技术研究所 | Formation target tracking method based on random finite set |
Families Citing this family (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110134139B (en) * | 2019-05-08 | 2022-04-08 | 合肥工业大学 | Tactical decision method and device for unmanned aerial vehicle formation in confrontation environment |
| CN110517294A (en) * | 2019-08-29 | 2019-11-29 | 中国科学院长春光学精密机械与物理研究所 | A kind of Multiple objects numbering method, Multiple objects numbering device and Multiple objects numbering equipment |
| CN112711020A (en) * | 2020-12-08 | 2021-04-27 | 中国人民解放军海军航空大学 | Group target tracking method based on cycle threshold distance segmentation |
| CN115420293A (en) * | 2022-08-31 | 2022-12-02 | 中国人民解放军海军航空大学 | Automatic target tracking method and device |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2007144570A1 (en) * | 2006-06-13 | 2007-12-21 | Bae Systems Plc | Improvements relating to target tracking |
| CN102148921A (en) * | 2011-05-04 | 2011-08-10 | 中国科学院自动化研究所 | Multi-target tracking method based on dynamic group division |
| CN102621542A (en) * | 2012-04-02 | 2012-08-01 | 中国人民解放军海军航空工程学院 | Track method before locomotive weak target detection based on multimode grain filtering and data association |
| CN103471591A (en) * | 2013-04-15 | 2013-12-25 | 中国人民解放军海军航空工程学院 | Logical method, global nearest neighbor and target course information based maneuvering multi-target data interconnection algorithm |
| CN103648108A (en) * | 2013-11-29 | 2014-03-19 | 中国人民解放军海军航空工程学院 | Sensor network distributed consistency object state estimation method |
| CN104050368A (en) * | 2014-06-09 | 2014-09-17 | 中国人民解放军海军航空工程学院 | Error compensation-based group track fine association algorithm under system error |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7194111B1 (en) * | 2003-07-10 | 2007-03-20 | The United States Of America As Represented By The Secretary Of The Navy | Hyperspectral remote sensing systems and methods using covariance equalization |
| CN101290350B (en) * | 2008-05-21 | 2011-02-16 | 覃征 | Ground cluster object tracking method |
| CN101984560A (en) * | 2010-04-27 | 2011-03-09 | 中国人民解放军海军航空工程学院 | Centralized multi-source combined Viterbi data interconnection tracker |
| CN103853908B (en) * | 2012-12-04 | 2017-11-14 | 中国科学院沈阳自动化研究所 | A kind of maneuvering target tracking method of adaptive interaction formula multi-model |
-
2014
- 2014-09-18 CN CN201710386479.0A patent/CN107169299B/en active Active
- 2014-09-18 CN CN201410478495.9A patent/CN104182652B/en active Active
- 2014-09-18 CN CN201710385783.3A patent/CN107092808B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2007144570A1 (en) * | 2006-06-13 | 2007-12-21 | Bae Systems Plc | Improvements relating to target tracking |
| CN102148921A (en) * | 2011-05-04 | 2011-08-10 | 中国科学院自动化研究所 | Multi-target tracking method based on dynamic group division |
| CN102621542A (en) * | 2012-04-02 | 2012-08-01 | 中国人民解放军海军航空工程学院 | Track method before locomotive weak target detection based on multimode grain filtering and data association |
| CN103471591A (en) * | 2013-04-15 | 2013-12-25 | 中国人民解放军海军航空工程学院 | Logical method, global nearest neighbor and target course information based maneuvering multi-target data interconnection algorithm |
| CN103648108A (en) * | 2013-11-29 | 2014-03-19 | 中国人民解放军海军航空工程学院 | Sensor network distributed consistency object state estimation method |
| CN104050368A (en) * | 2014-06-09 | 2014-09-17 | 中国人民解放军海军航空工程学院 | Error compensation-based group track fine association algorithm under system error |
Non-Patent Citations (2)
| Title |
|---|
| 何友等: ""基于相对位置矢量的群目标灰色精细航迹起始算法"", 《航空学报》 * |
| 秦卫华等: ""基于交互多模型的高机动群目标跟踪技术"", 《现代导航》 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112068121A (en) * | 2020-09-09 | 2020-12-11 | 中国航空工业集团公司雷华电子技术研究所 | Formation target tracking method based on random finite set |
Also Published As
| Publication number | Publication date |
|---|---|
| CN107169299A (en) | 2017-09-15 |
| CN107169299B (en) | 2021-06-15 |
| CN104182652B (en) | 2017-06-23 |
| CN104182652A (en) | 2014-12-03 |
| CN107092808B (en) | 2020-09-08 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN104182652B (en) | Typical motor formation target tracking modeling method | |
| CN107829452B (en) | Deep foundation pit construction monitoring and early warning technology integrating multiple sensors and foundation SAR | |
| CN104730596B (en) | A kind of discrete fractures modeling method based on multiple dimensioned factor constraints | |
| CN104915970B (en) | A kind of multi-object tracking method based on Track association | |
| CN110824467B (en) | Multi-target tracking data association method and system | |
| CN109410586A (en) | A kind of Traffic State Detection Method based on multivariate data fusion | |
| CN107633303A (en) | Parking site detecting system is tested and trained using virtual data | |
| CN107462892A (en) | Mobile robot synchronous superposition method based on more sonacs | |
| CN110071913A (en) | A kind of time series method for detecting abnormality based on unsupervised learning | |
| CN105045950A (en) | Three-dimensional laser scan based bridge safety evaluation system | |
| CN102829967B (en) | A kind of time domain fault recognition method based on regression model index variation | |
| CN106169188A (en) | A kind of method for tracing object based on the search of Monte Carlo tree | |
| CN106123850A (en) | AUV prestowage multibeam sonar underwater topography mapping modification method | |
| CN112098993A (en) | Multi-target tracking data association method and system | |
| Ma et al. | Target tracking system for multi-sensor data fusion | |
| CN102455438A (en) | Method for predicting volume of carbonate rock fractured cave type reservoir | |
| CN103268496A (en) | Target identification method of SAR (synthetic aperture radar) images | |
| CN107122735A (en) | A kind of multi-object tracking method based on deep learning and condition random field | |
| CN104569923B (en) | Velocity restraint-based Hough transformation fast track starting method | |
| CN110210326A (en) | A kind of identification of online train and speed estimation method based on fiber-optic vibration signal | |
| CN110764142A (en) | Method for assisting seismic data interpretation | |
| CN107292039A (en) | A kind of UUV based on Wave Cluster patrols bank profile construction method | |
| CN103717833A (en) | Method and system of correlating a measured log to a predicted log | |
| CN114972153A (en) | Bridge vibration displacement visual measurement method and system based on deep learning | |
| CN106127798A (en) | Dense space-time contextual target tracking based on adaptive model |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |