CN105259540B - A kind of optimization method of multistation radar anti-active cheating formula interference - Google Patents
A kind of optimization method of multistation radar anti-active cheating formula interference Download PDFInfo
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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
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Abstract
The invention discloses a kind of optimization method of multistation radar anti-active cheating formula interference, thinking is：Set up netted radar system, including N number of node radar, optional node radar docking is collected mail after number progress matched filtering and target detection, obtain K+M point target, using first node radar to refer to radar, and time unifying operation is carried out to 1 node radar of K+M point target and other N, obtain and then obtain each self-corresponding amplitude bit of K+M point target levying vector accordingly；Cluster numbers scope is set as [1, K+M], self-corresponding amplitude bit each to K+M point target is levied vector and clustered, obtain each self-corresponding cluster result of K+M cluster numbers, and phase patibhaga-nimitta is carried out to it from metrics evaluation, choose phase patibhaga-nimitta cluster numbers corresponding from index maximum and be used as final cluster result as preferable clustering number, and using the corresponding cluster result of the preferable clustering number；The decision gate limit value ε of the false point target of setting, and the false point target included in the final cluster result of the point target Amplitude Ratio characteristic vector is obtained accordingly.
Description
Technical field
The present invention relates to Anti-jamming Technology for Radar field, it is excellent that more particularly to a kind of multistation radar anti-active cheating formula is disturbed
Change method, it is adaptable to which radar network system data fusion center efficiently identifies and rejects false targets, realizes multistation thunder
Up to system counter Deceiving interference.
Background technology
Spoofing techniques are directed to cheating aggrieved radar in message contexts such as direction, position, tracking starting points, or
It is that many decoys are manufactured around real goal echo so that actual target information can not be extracted.It is a kind of effective
Spoofing techniques classification is deception formula spoofing techniques, and the deception purpose of the deception formula spoofing techniques is by modulation
Transmitting or forwarding the information such as amplitude, phase of echo is received to radar and is misled, especially digital radiofrequency memory
(DRFM), i.e., the appearance of advanced translation jammer causes Cheat Jamming Technique more ripe, is widely used in self-defence type
Disturb and go along with the team interference；In addition, Deceiving interference can take substantial amounts of system resource, the detection performance of radar system is had a strong impact on
And tracking performance.
For false targets interference, monostatic radar is single due to visual angle, it is difficult to it is resisted, and multistation radar
The method of utilisation point mark association carries out true and false differentiation to the target detected, and weeds out decoy, so as to realize deception formula
The confrontation of interference.But, because each node radar can be by Deceiving interference in multistation radar so that intensive decoy is led
The error rate that inspection is associated between the measuring value for causing each node radar is higher, and the cloth station location of multistation radar is undesirable,
Also the ability of multistation radar electronic warfare Deceiving interference can be influenceed.
Existing multistation radar is largely that Deceiving interference is resisted using pixel-based fusion, in multistation radar pair
During target measurement, the point mark information or flight path information of target only make use of so that pixel-based fusion anti-interference method is not
Its antijamming capability can be played completely, and then can not make full use of the advantage of multistation radar.
Existing confrontation Deceiving interference is signal level fusing method, although can make full use of the various information of echo,
But there is also many restrictions and deficiency, the signal level fusing method utilizes the multiple bag of the real goal echo in different radar stations
Network is separate and differentiates true and false target the characteristics of interference signal complex envelope is related, and its identification result is wrapped again dependent on the slow time
The number of pulse repetition period (PRT) in network sequence, and in actual radar operating environment, available PRT number right and wrong
Often it is limited, or even the only one of which pulse repetition period (PRT) can utilize so that now signal correlation detection method will
It is entirely ineffective.Simultaneously, it is more likely that the true target risen and fallen slowly can be differentiated to be decoy.A kind of multistation radar being previously proposed resists
Although the method for active deception overcomes deficiency above, but requires that each false target has identical interference
Noise power ratio (JNR).
The content of the invention
The deficiency existed for above-mentioned prior art, it is an object of the invention to propose a kind of multistation radar anti-active cheating
The optimization method of formula interference, this method can resist the intensive decoy of deception formula, and also different Deceiving interferences can be produced
Decoy is effectively differentiated.
To reach above-mentioned purpose, the present invention is achieved using following technical scheme.
