CN106443662A - Target steady association method in velocity ambiguity in low repetition frequency system - Google Patents
Target steady association method in velocity ambiguity in low repetition frequency system Download PDFInfo
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Abstract
The invention discloses a target steady association method in velocity ambiguity in a low repetition frequency system. The target steady association method comprises the following steps of: S1, confirming a detection threshold; S2, acquiring a distance dimension target point matrix R (n mR) and a velocity dimension target point matrix V (n mV) from a target point of which the radar echo distance-velocity dimension plane of each frame is greater than the detection threshold, wherein n refers to a frame index, mR refers to a distance dimension index, mV refers to a velocity dimension index, a matrix value is 0 or 1; 0 refers to no target and 1 refers to a target; S3, in a distance dimension, removing noisy points with relatively large distance differences according to a first preset algorithm so as to obtain an updated distance dimension target point matrix R' (n mR); and S4, in a velocity dimension, removing noisy points in the velocity dimension according to a first preset algorithm so as to obtain an updated velocity dimension target point matrix V' (n mV), wherein the orders of the step S3 and the step S4 can be exchanged. The target steady association method is relatively small in calculation amount, relatively high in instantaneity and applicable to tracking association of a radar target under a velocity ambiguity condition.
Description
Technical field
The present invention relates to radar target tracking association field, and in particular to the mesh under a kind of low repetition system during velocity ambiguity
Mark sane correlating method.
Background technology
As battlefield surroundings become increasingly complex, the requirement to missile-borne radar signal processor processing speed becomes increasingly
High.This amount of calculation to target association method during radar target tracking proposes challenge.In missile-borne radar resource-constrained
In the case of, the high-speed flight target radar returns under low repetition system occur velocity ambiguity.In the case of velocity ambiguity, such as
What can realize the robust tracking association of target, and problem demanding prompt solution.
201410422500.4 Chinese patent literature of Application No. disclose a kind of based on improve particle filter target with
Track method, the method is by the improvement to particle filter method, it is achieved that the detection of radar weak target and tracking.Particle filter
Method needs the posterior probability density of the good approximation system of substantial amounts of sample size ability, and operand is larger, is not suitable for reality
The missile-borne radar that when property has high demands.
201410707302.2 Chinese patent literature of Application No. discloses one kind based on connected component and template matching
Radar plot correlating method, method of the method by extracting connected component and template matching, it is achieved that Targets Dots are closed
Connection, but the method has certain limitation not using the velocity characteristic of target for target velocity tracking.
201610339346.3 Chinese patent literature of Application No. discloses a kind of direction cosines coordinate system lower band Doppler
The radar target tracking method of measurement, the method realizes radar target by constructing pseudo- measurement and extracting Descartes's status information
Tracking, but the method does not consider velocity ambiguity situation, and in the case of velocity ambiguity, the method is not applied to.
It is published in the 12nd phase in 2012《Electronic information journal》Non-patent literature on periodical《Sky based on multiple target tracking
Intercentrum target micro-doppler frequency extraction method》A kind of tracking correlating method of target velocity is described, by using many mesh
The method of mark tracking achieves micro-doppler frequency abstraction, but the method does not account for micro-doppler in the case of velocity ambiguity
Tracking related question, is not suitable for the tracking related question of radar target in the case of velocity ambiguity.
It is published in the 4th phase in 2012《Radar journal》Non-patent literature on periodical《Based on the different of o- topological diagram recently
Class sensor target correlating method》A kind of target association method based on o- topological diagram recently is described, by arest neighbors method
With topological diagram, it is achieved that target association.The method has certain limitation not using velocity information to target velocity tracking.
Content of the invention
It is an object of the invention to provide a kind of sane correlating method of target under low repetition system during velocity ambiguity, calculates
Amount is less, and real-time is higher, it is adaptable to the tracking association of radar target in the case of velocity ambiguity.
In order to achieve the above object, the present invention is achieved through the following technical solutions：Velocity ambiguity under a kind of low repetition system
When the sane correlating method of target, be characterized in, comprise the steps of：
S1, determine detection threshold value；
S2, take every frame radar return distance-speed dimensional plane more than detection threshold value impact point, obtain distance dimension impact point
Matrix R (n mR) and speed dimension impact point matrix V (n mV), wherein, n represents frame index, mRRepresent distance dimension index, mVRepresent
Speed dimension index, matrix value indicates no target for 0 or 1,0, and 1 indicates target；
S3, in distance dimension, removed apart from the larger miscellaneous noise of upper difference according to the first preset algorithm, after being updated away from
From dimension target dot matrix R ' (n mR)；
S4, tie up in speed, the miscellaneous noise in speed dimension is removed according to the second preset algorithm, speed after being updated ties up mesh
Punctuate matrix V ' (n mV)；
Described step S3 is interchangeable with the order of step S4.
