CN106056098A - Pulse signal cluster sorting method based on class merging - Google Patents
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Abstract
The invention relates to a pulse signal cluster sorting method based on class merging. The method aims to overcome the defects that the class number accuracy in the existing cluster result is low and the cluster number is inconsistent with the real signal number after class merging. The pulse signal cluster sorting method based on class merging specifically comprises the following steps: 1, determining initial cluster centers and sorting distances; 2, obtaining new cluster centers; 3, calculating whether the new cluster centers satisfy signal features; and 4, merging the cluster centers satisfying the signal features, thereby accomplishing signal cluster sorting based on class merging. The method is applied in the field of signal processing.
Description
Technical field
The present invention relates to pulse signal based on categories combination cluster method for separating.
Background technology
Classical signal cluster deinterleaving algorithm is based on K means clustering algorithm, and traditional K mean algorithm is with given
Cluster centre completes the sorting of signal, then cluster centre choose class number and the correctness largely determining classification.
Therefore signal clustering algorithm is generally adopted by the clustering algorithm improved, and i.e. chooses the pulse signal parameter first intercepted and captured in cluster
The heart, according to euclids throrem, it is judged that succeeding impulse signal should individually become new cluster centre, still should incorporate into
In some classes.But there is problems of this clustering method to cluster according to the difference of pulse signal modulation type, than
As: the scope of the cluster centre of dither signal wants big compared to the cluster centre scope of normal signal, in the cluster of sliding varying signal
The heart should linearly change, and causes the classification number accuracy in cluster result low, the clusters number after class merging and true letter
Count mesh is not inconsistent.
Summary of the invention
The classification number accuracy that the invention aims to solve in existing cluster result is low, the cluster after class merging
The shortcoming that number is not inconsistent with actual signal number, and a kind of pulse signal based on categories combination cluster method for separating is proposed.
A kind of pulse signal based on categories combination cluster method for separating specifically follows the steps below:
Step one, determine initial cluster center d1,d2…dnWith classifying distance D1,D2…Dn, d1It is at the beginning of first classification
Beginning cluster centre, d2It is the initial cluster center of second classification, dnIt is the initial cluster center of the n-th classification, D1It it is first
The classifying distance of classification, D2It is the classifying distance of second classification, DnBeing the classifying distance of the n-th classification, n is positive integer；
Step 2, data point a that radar receiver is received1,a2…amThe most successively with initial cluster center d1,d2…
dnCalculate euclidean clustering distance, obtain | ai-dj|, wherein, 1≤i≤m, 1≤j≤n, m are positive integer；
If | ai-dj|≤Dj, then by data point aiIt is classified as djIndividual initial cluster center, corresponding one of each cluster centre
Classification；
If | ai-dj|>Dj, then data point aiIt is not belonging to djIndividual initial cluster center, continues and more than djAnd be less than
In dnInitial cluster center compare, if data point aiIt is not belonging to more than djAnd less than or equal to dnInitial cluster center, then
By data point aiIt is considered as a new cluster centre；
Until all data points a1,a2…amComplete cluster sorting, obtain new cluster centre d1′,d2′…dn′；
Step 3, calculate whether meet signal characteristic between new cluster centre；
Step 4, will meet signal characteristic cluster centre merge, complete categories combination signal cluster sorting.
The invention have the benefit that
The present invention proposes a kind of pulse signal based on categories combination cluster deinterleaving algorithm, compared to traditional algorithm, this
Signal after normally cluster is carried out categories combination by the bright signal cluster deinterleaving algorithm utilizing categories combination, and that terminates cluster is each
The cluster centre of individual group calculates, if meeting certain rule, then can merge into same classification, do so good
Place is to improve the classification number accuracy in cluster result, makes the classification number accuracy in cluster result bring up to from 85%
95%, so that the clusters number after group feature more conforms to the Changing Pattern of signal, class merging in actual cluster is with true
Real signal number is consistent, makes the clusters number true number closer to signal of reality, the beneficially parameter of follow-up signal
Extract and process, solve possible mistake cluster and the clusters number problem more than actual signal number.