A kind of optimization method of multistation radar anti-active cheating formula interference, comprises the following steps：
Step 1, set up and N number of node radar, N number of node are included in netted radar system, the netted radar system
Radar receives signal respectively, and N is natural number, and N >=2, and within a pulse-recurrence time, in N number of node radar
The docking of any one node radar collect mail and number carry out after matched filtering and target detection, obtain K+M point target；Wherein, K tables
Show true point target number present in reception signal, M represents to receive false point target number present in signal；
Step 2, using first node radar to refer to radar, and K+M point target and other N-1 node radar are entered
Row time unifying is operated, obtain the N-1 node detections of radar to point target and the point target that arrives of reference detections of radar it
Between echo amplitude corresponding relation, p-th of point target is then chosen from the echo of n-th of node radar in n-th of node thunder
The echo amplitude ε reachedp,n, and the Amplitude Ratio characteristic vector Ω for obtaining p-th of point target is calculated accordinglyp, and then obtain K+M point
Each self-corresponding amplitude bit of target levies vector；Wherein, p ∈ { 1,2 ..., K+M }, n ∈ { 2 ..., N }；
Step 3, cluster numbers scope is set as [1, K+M], and self-corresponding amplitude bit each to K+M point target is levied accordingly
Vector carries out cluster operation, obtains the amplitude bit of each self-corresponding point target of K+M cluster numbers in the range of the cluster numbers of setting
Levy the cluster result of vector；
Step 4, each to K+M cluster numbers in the range of the cluster numbers of setting self-corresponding point target Amplitude Ratio characteristic vector
Cluster result carries out phase patibhaga-nimitta from metrics evaluation, calculates that to obtain K+M cluster numbers each self-corresponding similar from desired value respectively,
And the K+M cluster numbers it is each it is self-corresponding it is similar from desired value in choose phase patibhaga-nimitta from index maximum, then by institute
Phase patibhaga-nimitta cluster numbers corresponding from index maximum are stated as preferable clustering number, and by the corresponding point target of the preferable clustering number
The cluster result of Amplitude Ratio characteristic vector, is used as the final cluster result of point target Amplitude Ratio characteristic vector；
Step 5, the decision gate limit value ε of false point target is set, and obtains the point target Amplitude Ratio characteristic vector accordingly
Final cluster result in the true point target that includes and false point target；Wherein, ε is natural number.
The present invention has the advantage that as follows compared with prior art：First, compared to existing method, the present invention is using each
The discrete distribution of Amplitude Ratio of radar station real goal echo, and the Amplitude Ratio of false target echo are approximately the same, and use system
The method for clustering of uniting, can more effectively resist various Deceiving interferences；
Second, the present invention is independent of long-term data accumulation, it is only necessary to which the time of a pulse repetition period (PRT) is just
The discriminating of true and false target can be completed, more efficient, practicality is stronger；
3rd, the present invention does not require that each false target has identical interference noise power ratio (JNR), it becomes possible to right
The anti-intensive decoy of deception formula, also can effectively be differentiated to the decoy that different Deceiving interferences are produced.
Brief description of the drawings
The present invention is described in further detail with reference to the accompanying drawings and detailed description.
The implementation process figure for the optimization method that Fig. 1 disturbs for a kind of multistation radar anti-active cheating formula of the present invention；
Fig. 2 is the distribution situation schematic diagram of real goal and false target respectively in Amplitude Ratio feature space；
Under Fig. 3 is three kinds of arrangement manners, the correct discrimination probability P of real goalPTRespectively with interference noise power ratio (JNR)
Change curve, wherein, abscissa be interference noise power ratio (JNR), ordinate be real goal correct discrimination probability
PPT；
Under Fig. 4 is three kinds of arrangement manners, the correct discrimination probability P' of false targetFTRespectively with interference noise power ratio
(JNR) change curve, wherein, abscissa is interference noise power ratio (JNR), and ordinate is the correct discriminating of false target
Probability P 'FT。
Embodiment
Reference picture 1, is a kind of implementation process figure of the optimization method of multistation radar anti-active cheating formula interference of the present invention,
The optimization method of this kind of multistation radar anti-active cheating formula interference, comprises the following steps：
Step 1, set up and N number of node radar, N number of node are included in netted radar system, the netted radar system
Radar receives signal respectively, and N is natural number, and N >=2, and in a pulse-recurrence time (PRT), N number of node thunder
Any one the node radar docking reached is collected mail after number progress matched filtering and target detection, obtains K+M point target；Its
In, K represents to receive true point target number present in signal, and M represents to receive false point target number present in signal.