Include in described step S2：
S2.1, n-th frame radar return is set as Xn(mRmV)；
S2.2, initialization distance dimension target dot matrix R (n mR)=0, n=1,2,3......N, mR=1,2,
3......MR, speed dimension impact point matrix V (n mV)=0, mV=1,2,3......MV；
S2.3, traversal Xn(mRmV), n=1,2,3......N, work as Xn(mRmVDuring) >=ζ, by corresponding mRThe distance of position
Dimension matrix R (n mR) put 1, corresponding mVSpeed dimension matrix V (the n m of positionV) put 1.
Include in described step S3：
S3.1, setpoint distance allowable error are Δ R；
S3.2, taking-up R (1 mR) intermediate value be 1 corresponding distance value, make average treatment, obtain distance dimension meansigma methodss, will
The corresponding m of the meansigma methodssR1 is disposed, remaining sets to 0；
S3.3, setting n=2；
S3.4, renewal frame index n, vectorial R (the n m of comparisonR) intermediate value beR) intermediate value be 1 pair
The distance value that answers, range difference is more than R (the n m of Δ RR) set to 0；
S3.5, taking-up R (n mR) intermediate value be 1 corresponding distance value, make average treatment, obtain distance dimension meansigma methodss, will
The corresponding m of the meansigma methodssR1 is disposed, remaining sets to 0；
S3.6, renewal n=n+1, if n≤N, go to step S3.4, if n is ＞ N, go to step S3.7；
After the completion of S3.7, traversal, renewal distance dimension matrix R ' (the n m that distance dimension goes remove impurity noise is obtainedR).
In described step S3, the first preset algorithm is arest neighbors method.
Include in described step S4：
S4.1, initialization n=1, setting speed allowable error is Δ v；
S4.2, taking-up V (1 mV) intermediate value be 1 corresponding velocity amplitude, make average treatment, obtain speed dimension meansigma methodss v1, will
The corresponding m of the meansigma methodssV1 is disposed, remaining sets to 0；
S4.3, determination obscure the up-and-down boundary [K of number of times K, LminKmax]、[LminLmax]；
S4.4, setting n=2；
S4.5, renewal frame index n, take out V (n mV) intermediate value be 1 corresponding velocity vector
Previous frame speed ties up meansigma methodss vn-1, ambiguity period VnAnd Vn-1；
S4.6, initialization K=Kmin, initialize i=1；
S4.7, renewal K, i, are calculated the value of L by formula (1)：
S4.8, judge boundary condition；
IfAndThen make
IfAndThen make
IfAndThen make
IfOrThen invalid；
If S4.9 border is effective, judgeWithBetween whether there is integer, if there is integer, go to step
S4.10；If there is no integer, K=K+1 is made, if K≤Kmax, step S4.7 is gone to, otherwise willCorresponding V (n mV) set to 0,
And go to step S4.10；
S4.10, taking-up V (n mV) intermediate value be 1 corresponding velocity amplitude, make average treatment, obtain speed dimension meansigma methodss vn,
By corresponding for meansigma methodss mV1 is disposed, remaining sets to 0；
S4.11, make i=i+1, K=KminIf, i≤I, step S4.7 is gone to, otherwise goes to step S4.12；
S4.12, n=n+1 is made, if n≤N, step S4.5 is gone to, otherwise go to S4.13；
Speed dimension impact point matrix V ' (n m after S4.13, renewalV).
In described step S4, the second preset algorithm is linear programming method.
The sane correlating method of target under a kind of low repetition system of the present invention during velocity ambiguity has compared with prior art
Advantages below：Amount of calculation of the present invention is less, and real-time is higher, is suitably applied the missile-borne radar that adaptability to changes has high demands；At a high speed
Airbound target radar return occurs velocity ambiguity, the association of frame target before and after impact, the method that the present invention utilizes linear programming,
Achieve radar target robust tracking association in the case of velocity ambiguity
Description of the drawings
Fig. 1 is the flow chart of the sane correlating method of target during velocity ambiguity under a kind of low repetition system of the present invention；
Fig. 2 is linear programming method schematic diagram.