Input data are the data in table 1, and the multiparameter cluster merged by nothing respectively and the multiparameter cluster having merging are right
Its Monte Carlo simulation, simulation result is as shown in table 3: in emulation in addition to five pairs of parameters of signal own, be also mixed into 12 right
Ghost pulse, the classification number that therefore ought to obtain is 17 classes, it is contemplated that there is noise jamming and jitter problem, can tolerance be expanded
Between 16-18 class.During the multiparameter clustering procedure having merging as can be seen from Table 3 emulates at 1000 times, there are 849 cluster numbers
Mesh falls between 16-18, and the multiparameter clustering procedure without merging has 950 times and falls between 20-22, differs with correct classification number
A lot, therefore relevant classification is merged and the classification number accuracy in cluster result can be effectively improved, it was demonstrated that should
The superiority of inventive method.
Accompanying drawing explanation
Fig. 1 is flow chart of the present invention；
Fig. 2 is that RF and PW combines cluster sorting Vibrating pulse signal results figure.
Detailed description of the invention
Detailed description of the invention one: combine Fig. 1 and present embodiment is described, a kind of based on categories combination the arteries and veins of present embodiment
Rush what signal cluster method for separating specifically followed the steps below:
Step one, determine initial cluster center d1,d2…dnWith classifying distance D1,D2…Dn, d1It is at the beginning of first classification
Beginning cluster centre, d2It is the initial cluster center of second classification, dnIt is the initial cluster center of the n-th classification, D1It it is first
The classifying distance of classification, D2It is the classifying distance of second classification, DnBeing the classifying distance of the n-th classification, n is positive integer；
Step 2, data point a that radar receiver is received1,a2…amThe most successively with initial cluster center d1,d2…
dnCalculate euclidean clustering distance, obtain | ai-dj|, wherein, 1≤i≤m, 1≤j≤n,
If | ai-dj|≤Dj, then by data point aiIt is classified as djIndividual initial cluster center, corresponding one of each cluster centre
Classification；
If | ai-dj|>Dj, then data point aiIt is not belonging to djIndividual initial cluster center, continues and more than djAnd be less than
In dnInitial cluster center compare, if be not belonging to more than djAnd less than or equal to dnInitial cluster center, then by data point
aiIt is considered as new cluster centre, i.e. a dn+1；
Until all data points a1,a2…amCompleting cluster sorting, m is positive integer, obtains new cluster centre d1′,d2′…
dn′；
Step 3, calculate whether meet signal characteristic between new cluster centre；
Step 4, by meet signal characteristic cluster centre merge after, the number and the scope that finally cluster are more nearly very
The classification of signal and actual quantity in real environment, complete the signal cluster sorting of categories combination.
Detailed description of the invention two: present embodiment is unlike detailed description of the invention one: in described step 2Wherein k is the data point number of each cluster centre, acFor each poly-
The data point that apoplexy due to endogenous wind pericardium contains, 1≤c≤k, k are positive integer.
Other step and parameter are identical with detailed description of the invention one.
Detailed description of the invention three: present embodiment is unlike detailed description of the invention one or two: described step 3 is fallen into a trap
Whether signal characteristic is met between new cluster centre；Concrete mistake is referred to as:
For dither signal, if new cluster centre meets relation: | di'-dj'|≤(1+Δδ)D0, then di'、dj' two
Individual cluster centre should merge into same category；
Wherein, 1≤i≤n, 1≤j≤n, wherein di', dj' it is respectively d1′,d2′…dn' middle any two cluster centre, D0
For threshold parameter, Δ δ is that shake measures 0.01-0.03；Threshold parameter is artificial setting, empirical value.
Other step and parameter are identical with detailed description of the invention one or two.
Detailed description of the invention four: present embodiment is unlike one of detailed description of the invention one to three: described step 3
Whether signal characteristic is met between the cluster centre that middle calculating is new；Concrete mistake is referred to as:
For sliding varying signal, if new cluster centre meets relation:Then di'、dj'、dl' three gather
Same category should be merged in class center；
Wherein, 1≤i≤n, 1≤j≤n, 1≤l≤n, wherein di', dj', dl' it is respectively d1′,d2′…dnIn ' any three
Individual cluster centre, λ is any real number.
Other step and parameter are identical with one of detailed description of the invention one to three.