Step 2, using first node radar to refer to radar, and K+M point target and other N-1 node radar are entered
Row time unifying is operated, obtain the N-1 node detections of radar to point target and the point target that arrives of reference detections of radar it
Between echo amplitude corresponding relation, p-th of point target is then chosen from the echo of n-th of node radar in n-th of node thunder
The echo amplitude ε reachedp,n, and the Amplitude Ratio characteristic vector Ω for obtaining p-th of point target is calculated accordinglyp, and then obtain K+M point
Each self-corresponding amplitude bit of target levies vector；Wherein, p ∈ { 1,2 ..., K+M }, n ∈ { 2 ..., N }.
Specifically, using first node radar as radar is referred to, K+M point target and other N-1 node radar are entered
Row time unifying is operated, obtain the N-1 node detections of radar to point target and the point target that arrives of reference detections of radar it
Between echo amplitude corresponding relation, p-th of point target is then chosen from the echo of n-th of node radar in n-th of node thunder
The echo amplitude ε reachedp,n, and the Amplitude Ratio characteristic vector Ω for obtaining p-th of point target is calculated accordinglyp, its expression formula is：
p∈{1,2,…,K+M}
n∈{2,…,N}
Wherein, εp,1Represent echo amplitude of p-th of point target on reference to radar, εp,nRepresent p-th of point target n-th
Echo amplitude on individual node radar, K represents to receive true point target number present in signal, and M is represented to receive and deposited in signal
False point target number.
According to the Amplitude Ratio characteristic vector Ω of p-th of point targetp, obtain each self-corresponding amplitude bit of K+M point target
Levy vector.
Step 3, set cluster numbers scope as [1, K+M], using the method for hierarchial-cluster analysis to K+M point target each
Corresponding Amplitude Ratio characteristic vector carries out cluster operation, obtains K+M cluster numbers in the range of the cluster numbers of setting each self-corresponding
The cluster result of the Amplitude Ratio characteristic vector of point target.
The sub-step of step 3 is：
3.1 set cluster numbers scope as [1, K+M], and by the Amplitude Ratio characteristic vector Ω of p-th of point targetpIt is classified as pth
Class, and then obtain K+M different classes, p ∈ { 1,2 ..., K+M }, the K+M different classes, i.e. cluster numbers K+
The cluster result of the Amplitude Ratio characteristic vector of the corresponding point targets of M.
3.2 obtain the Euclidean distance value between any two class in K+M different classes, wherein K+M each not phases
Euclidean distance value in same class between two classes is the Euclidean distance value between the element of its corresponding two class；
Specifically, it is European between described two classes if two classes only include an element, as independent class respectively
Distance value is the Euclidean distance value between the element of corresponding two classes, and calculating is obtained in K+M different classes accordingly
Euclidean distance value between each two class, and then D Euclidean distance value is obtained, choose minimum in the D Euclidean distance value
Euclidean distance value simultaneously optimizes generic operation；
Corresponding two classes of Euclidean distance value minimum in the D Euclidean distance value are merged into first optimization class,
K+M different classes are changed into K+M-1 different classes accordingly, and the K+M-1 different classes are cluster
The cluster result of the Amplitude Ratio characteristic vector of the corresponding point targets of number K+M-1.
3.3 choose any two class in the K+M-1 different class, if at least one class in any two class
During comprising two or more elements, the Euclidean distance value between two class is corresponding each self-contained element of two classes
Between maximum Euclidean distance value, and then calculate the Euclidean distance value for obtaining the different classes of K+M-1 accordingly, choose described
Minimum Euclidean distance value and generic operation is optimized in the Euclidean distance value of class different K+M-1；
By corresponding two classes of minimum Euclidean distance value of classes different the K+M-1 merge into second it is excellent
Change class, the K+M-1 different classes are changed into K+M-2 different classes accordingly, the K+M-2 different
Class is the cluster result of the Amplitude Ratio characteristic vector of the corresponding point targets of cluster numbers K+M-2.
3.4 repeat to optimize generic operation, and cluster numbers respectively K+M-3, K+M-4 ... are obtained successively, is each corresponded to when 1
Point target Amplitude Ratio characteristic vector cluster result, stop optimization generic operation, and then obtain setting cluster numbers in the range of K
The cluster result of the Amplitude Ratio characteristic vector of+M each self-corresponding point targets of cluster numbers.
