CN105891814A  Rangeonly radar networking singletarget clustering positioning method  Google Patents
Rangeonly radar networking singletarget clustering positioning method Download PDFInfo
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 CN105891814A CN105891814A CN201610031441.7A CN201610031441A CN105891814A CN 105891814 A CN105891814 A CN 105891814A CN 201610031441 A CN201610031441 A CN 201610031441A CN 105891814 A CN105891814 A CN 105891814A
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 step
 amp
 target
 radar
 outlier
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 G—PHYSICS
 G01—MEASURING; TESTING
 G01S—RADIO DIRECTIONFINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCEDETECTING 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
 G01S13/02—Systems using reflection of radio waves, e.g. primary radar systems; Analogous systems
 G01S13/06—Systems determining position data of a target

 G—PHYSICS
 G01—MEASURING; TESTING
 G01S—RADIO DIRECTIONFINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCEDETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
 G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
 G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
 G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target crosssection
Abstract
Description
Technical field
The invention belongs to radar network composite field of locating technology, it is adaptable to landbased radar networking, fleet radar network composite etc. are to sky The occasion of middle single goal location；In the occasion that positioning accuracy request is relatively low, this method can be directly applied to obtain target location；? The occasion that positioning accuracy request is higher, can particularly relate to a kind of pure distance radar group using the result of this method as estimating initial value Net single goal cluster localization method.
Background technology
During single goal is positioned by radar network composite, due to various objective factors or the impact of subjective operation, each thunder Reach in the target measurement of acquisition and be usually present outlier.Although the negligible amounts of outlier, but seriously reduce the positioning precision of target. Traditional outlier recognition methods such as " 3 σ " criterion, " Ge Luobusi " criterion etc., mainly to measure take afterwards batch at Reason form.Specifically, after obtaining all of measurement, suppose that it obeys a certain distribution, then according to abovementioned criterion structure Statistic of test and then identification outlier.But practical experience shows, the method is appropriate only under quiescent conditions entering a certain fixed amount Row outlier identification, the most largely effective for current intelligences such as air mobile targets；The method takes batch processing afterwards simultaneously Mode, is unfavorable for the realtime positioning of target.If location algorithm can identify outlier in real time, accurately, it is possible not only to mesh Mark quickly positions and follows the tracks of, and can be effectively improved the positioning precision of target simultaneously.
Summary of the invention
The purpose of the present invention is to propose to a kind of pure distance radar network composite single goal and cluster localization method, solve in conventional location algorithm Outlier discrimination is low in the dynamic case, the problem of poor real.
For realizing the purpose of the present invention, the pure distance radar network composite single goal cluster localization method that the present invention proposes includes following step Rapid:
Step 1: by a certain moment N (N ＞ 3) portion radar receiver R_{i}=(x_{i},y_{i})^{T}(i=1,2 ..., N) record target range letter Breath d_{i}Send into Radar Signal Processing computer；
Step 2: initialize
n_{p}For number of intersections, initialize n_{p}=1；
n_{max}For number of intersections maximum,
χ_{0}For cluster detection threshold, take χ_{0}∈(0,1)；
n_{g}For the number of intersections that dense degree is high, initialize n_{g}=0；
Step 3: calculate intersecting point coordinate
(1) three radar R are arbitrarily selected_{m}=(x_{m},y_{m})^{T}、R_{n}=(x_{n},y_{n})^{T}、R_{k}=(x_{k},y_{k})^{T}；
(2) intersecting point coordinate about target is recordedFor:
Wherein, L_{1}=x_{m}y_{k}x_{n}y_{m}+x_{n}y_{k}+y_{m}x_{k}+y_{n}x_{m}y_{n}x_{k}；
(3)n_{p}=n_{p}+1；
(4) during circulation performs this step (1)～(3), until n_{p}=n_{max}+ 1 terminates；
(5)n_{p}=1；
Step 4: calculate intersection point distance and
(1) intersection pointTo the distance of other all intersection points be:
(2)n_{p}=n_{p}+1；
(3) during circulation performs this step (1)～(2), until n_{p}=n_{max}+ 1 terminates；
(4)n_{p}=1；
Step 5: outlier detection based on clustering method
(1) minimum range and D are initialized_{min}=D_{np}；
(2) D is compared_{min}、Size, and to D_{min}Assignment:
(3)n_{p}=n_{p}+1；
(4) during circulation performs this step (2)～(3), until n_{p}=n_{max}+ 1 terminates；
(5)n_{p}=1；
(6) intersection pointThe intersection point dense degree following formula of present position calculates
(7) if γ_{np}＜ χ_{0}, willIt is considered as outlier, and noteOn the contrary, n_{g}=n_{g}+1；
(8)n_{p}=n_{p}+1；
(9) during circulation performs this step (6)～(8), until n_{p}=n_{max}+ 1 terminates；
Step 6: estimate target location
Target state estimator positionCalculate with following formula
Thus complete the location to target.