Specific embodiment
Below in conjunction with accompanying drawing, by describing a preferably specific embodiment in detail, the present invention is further elaborated.
As shown in figure 1, a kind of sane correlating method of target under low repetition system during velocity ambiguity, comprises the steps of：
S1, determine detection threshold value.
S2, take every frame radar return distance-speed dimensional plane more than detection threshold value impact point, obtain distance dimension impact point
Matrix R (n mR) and speed dimension impact point matrix V (n mV), wherein, n represents frame index, mRRepresent distance dimension index, mVRepresent
Speed dimension index, matrix value indicates no target for 0 or 1,0, and 1 indicates target.
S2.1, n-th frame radar return is set as Xn(mRmV)；
S2.2, initialization distance dimension target dot matrix R (n mR)=0, n=1,2,3......N, mR=1,2,
3......MR, speed dimension impact point matrix V (n mV)=0, mV=1,2,3......MV；
S2.3, traversal Xn(mRmV), n=1,2,3......N, work as Xn(mRmVDuring) >=ζ, by corresponding mRThe distance of position
Dimension matrix R (n mR) put 1, corresponding mVSpeed dimension matrix V (the n m of positionV) put 1.
S3, in distance dimension, removed apart from the larger miscellaneous noise of upper difference according to arest neighbors method, after being updated away from
From dimension target dot matrix R ' (n mR).
S3.1, setpoint distance allowable error are Δ R；
S3.2, taking-up R (1mR) intermediate value be 1 corresponding distance value, make average treatment, obtain distance dimension meansigma methodss, should
The corresponding m of meansigma methodssR1 is disposed, remaining sets to 0；
S3.3, setting n=2；
S3.4, renewal frame index n, vectorial R (the n m of comparisonR) intermediate value beR) intermediate value be 1 correspond to
Distance value, by range difference more than Δ R R (n mR) set to 0；
S3.5, taking-up R (n mR) intermediate value be 1 corresponding distance value, make average treatment, obtain distance dimension meansigma methodss, will
The corresponding m of the meansigma methodssR1 is disposed, remaining sets to 0；
S3.6, renewal n=n+1, if n≤N, go to step S3.4, if n is ＞ N, go to step S3.7；
After the completion of S3.7, traversal, renewal distance dimension matrix R ' (the n m that distance dimension goes remove impurity noise is obtainedR).
S4, tie up in speed, the miscellaneous noise in speed dimension is removed according to linear programming method, speed after being updated ties up mesh
Punctuate matrix V ' (n mV), as shown in Fig. 2 by the restriction to obscuring number of times and adjacent two frame speeds error, using linear gauge
The method that draws, in the case of fuzzy number of times is not solved, the association of frame speed before and after realization, in figure shadow region represents that integer is searched
Rope region, if there is integer in shadow region, before and after expression, frame speed meets qualificationss, realizes before and after's frame speed with this
Association.
S4.1, initialization n=1, setting speed allowable error is Δ v；
S4.2, taking-up V (1 mV) intermediate value be 1 corresponding velocity amplitude, make average treatment, obtain speed dimension meansigma methodss v1, will
The corresponding m of the meansigma methodssV1 is disposed, remaining sets to 0；
S4.3, determination obscure the up-and-down boundary [K of number of times K, LminKmax]、[LminLmax]；
S4.4, setting n=2；
S4.5, renewal frame index n, take out V (n mV) intermediate value be 1 corresponding velocity vector
Previous frame speed ties up meansigma methodss vn-1, ambiguity period VnAnd Vn-1；
S4.6, initialization K=Kmin, initialize i=1；
S4.7, renewal K, i, are calculated the value of L by formula (1)：
S4.8, judge boundary condition；
IfAndThen make
IfAndThen make
IfAndThen make
IfOrThen invalid；
If S4.9 border is effective, judgeWithBetween whether there is integer, if there is integer, go to step
S4.10；If there is no integer, K=K+1 is made, if K≤Kmax, step S4.7 is gone to, otherwise willCorresponding V (n mV) set to 0,
And go to step S4.10；
S4.10, taking-up V (n mV) intermediate value be 1 corresponding velocity amplitude, make average treatment, obtain speed dimension meansigma methodss vn,
By corresponding for meansigma methodss mV1 is disposed, remaining sets to 0；
S4.11, make i=i+1, K=KminIf, i≤I, step S4.7 is gone to, otherwise goes to step S4.12；
S4.12, n=n+1 is made, if n≤N, step S4.5 is gone to, otherwise go to S4.13；
Speed dimension impact point matrix V ' (n m after S4.13, renewalV).