Step 4, each to K+M cluster numbers in the range of the cluster numbers of setting self-corresponding point target Amplitude Ratio characteristic vector
Cluster result carries out phase patibhaga-nimitta from metrics evaluation, calculates that to obtain K+M cluster numbers each self-corresponding similar from desired value respectively,
And the K+M cluster numbers it is each it is self-corresponding it is similar from desired value in choose phase patibhaga-nimitta from index maximum, then by institute
Phase patibhaga-nimitta cluster numbers corresponding from index maximum are stated as preferable clustering number, and by the corresponding point target of the preferable clustering number
The cluster result of Amplitude Ratio characteristic vector, is used as the final cluster result of point target Amplitude Ratio characteristic vector.
Specifically, self-corresponding cluster result each to K+M cluster numbers in the range of the cluster numbers of setting carry out phase patibhaga-nimitta from
Metrics evaluation, each self-corresponding point target Amplitude Ratio characteristic vector of K+M cluster numbers first in the range of the cluster numbers of setting
In cluster result, the cluster result of the corresponding point target Amplitude Ratio characteristic vector of q-th of cluster numbers is chosen, and to described q-th
The cluster result of the corresponding point target Amplitude Ratio characteristic vector of cluster numbers carries out phase patibhaga-nimitta from metrics evaluation, and calculating is obtained q-th
The phase patibhaga-nimitta of the corresponding cluster result of cluster numbers is from desired value HS (q), its expression formula：
HS (q)=| Hom (q)-Sep (q) |
Wherein, i ∈ { 1,2 ..., q }, j ∈ { 1,2 ..., q }, q ∈ [1, K+M], niRepresent i-th of independent class CiComprising
Element number, njRepresent j-th of independent class CjComprising element number, R (s, t) represent s-th of point target Amplitude Ratio Characteristic Vectors
Measure ΩsWith the Amplitude Ratio characteristic vector Ω of t-th of point targettBetween coefficient correlation, s ∈ { 1,2 ..., K+M }, K represent receive
True point target number present in signal, M represents to receive false point target number, t ∈ { 1,2 ..., K+ present in signal
M}；As s=t, R (s, t)=1；As s ≠ t,||·||2Represent 2 norms, ΩmRepresent the
The Amplitude Ratio characteristic vector of m point target, ΩqRepresent the Amplitude Ratio characteristic vector of q-th of point target, m ∈ { 1,2 ..., K+M },
q∈{1,2,…,K+M}。
According to the phase patibhaga-nimitta of the corresponding cluster result of q-th of cluster numbers from desired value HS (q), and then obtain K+M cluster
Number it is each self-corresponding similar from desired value, and the K+M cluster numbers it is each it is self-corresponding it is similar from desired value in selection
Phase patibhaga-nimitta is from index maximum, then using phase patibhaga-nimitta cluster numbers corresponding from index maximum as preferable clustering number, and
By the cluster result of the Amplitude Ratio characteristic vector of the corresponding point target of the preferable clustering number, point target Amplitude Ratio characteristic vector is used as
Final cluster result.
Step 5, the decision gate limit value ε of false point target is set, and obtains the point target Amplitude Ratio characteristic vector accordingly
Final cluster result in the true point target that includes and false point target；Wherein, ε is natural number.
Specifically, due in Amplitude Ratio feature space, each node radar in the netted radar system it is true
The discrete distribution of the Amplitude Ratio of real point target echo, and the Amplitude Ratio of False Intersection Points target echo is approximately the same, and distribution is concentrated so that
The Amplitude Ratio characteristic vector of K+M point target is carried out after clustering, each true point target can individually turn into a class,
I.e. independent class；And false target can sum up in the point that same class because of Amplitude Ratio distribution concentration, the judgement of false point target is set accordingly
Threshold value ε, if number of any one class comprising point target is less than ε, its corresponding class bag in the final cluster result
The point target contained corresponds to true point target respectively.
If any one class judges to include in such comprising ε or ε with last point target in the final cluster result
Point target correspond to false point target respectively.
Wherein, the decision gate limit value ε of the false point target is typically taken as 2；If in view of the point target amplitude bit
The number of the false point target included in the final cluster result for levying vector is more than the number of true point target, can be taken as ε
Integer more than 2.