Comparing with background technology, beneficial effects of the present invention illustrates: the change of (1) outlier recognition methods；Traditional outlier is known Other method is directly to be identified measurement, the intersection point that first measurement conversion is target by the method that the present invention uses data to change, Outlier identification is carried out according to the dense degree between intersection point；(2) raising of outlier recognition efficiency；Traditional outlier recognition methods Taking the form of batch processing, the method that the present invention proposes can carry out outlier identification after each radar obtains the single measurement of target, Realtime is improved；(3) extension of outlier identification range；For the outlier of traditional method Noneidentified, the present invention is permissible How many selfadaptative adjustment according to number of sensors cluster detection threshold, and then science determines the scope of outlier；(4) target is fixed The improvement of position precision；Traditional outlier recognition methods causes the positioning precision of algorithm not owing to being not suitable for the factors such as dynamic case Ideal, and the cluster localization method that the present invention proposes can be effectively improved the positioning precision of target.
Accompanying drawing explanation
Accompanying drawing 1 is the flow chart of steps of the pure distance radar network composite single goal cluster localization method of the present invention；
Accompanying drawing 2 is that the inventive method is to the positioning result figure after target single measurement；
Accompanying drawing 3 is the inventive method and traditional positioning result comparison diagram based on 3 σ criterions after outlier identification；
Accompanying drawing 4 is the inventive method and traditional positioning precision comparison diagram based on 3 σ criterions after outlier identification.
Detailed description of the invention
Below in conjunction with the accompanying drawings the pure distance radar network composite single goal cluster localization method of the present invention is described in detail.
Implementation condition: assume that quantity N=5 of radar network, each radar site are respectively as follows: R_{1}=(20,30)^{T}、R_{2}=(0,0)^{T}、 R_{3}=(15,10)^{T}、R_{4}=(30,0)^{T}、R_{5}=(60,15)^{T}(unit: km)；Each radar ranging accuracy is identical, and sets its value For σ_{d}=0.1km；Target current location is set to T=(40,60)^{T}km；Take cluster detection threshold χ_{0}=0.6.