The sane correlating method of target as claimed in claim 1, it is characterised in that the second pre- imputation in described step S4
Method is linear programming method.
Although present disclosure has been made to be discussed in detail by above preferred embodiment, but it should be appreciated that above-mentioned
Description is not considered as limitation of the present invention.After those skilled in the art have read the above, for the present invention's
Multiple modifications and substitutions all will be apparent.Therefore, protection scope of the present invention should be limited to the appended claims.
Claims (6)
1. the sane correlating method of target under a kind of low repetition system during velocity ambiguity, it is characterised in that comprise the steps of：
S1, determine detection threshold value；
S2, take every frame radar return distance-speed dimensional plane more than detection threshold value impact point, obtain distance dimension target dot matrix
R(n mR) and speed dimension impact point matrix V (n mV), wherein, n represents frame index, mRRepresent distance dimension index, mVRepresent speed
Dimension index, matrix value indicates no target for 0 or 1,0, and 1 indicates target；
S3, in distance dimension, removed apart from the larger miscellaneous noise of upper difference according to the first preset algorithm, the distance after being updated is tieed up
Target dot matrix R ' (n mR)；
S4, tie up in speed, the miscellaneous noise in speed dimension is removed according to the second preset algorithm, speed after being updated ties up impact point
Matrix V ' (n mV)；
Described step S3 is interchangeable with the order of step S4.
2. the sane correlating method of target as claimed in claim 1, it is characterised in that include in described step S2：
S2.1, n-th frame radar return is set as Xn(mRmV)；
S2.2, initialization distance dimension target dot matrix R (n mR)=0, n=1,2,3......N, mR=1,2,3......MR, speed
Degree dimension impact point matrix V (n mV)=0, mV=1,2,3......MV；
S2.3, traversal Xn(mRmV), n=1,2,3......N, work as Xn(mRmVDuring) >=ζ, by corresponding mRThe distance dimension matrix of position
R(n mR) put 1, corresponding mVSpeed dimension matrix V (the n m of positionV) put 1.
3. the sane correlating method of target as claimed in claim 1, it is characterised in that include in described step S3：
S3.1, setpoint distance allowable error are Δ R；
S3.2, taking-up R (1 mR) intermediate value be 1 corresponding distance value, make average treatment, obtain distance dimension meansigma methodss, this is average
It is worth corresponding mR1 is disposed, remaining sets to 0；
S3.3, setting n=2；
S3.4, renewal frame index n, vectorial R (the n m of comparisonR) intermediate value beR) intermediate value is 1 corresponding
Distance value, range difference is more than R (the n m of Δ RR) set to 0；
S3.5, taking-up R (n mR) intermediate value be 1 corresponding distance value, make average treatment, obtain distance dimension meansigma methodss, this is average
It is worth corresponding mR1 is disposed, remaining sets to 0；
S3.6, renewal n=n+1, if n≤N, go to step S3.4, if n is ＞ N, go to step S3.7；
After the completion of S3.7, traversal, renewal distance dimension matrix R ' (the n m that distance dimension goes remove impurity noise is obtainedR).
4. the sane correlating method of target as claimed in claim 1, it is characterised in that the first preset algorithm in described step S3
For arest neighbors method.
5. the sane correlating method of target as claimed in claim 1, it is characterised in that include in described step S4：
S4.1, initialization n=1, setting speed allowable error is Δ v；
S4.2, taking-up V (1 mV) intermediate value be 1 corresponding velocity amplitude, make average treatment, obtain speed dimension meansigma methodss v1, this is put down
The corresponding m of averageV1 is disposed, remaining sets to 0；
S4.3, determination obscure the up-and-down boundary [K of number of times K, LminKmax]、[LminLmax]；
S4.4, setting n=2；
S4.5, renewal frame index n, take out V (n mV) intermediate value be 1 corresponding velocity vectorUpper one
Frame speed ties up meansigma methodss vn-1, ambiguity period VnAnd Vn-1；
S4.6, initialization K=Kmin, initialize i=1；
S4.7, renewal K, i, are calculated the value of L by formula (1)：
S4.8, judge boundary condition；
IfAndThen make
IfAndThen make
IfAndThen make
IfOrThen invalid；
If S4.9 border is effective, judgeWithBetween whether there is integer, if there is integer, go to step S4.10；If
There is no integer, K=K+1 is made, if K≤Kmax, step S4.7 is gone to, otherwise willCorresponding V (n mV) set to 0, and go to step
Rapid S4.10；
S4.10, taking-up V (n mV) intermediate value be 1 corresponding velocity amplitude, make average treatment, obtain speed dimension meansigma methodss vn, should
The corresponding m of meansigma methodssV1 is disposed, remaining sets to 0；
S4.11, make i=i+1, K=KminIf, i≤I, step S4.7 is gone to, otherwise goes to step S4.12；
S4.12, n=n+1 is made, if n≤N, step S4.5 is gone to, otherwise go to S4.13；
Speed dimension impact point matrix V ' (n m after S4.13, renewalV).