Detailed description of the invention five: present embodiment is unlike one of detailed description of the invention one to four: described step 3
Whether signal characteristic is met between the cluster centre that middle calculating is new；Concrete mistake is referred to as:
For irregular signal, if new cluster centre meets relation:Then di'、djIn ' two cluster
The heart should merge into same category；
Wherein, 1≤i≤n, 1≤j≤n, di', dj' it is respectively d1′,d2′…dn' middle any two cluster centre, D1For inspection
Survey parameter, described detection parameter D1Arrange for artificial, empirical value.
Other step and parameter are identical with one of detailed description of the invention one to four.
Employing following example checking beneficial effects of the present invention:
Embodiment one:
A kind of pulse signal based on categories combination of the present embodiment cluster method for separating is specifically prepared according to following steps
:
The data used in experiment are as shown in table 1: every radar 20 groups of data totally one hundred groups of data are blended in one at random
Rising, pulse-width PW adds the shake of 1.8 μ s and radio frequency adds the amount of jitter of 1.8MHz respectively.On this basis, again at signal
Add multiple ghost pulse signal simulation and go out real signal environment.In last radar pulse data, PW value adds average
Being 0 μ s, variance is the measurement error of the normal distribution of 0.8 μ s, and RF value is 0MHz plus average, and variance is that the normal state of 0.4MHz is divided
The measurement error of cloth.Table 2 is central value of parameter after cluster.
Table 1 radar parameter
Step one, take two data of radar data be initial cluster center (20,25) and (62,38) and classification away from
From 15 and 16, (20,25) are the initial cluster centers of first classification, and (62,38) are the initial cluster centers of second classification,
15 is the classifying distance of first classification, and 16 is the classifying distance of second classification；
Step 2, the data point that radar receiver is received respectively the most successively with initial cluster center (20,25) and (62,
38) calculate euclidean clustering distance, obtain | ai-dj|, wherein, 3≤i≤112,1≤j≤2,
If | ai-dj|≤Dj, then by data point aiIt is classified as djIndividual initial cluster center, corresponding one of each cluster centre
Classification；
If | ai-dj|>Dj, then data point aiIt is not belonging to djIndividual initial cluster center, continues and more than djAnd be less than
In dnInitial cluster center compare, if be not belonging to more than djAnd less than or equal to dnInitial cluster center, then by data point
aiIt is considered as new cluster centre, i.e. a dn+1, until all data points a1,a2…amCompleting cluster sorting, m is positive integer,
To new cluster centre d1′,d2′…dn′；
DescribedWherein k is the data point of each cluster centre
Number, acThe data point comprised for each cluster centre, 1≤c≤k, k are positive integer；
Step 4, calculate between new cluster centre, whether to meet certain signal characteristic,
For dither signal, if new cluster centre meets relation: | di'-dj'|≤(1+Δδ)D0, 1≤i≤n, 1≤
J≤n, wherein Δ δ takes 0.3, D0Take 16, wherein di', dj' it is respectively d1′,d2′…dn' middle any two cluster centre, then di'、
dj' two cluster centre should merge into same category；
Central value of parameter after table 2 cluster
As can be seen from Figure 2 pulse signal is substantially broadly divided into five classes, and ' o ' represents pulse signal, '+' represent in cluster
The heart, other some are interference signal and ghost pulse.For the Vibrating pulse signal in emulation, as can be seen from Figure 2 exist
Near cluster centre, pulse signal has bigger shake, and in table 2, the central value after cluster exists one compared to initial cluster center
Fixed deviation, if ghost pulse distance signal is close together likely mistake can be divided into a class or signal jitter scope is excessive leads
In the number of writing, indivedual subpulses are mistaken for another kind of, and the levels of precision of separation results is relevant with the version of signal itself, i.e.
The modulation format of signal influences whether cluster centre thus affects cluster result.
Input data are the data in table 1, and the multiparameter cluster merged by nothing respectively and the multiparameter cluster having merging are right
Its Monte Carlo simulation, simulation result is as shown in table 3:
The contrast table of 3 two kinds of clustering method results of table
Simulation analysis: in emulation in addition to five pairs of parameters of signal own, has also been mixed into 12 pairs of ghost pulses, therefore ought to
The classification number obtained is 17 classes, it is contemplated that there is noise jamming and jitter problem, between can expanding tolerance for 16-18 class.From
Table 3 can be seen that, the multiparameter clustering procedure of merging, in 1000 emulation, has 849 clusters number to fall between 16-18,
And the multiparameter clustering procedure without merging has 950 times and falls between 20-22, mutually far short of what is expected with correct classification number, therefore will be relevant
Classification merge and the classification number accuracy in cluster result can be effectively improved, it was demonstrated that this inventive method superior
Property.