The ability of present invention confrontation Deceiving interference can pass through following emulation further checking.
(1) simulation parameter
Emulation experiment, the work of first node radar are carried out by taking the netted radar system of four node radar compositions as an example
Pattern is send-receive pattern, and its excess-three node radar is respectively reception pattern, then causes four node thunders
Netted radar system up to composition is detected to the same space region, there is five true mesh in the same space region of detection
Mark, one of target carries Self defense jammer, and produces 30 active cheating formula decoys.
(2) experiment content and interpretation of result
Experiment one：In above-mentioned experiment scene, four node Method in Positioning of Radar situations are [0,0], [- 300,0], [300,0],
[600,0], rectangular coordinate system are set up by reference point of first node radar, the size of five real goals is 15m, should
The original state of five real goals is as follows：
When noise in the netted radar system is white Gaussian noise, distribution of the true and false target in Amplitude Ratio feature space
Situation is as shown in Fig. 2 Fig. 2 is the distribution situation schematic diagram of real goal and false target respectively in Amplitude Ratio feature space；
Wherein, false alarm rate is determined as the false alarm rate of real goal for the false target of setting.
From fig. 2 it can be seen that in Amplitude Ratio feature space, five real goal random distributions, and false target is
Integrated distribution, this is due to the presence of intrasystem noise so that the Amplitude Ratio of false target not exclusively, but ten tap
Closely.
Then method proposed by the present invention is used, obtaining last identification result is：It is 23,26,28 to detect target sequence number,
30,32 target is real goal, and remaining target is false target.By result above and analysis, it is seen that the side of carrying of the invention
The validity of method.
Experiment two：Analyze the influence that radar geometry cloth station differentiates performance to this method.Four node thunders in one will be tested
Netted radar system up to composition is set to arrangement manner 1, in addition to radar site, and remaining setting keeps constant, other two kinds of cloth
The mode of station is respectively：
Cloth station 2：[0,0],[-200,0],[200,0],[400,0]；
Cloth station 3：[0,0],[-300,0],[300,0],[600,0],[-600,0].
If with the interference noise power ratio (JNR) of first node radar for variable, incrementally 5dB, remaining node each time
The interference noise power ratio (JNR) of radar can be tried to achieve by bistatic radar equation.According to the interference noise power ratio of each fixation
(JNR) 10, are carried out to active decoy discrimination method proposed by the present invention4Secondary Monte Carlo experiment, statistics obtains real goal
Correct discrimination probability PPTAnd the correct discrimination probability P' of false decoyFT, respectively as shown in Figure 3 and Figure 4；Fig. 3 is three kinds of cloth stations
Under mode, the correct discrimination probability P of real goalPTRespectively with the change curve of interference noise power ratio (JNR), wherein, it is horizontal
Coordinate is interference noise power ratio (JNR), and ordinate is the correct discrimination probability P of real goalPT；Fig. 4 is three kinds of arrangement manners
Under, the correct discrimination probability P' of false targetFTRespectively with the change curve of interference noise power ratio (JNR), wherein, abscissa
For interference noise power ratio (JNR), ordinate is the correct discrimination probability P' of false targetFT；Interference noise in Fig. 3 and Fig. 4
Power ratio (JNR) excursion is 20~60.
It can see from Fig. 3 and Fig. 4, with the increase of interference noise power ratio (JNR), real goal correctly differentiates general
Rate PPTWith the correct discrimination probability P' of false targetFTIt is continuously increased respectively, especially when interference noise power ratio (JNR)>30 with
Afterwards, respectively with a relatively high discrimination probability.In fact, in order to obtain preferably deception performance, interference noise power ratio (JNR)
Generally bigger, also therefore method proposed by the present invention can more effectively differentiate false target, retain real goal.
From Fig. 3 and Fig. 4 it can further be seen that under three kinds of arrangement manners, method proposed by the present invention has very respectively
The correct discrimination probability of the correct discrimination probability of satisfied real goal and false target, and performance is sufficiently close to, and illustrates radar
Discriminating performance impact very little of the cloth station to institute's extracting method of the present invention.But the result of first, second kind of arrangement manner is contrasted respectively, the
A kind of cloth station, which has, preferably differentiates performance, because the first cloth station has baseline between longer radar station, baseline is longer,
The correlation of real goal is smaller, and in Amplitude Ratio feature spatial spreading branch, randomness is bigger, differentiates that performance is better.It is right respectively
Than the result of first, the third arrangement manner, it can be deduced that, receiving station is more, differentiates that performance is better.Because, receiving station
It is more, it becomes possible to more information are provided and set up a multidimensional feature space, and then greatly increase real goal and false mesh
Separability between mark.