6. the sane correlating method of target as claimed in claim 1, it is characterised in that the second preset algorithm in described step S4
For linear programming method.
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109444838A (en) * | 2018-09-12 | 2019-03-08 | 上海无线电设备研究所 | One kind being based on the dual frequency solution velocity ambiguity method and system of pulse accumulation frame |
| CN109521420A (en) * | 2018-12-20 | 2019-03-26 | 西安电子科技大学 | Based on the matched multi-object tracking method of multiple features |
| CN111289953A (en) * | 2020-01-14 | 2020-06-16 | 北京理工大学 | Space-based radar distance/speed ambiguity resolution method based on fuzzy matrix updating |
| WO2021035395A1 (en) * | 2019-08-23 | 2021-03-04 | 深圳市大疆创新科技有限公司 | Speed determining method and device, and storage medium |
| CN113269682A (en) * | 2021-04-21 | 2021-08-17 | 青岛海纳云科技控股有限公司 | Non-uniform motion blur video restoration method combined with interframe information |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6064331A (en) * | 1998-06-11 | 2000-05-16 | Boeing North American, Inc. | Pulse repetition frequency section method and system |
| CN104637070A (en) * | 2014-12-15 | 2015-05-20 | 江南大学 | Probability hypothesis density based variable target number video tracking algorithm |
| CN105093199A (en) * | 2015-07-30 | 2015-11-25 | 中国人民解放军信息工程大学 | Target identification feature extraction method based on radar time domain echoes |
| CN105158748A (en) * | 2015-07-29 | 2015-12-16 | 中国人民解放军海军航空工程学院 | High-speed target multichannel compensation focusing and TBD mixed accumulation detection method |
-
2016
- 2016-10-28 CN CN201610969889.3A patent/CN106443662B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6064331A (en) * | 1998-06-11 | 2000-05-16 | Boeing North American, Inc. | Pulse repetition frequency section method and system |
| CN104637070A (en) * | 2014-12-15 | 2015-05-20 | 江南大学 | Probability hypothesis density based variable target number video tracking algorithm |
| CN105158748A (en) * | 2015-07-29 | 2015-12-16 | 中国人民解放军海军航空工程学院 | High-speed target multichannel compensation focusing and TBD mixed accumulation detection method |
| CN105093199A (en) * | 2015-07-30 | 2015-11-25 | 中国人民解放军信息工程大学 | Target identification feature extraction method based on radar time domain echoes |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109444838A (en) * | 2018-09-12 | 2019-03-08 | 上海无线电设备研究所 | One kind being based on the dual frequency solution velocity ambiguity method and system of pulse accumulation frame |
| CN109521420A (en) * | 2018-12-20 | 2019-03-26 | 西安电子科技大学 | Based on the matched multi-object tracking method of multiple features |
| CN109521420B (en) * | 2018-12-20 | 2022-10-04 | 西安电子科技大学 | Multi-target tracking method based on multi-feature matching |
| WO2021035395A1 (en) * | 2019-08-23 | 2021-03-04 | 深圳市大疆创新科技有限公司 | Speed determining method and device, and storage medium |
| CN111289953A (en) * | 2020-01-14 | 2020-06-16 | 北京理工大学 | Space-based radar distance/speed ambiguity resolution method based on fuzzy matrix updating |
| CN113269682A (en) * | 2021-04-21 | 2021-08-17 | 青岛海纳云科技控股有限公司 | Non-uniform motion blur video restoration method combined with interframe information |
| CN113269682B (en) * | 2021-04-21 | 2023-03-24 | 海纳云物联科技有限公司 | Non-uniform motion blur video restoration method combined with interframe information |
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