The present invention also can have other various embodiments, in the case of without departing substantially from present invention spirit and essence thereof, and this area
Technical staff is when making various corresponding change and deformation according to the present invention, but these change accordingly and deformation all should belong to
The protection domain of appended claims of the invention.
Claims (5)
1. pulse signal based on a categories combination cluster method for separating, it is characterised in that: a kind of arteries and veins based on categories combination
Rush what signal cluster method for separating specifically followed the steps below:
Step one, determine initial cluster center d1,d2…dnWith classifying distance D1,D2…Dn, d1It it is the initial clustering of first classification
Center, d2It is the initial cluster center of second classification, dnIt is the initial cluster center of the n-th classification, D1It is first classification
Classifying distance, D2It is the classifying distance of second classification, DnBeing the classifying distance of the n-th classification, n is positive integer；
Step 2, data point a that radar receiver is received1,a2…amThe most successively with initial cluster center d1, d2…dnMeter
Calculate euclidean clustering distance, obtain | ai-dj|, wherein, 1≤i≤m, 1≤j≤n, m are positive integer；
If | ai-dj|≤Dj, then by data point aiIt is classified as djIndividual initial cluster center, the corresponding class of each cluster centre
Not；
If | ai-dj|>Dj, then data point aiIt is not belonging to djIndividual initial cluster center, continues and more than djAnd less than or equal to dn's
Initial cluster center compares, if data point aiIt is not belonging to more than djAnd less than or equal to dnInitial cluster center, then by data
Point aiIt is considered as a new cluster centre；
Until all data points a1,a2…amComplete cluster sorting, obtain new cluster centre d1′,d2′…dn′；
Step 3, calculate whether meet signal characteristic between new cluster centre；
Step 4, will meet signal characteristic cluster centre merge, complete categories combination signal cluster sorting.
A kind of pulse signal based on categories combination cluster method for separating, it is characterised in that: described
In step 2Wherein k is the data point number of each cluster centre, ac
The data point comprised for each cluster centre, 1≤c≤k, k are positive integer.
A kind of pulse signal based on categories combination cluster method for separating, it is characterised in that: described
Step 3 calculates and between new cluster centre, whether meets signal characteristic；Concrete mistake is referred to as:
For dither signal, if new cluster centre meets relation: | di'-dj'|≤(1+Δδ)D0, then di'、dj' two gather
Same category should be merged in class center；
Wherein, 1≤i≤n, 1≤j≤n, wherein di', dj' it is respectively d1′,d2′…dn' middle any two cluster centre, D0For threshold
Value parameter, Δ δ is that shake measures 0.01-0.03.
A kind of pulse signal based on categories combination cluster method for separating, it is characterised in that: described
Step 3 calculates and between new cluster centre, whether meets signal characteristic；Concrete mistake is referred to as:
For sliding varying signal, if new cluster centre meets relation:Then di'、dj'、dlIn ' three cluster
The heart should merge into same category；
Wherein, 1≤i≤n, 1≤j≤n, 1≤l≤n, wherein di', dj', dl' it is respectively d1′,d2′…dnIn ', any three are gathered
Class center, λ is any real number.
A kind of pulse signal based on categories combination cluster method for separating, it is characterised in that: described
Step 3 calculates and between new cluster centre, whether meets signal characteristic；Concrete mistake is referred to as:
For irregular signal, if new cluster centre meets relation: | di'-dj' |=D1, then di'、dj' two cluster centre should
Merge into same category；
Wherein, 1≤i≤n, 1≤j≤n, di', dj' it is respectively d1′,d2′…dn' middle any two cluster centre, D1For detection ginseng
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| CN114114199A (en) * | 2022-01-27 | 2022-03-01 | 北京宏锐星通科技有限公司 | Sorting method and sorting device for synthetic aperture radar signal parameters |
| CN115664902A (en) * | 2022-12-12 | 2023-01-31 | 南京莱芯科技有限公司 | Pulse signal synchronous acquisition method and device, electronic equipment and storage medium |
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