In summary, emulation experiment demonstrates the correctness of the present invention, validity and reliability.
Obviously, those skilled in the art can carry out the essence of various changes and modification without departing from the present invention to the present invention
God and scope；So, if these modifications and variations of the present invention belong to the scope of the claims in the present invention and its equivalent technologies
Within, then the present invention is also intended to comprising including these changes and modification.
Claims (5)
1. a kind of optimization method of multistation radar anti-active cheating formula interference, it is characterised in that comprise the following steps：
Step 1, set up and N number of node radar, N number of node radar are included in netted radar system, the netted radar system
Receive signal respectively, N is natural number, and N >=2, and within a pulse-recurrence time, appointing in N number of node radar
One node radar docking of meaning is collected mail after number progress matched filtering and target detection, obtains K+M point target；Wherein, K represents to connect
True point target number present in the collection of letters number, M represents to receive false point target number present in signal；
Step 2, using first node radar as when referring to radar, and K+M point target and other N-1 node radar being carried out
Between alignment operation, obtain the N-1 node detections of radar to point target and the point target that arrives with reference to detections of radar between
Echo amplitude corresponding relation, then chooses p-th of point target in n-th node radar from the echo of n-th of node radar
Echo amplitude εP, n, and the Amplitude Ratio characteristic vector Ω for obtaining p-th of point target is calculated accordinglyp, and then obtain K+M point target
Each self-corresponding amplitude bit levies vector；Wherein, p ∈ { 1,2 ..., K+M }, n ∈ { 2 ..., N }；
Step 3, cluster numbers scope is set as [1, K+M], and self-corresponding amplitude bit each to K+M point target levies vector accordingly
Cluster operation is carried out, the Amplitude Ratio Characteristic Vectors of each self-corresponding point target of K+M cluster numbers in the range of the cluster numbers of setting are obtained
The cluster result of amount；
Step 4, the cluster of self-corresponding point target Amplitude Ratio characteristic vector each to K+M cluster numbers in the range of the cluster numbers of setting
As a result carry out phase patibhaga-nimitta and calculate that to obtain K+M cluster numbers each self-corresponding similar from desired value from metrics evaluation, respectively, and
The K+M cluster numbers are each self-corresponding similar from phase patibhaga-nimitta is chosen in desired value from index maximum, then by the phase
Patibhaga-nimitta cluster numbers corresponding from index maximum are as preferable clustering number, and by the amplitude of the corresponding point target of the preferable clustering number
Than the cluster result of characteristic vector, the final cluster result of point target Amplitude Ratio characteristic vector is used as；
Step 5, the decision gate limit value ε of false point target is set, and obtains the point target Amplitude Ratio characteristic vector accordingly most
The true point target and false point target included in whole cluster result；Wherein, ε is natural number.
2. the optimization method of a kind of multistation radar anti-active cheating formula interference as claimed in claim 1, it is characterised in that in step
In rapid 2, the Amplitude Ratio characteristic vector Ω for obtaining p-th of point targetp, its expression formula is：
<mrow>
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<mi>&Omega;</mi>
<mi>p</mi>
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<mo>=</mo>
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<msubsup>
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<mi>&eta;</mi>
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<mo>&Element;</mo>
<mo>{</mo>
<mn>1</mn>
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<mn>2</mn>
<mo>,</mo>
<mo>...</mo>
<mo>,</mo>
<mi>K</mi>
<mo>+</mo>
<mi>M</mi>
<mo>}</mo>
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<mi>N</mi>
<mo>}</mo>
</mrow>
Wherein, εP, 1Represent echo amplitude of p-th of point target on reference to radar, εP, nRepresent p-th of point target in n-th of section
Echo amplitude on point radar, K represents to receive true point target number present in signal, and M represents to receive present in signal
False point target number.