According to abovementioned condition, the concrete steps of the present invention are as shown in Figure 1.Specific as follows:
Step 1: by a certain moment N (N ＞ 3) portion radar receiver R_{i}=(x_{i},y_{i})^{T}(i=1,2 ..., N) record target range letter Breath d_{i}Send into Radar Signal Processing computer；
Step 2: initialize
n_{p}For number of intersections, initialize n_{p}=1；
n_{max}For number of intersections maximum,
χ_{0}For cluster detection threshold, take χ_{0}∈(0,1)；
n_{g}For the number of intersections that dense degree is high, initialize n_{g}=0；
Step 3: calculate intersecting point coordinate
(1) three radar R are arbitrarily selected_{m}=(x_{m},y_{m})^{T}、R_{n}=(x_{n},y_{n})^{T}、R_{k}=(x_{k},y_{k})^{T}；
(2) intersecting point coordinate about target is recorded
The distance measuring of abovementioned three radars is respectively d_{m}、d_{n}And d_{k}, available equation below group:
Above formula is organized into following matrix form:
Wherein
Solve:
Wherein, L_{1}=x_{m}y_{k}x_{n}y_{m}+x_{n}y_{k}+y_{m}x_{k}+y_{n}x_{m}y_{n}x_{k}；
(3)n_{p}=n_{p}+1；
(4) during circulation performs this step (1)～(3), until n_{p}=n_{max}+ 1 terminates；
(5)n_{p}=1；
Step 4: calculate intersection point distance and
(1) intersection pointTo the distance of other all intersection points be:
(2)n_{p}=n_{p}+1；
(3) during circulation performs this step (1)～(2), until n_{p}=n_{max}+ 1 terminates；
(4)n_{p}=1；
Step 5: outlier detection based on clustering method
(1) initialize minimum range and
(2) D is compared_{min}、Size, and to D_{min}Assignment:
(3)n_{p}=n_{p}+1；
(4) during circulation performs this step (2)～(3), until n_{p}=n_{max}+ 1 terminates, and the most just obtains D_{min}；
(5)n_{p}=1；
(6) intersection pointThe intersection point dense degree following formula of present position calculates
From this formula,It is the least,The biggest；The i.e. distance of intersection point and the least, the intersection point of this intersection point present position is intensive Degree is the biggest；
(7) ifWillIt is considered as outlier, and noteOn the contrary, n_{g}=n_{g}+1；
χ_{0}To choose same N closely related, total principle is: N is the biggest, number of intersections n obtained_{p}The biggest, therefore χ_{0}Should The dense degree of the larger ability valid metric intersection point chosen；
(8)n_{p}=n_{p}+1；
(9) during circulation performs this step (6)～(8), until n_{p}=n_{max}+ 1 terminates；
Now the dense degree of all intersection points is all measured, and obtained n_{g}The intersection point set that individual dense degree is higher；
Step 6: estimate target location
Target state estimator positionCalculate with following formula
I.e. take n_{g}The higher intersecting point coordinate average of individual dense degree, as the estimation position of target, thus completes the location to target.
Accompanying drawing 2 is that each radar is to the cluster positioning result after target single measurement.5 radars to target when common property in prelocalization RawIndividual intersection point, the inventive method identifies 1 intersection point and processes as outlier.In terms of direct result, the outlier identified Point is significantly different with the trend that other intersection points are presented.
Accompanying drawing 3 is the inventive method and traditional positioning result comparison diagram based on 3 σ criterions after outlier identification.Two kinds of methods are equal Carrying out 200 Monte Carlo emulation, wherein figure (a) is the result figure of the present invention, and figure (b) is knots based on 3 σ criterions Fruit figure.As seen from the figure, the intersection point of figure (a) substantially concentrates near target location.
Accompanying drawing 4 is rootmeansquare error (RMS) comparison diagram of accompanying drawing 3.Under current simulated conditions, the positioning precision of the present invention Relatively 3 σ criterions improve about 600m.Therefore, compared with traditional outlier recognition methods, the method for the present invention is more realtime, accurate, And then it is effectively improved the positioning precision of target.
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Citations (2)
Publication number  Priority date  Publication date  Assignee  Title 

CN103605110A (en) *  20131203  20140226  北京理工大学  Indoor passive target positioning method based on received signal strength 
CN104898104A (en) *  20150610  20150909  中国西安卫星测控中心  Target combined positioning method based on Euler's distance means clustering 

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Patent Citations (2)
Publication number  Priority date  Publication date  Assignee  Title 

CN103605110A (en) *  20131203  20140226  北京理工大学  Indoor passive target positioning method based on received signal strength 
CN104898104A (en) *  20150610  20150909  中国西安卫星测控中心  Target combined positioning method based on Euler's distance means clustering 
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