3. the optimization method of a kind of multistation radar anti-active cheating formula interference as claimed in claim 1, it is characterised in that in step
In rapid 3, the Amplitude Ratio characteristic vector of each self-corresponding point target of K+M cluster numbers in the range of the cluster numbers for obtaining setting
Cluster result, its process is：
3.1 set cluster numbers scope as [1, K+M], and by the Amplitude Ratio characteristic vector Ω of p-th of point targetpPth class is classified as, is entered
And K+M different classes are obtained, p ∈ { 1,2 ..., K+M }, the K+M different classes, i.e. cluster numbers K+M correspondences
Point target Amplitude Ratio characteristic vector cluster result；
3.2 obtain the Euclidean distance value between any two class in K+M different classes, and wherein K+M different
Euclidean distance value in class between two classes is the Euclidean distance value between the element of its corresponding two class；
Specifically, if two classes only include an element, as independent class respectively, the Euclidean distance between described two classes
It is worth the Euclidean distance value between the element for corresponding two classes, and calculating is obtained every two in K+M different classes accordingly
Euclidean distance value between individual class, and then obtain D Euclidean distance value, chooses minimum European in the D Euclidean distance value
Distance value simultaneously optimizes generic operation；
Corresponding two classes of Euclidean distance value minimum in the D Euclidean distance value are merged into first optimization class, accordingly K
+ M different classes are changed into K+M-1 different classes, and the K+M-1 different classes are cluster numbers K+M-
The cluster result of the Amplitude Ratio characteristic vector of 1 corresponding point target；
3.3 choose any two class in the K+M-1 different class, if at least one class is included in any two class
During two or more elements, the Euclidean distance value between two class is between corresponding each self-contained element of two classes
Maximum Euclidean distance value, and then calculate the Euclidean distance value for obtaining the different classes of K+M-1 accordingly, choose the K+M-
Minimum Euclidean distance value and generic operation is optimized in the Euclidean distance value of 1 different class；
Corresponding two classes of minimum Euclidean distance value of the K+M-1 different classes are merged into second optimization
Class, accordingly the K+M-1 different classes be changed into K+M-2 different classes, the K+M-2 different classes
The as cluster result of the Amplitude Ratio characteristic vector of the corresponding point targets of cluster numbers K+M-2；
3.4 repeat to optimize generic operation, cluster numbers respectively K+M-3, K+M-4 ... are obtained successively, each self-corresponding point when 1
The cluster result of the Amplitude Ratio characteristic vector of target, stops optimization generic operation, and then obtain in the range of the cluster numbers set K+M
The cluster result of the Amplitude Ratio characteristic vector of each self-corresponding point target of cluster numbers.
4. the optimization method of a kind of multistation radar anti-active cheating formula interference as claimed in claim 1, it is characterised in that in step
In rapid 4, the K+M cluster numbers are each self-corresponding similar from desired value, and its calculating process is：
The cluster knot of each self-corresponding point target Amplitude Ratio characteristic vector of K+M cluster numbers first in the range of the cluster numbers of setting
In fruit, the cluster result of the corresponding point target Amplitude Ratio characteristic vector of q-th of cluster numbers is chosen, and to q-th of cluster numbers
The cluster result of corresponding point target Amplitude Ratio characteristic vector carries out phase patibhaga-nimitta from metrics evaluation, and calculating obtains q-th of cluster numbers
The phase patibhaga-nimitta of corresponding cluster result is from desired value HS (q), its expression formula：
HS (q)=| Hom (q)-Sep (q) |
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Wherein, i ∈ { 1,2 ..., q }, j ∈ { 1,2 ..., q }, q ∈ [1, K+M], niRepresent i-th of independent class CiComprising element
Number, njRepresent j-th of independent class CjComprising element number, R (s, t) represent s-th of point target Amplitude Ratio characteristic vector
ΩsWith the Amplitude Ratio characteristic vector Ω of t-th of point targettBetween coefficient correlation, s ∈ { 1,2 ..., K+M }, K represent receive letter
True point target number present in number, M represents to receive false point target number, t ∈ { 1,2 ..., K+M } present in signal；
As s=t, R (s, t)=1；As s ≠ t,||·||2Represent 2 norms, ΩmRepresent m-th
The Amplitude Ratio characteristic vector of point target, ΩqRepresent the Amplitude Ratio characteristic vector of q-th of point target, m ∈ { 1,2 ..., K+M }, q ∈
{ 1,2 ..., K+M }.
5. the optimization method of a kind of multistation radar anti-active cheating formula interference as claimed in claim 1, it is characterised in that in step
In rapid 5, decision gate the limit value ε, wherein ε of the false point target of setting are the integer more than or equal to 2.
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