CN107238817A - A kind of parameter adaptive setting and the radar emitter signal method for separating of adjust automatically - Google Patents
A kind of parameter adaptive setting and the radar emitter signal method for separating of adjust automatically Download PDFInfo
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- CN107238817A CN107238817A CN201710539573.5A CN201710539573A CN107238817A CN 107238817 A CN107238817 A CN 107238817A CN 201710539573 A CN201710539573 A CN 201710539573A CN 107238817 A CN107238817 A CN 107238817A
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Abstract
The invention discloses a kind of setting of parameter adaptive and the radar emitter signal method for separating of adjust automatically.This method introduces intrapulse feature while pulse angle of arrival is combined first in the selection of characteristic vector --- and symmetrical Holder coefficients are sorted, then method is based on iteration self-organizing data analysis algorithm, algorithm is referred to as ISODATA, according to the feature of characteristic vector itself, using the method for searching for histogram peak statistics, expected class number is set, simultaneously to the lower limit of minimum range carries out initial setting between the upper limit of the standard deviation of each data distribution and all kinds of centers in the ISODATA classes of algorithms, finally when ISODATA algorithms are run to the lower limit control setting Adaptive Criterion of minimum range reaches the accurate purpose finally sorted between the upper limit of the standard deviation of each data distribution and all kinds of centers in class, signal sorting is solved the problems, such as in this way.
Description
Technical field
The present invention relates to a kind of radar emitter signal method for separating.
Background technology
With the fast development of modern radar technology, the extensive use of especially various new system radar, battlefield electromagnetism ring
Border is increasingly complicated and changeable, and it is mainly shown as that emitter Signals in the same time and place are seriously interlocked, and signal modulation form complexity is various,
This seriously to be overlapped as the feature space border described by basic parameter, the emitter Signals based on traditional five big basic parameters
The need for sorting can not meet reality substantially.
Intra-pulse feature analysis method opens new Research Thinking to reduce the overlapping of feature space, and it to extract newly
Signal parameter is become a reality with the idea for further fully characterizing radar emitter signal feature.
The advantage of ISODATA algorithms is to will recognize that most of object to be sorted is dividing by the process progressively broken up
The essential attribute unlikely shown at the beginning of class, it is to decide final clusters number in its sole discretion according to the difference of sample, this
So that classification more science.But ISODATA algorithms also have its obvious shortcoming：Traditional ISODATA clustering algorithms parameter it is pre-
If often rule of thumb setting, and under modern battlefield electromagnetic environment, radar emitter signal number of species is various, and change is not
Survey, prior information can not be obtained at all the parameter of algorithm is accurately preset, so as to cause the result of cluster barely satisfactory.
Therefore, the present invention is felt the pulse, and interior characteristic parameter --- symmetrical Holder coefficients are added among sorting parameter, by certainly
The mode of adjustment ISODATA algorithms is adapted to, the sorting of radar emitter signal is reasonably realized.
The content of the invention
In order to solve the problems, such as the accurate sorting of radar emitter signal, the invention discloses a kind of parameter adaptive set and
The radar emitter signal method for separating of adjust automatically.The characteristic vector that this method is used is pulse angle of arrival and symmetrical Holder
The two parameters of coefficient, based on iteration self-organizing data analysis algorithm, algorithm is referred to as ISODATA, first according to characteristic vector
The feature of itself, sets expected class number, while to the mark of each data distribution in class using the method for searching for histogram peak statistics
The lower limit of minimum range carries out initial setting between the upper limit of quasi- difference and all kinds of centers, when algorithm is run to each data distribution in class
Standard deviation the upper limit and the lower limit control setting Adaptive Criterion of minimum range between all kinds of centers, letter is solved in this way
Number sorting problem.
The detailed technology measure step of the present invention is as follows：
The first step：The selection of characteristic vector
From radar emitter signal pulse angle of arrival and symmetrical Holder coefficients constitutive characteristic vector.
(1) extraction of Radar emitter pulse angle of arrival
The pulse of radar signal stream of pulses is extracted, the pulse angle of arrival in radar signal stream of pulses pulse descriptive word is obtained.
(2) extraction of symmetrical Holder coefficients
The pulse of radar signal stream of pulses is extracted, and the signal of extraction is pre-processed, it is main to include to signal frequency domain
Conversion, the normalization of bandwidth and energy.
According to following formula, the frequency spectrum of pulse and the symmetrical Holder coefficients H of rectangular signal and triangular signal are calculatedUAnd HT。
(1) with rectangular signal U (k) symmetrical Holder coefficients
(2) with triangular signal T (k) symmetrical Holder coefficients
In formula, F (i) is the frequency spectrum of signal, while selected p=5, q=5.
Construct characteristic vector B=(DOA, HT) and D=(DOA, HT,HU), wherein B is used to search for histogram peak statistics, D
Sorted for final emitter Signals.
Second step：The method of histogram peak statistics is searched for estimate the substantially number of cluster
Obtain after characteristic vector B, the method counted by searching for histogram peak sets the expection class of ISODATA algorithms
Number.
Remember characteristic vector B={ Bi}={ B1,B2,...,BN(i=1,2 ..., N), the dimension of characteristic vector is 2 dimensions.
Demarcation interval is determined by sample size.If per one-dimensional demarcation interval number being q,
In formula,Represent the maximum integer being no more than.
Remember DOAmax, DOAmin, HT max, HT minDOA and H respectively in characteristic vectorTMaximum and minimum value.
Then vector DOA ties up interval width DOAΔWith vectorial HTTie up interval width HTΔRespectively
So far the plane domain where characteristic vector B is divided into Q=q2Individual minizone, each minizone can be expressed as
{bij=(DOAi,HTj)|DOAi∈[DOAm,DOAm+1], HTj∈[HTn,HTn+1], m, n ∈ [1, q] } (8)
In formula, DOAm=DOAmin+(m-1)DOAΔ, HTn=HT min+(m-1)HTΔ。
Then statistics falls within the number of middle vector element between different districts respectively, to the vertical bar height of interval statistics value.Will
Each divides statistical value in good interval and is compared with adjacent interval, if the interval statistical value is more than its all adjacent interval
Statistical value, then remember the interval for peak value interval.Finally the interval number of statistics peak value is peak number S.
Default class number is set to the number of histogram peak i.e.
In formula,The smallest positive integral represented more than.
3rd step：The advance of the lower limit of minimum range sets between the upper limit of the standard deviation of each data distribution and all kinds of centers in class
Put
Characteristic vector D is obtained, the upper limit θ of the standard deviation of each data distribution in class is pre-setSIt is minimum between all kinds of centers
The lower limit θ of distanceD。
First to carrying out attribute normalization per one-dimensional in D：
In formula：xij' for normalization after sample data, xijFor the sample data before normalization,Tieed up for kth
Middle maximum element,Minimum element in being tieed up for kth；
In formula：ujFor attribute normalization sample data x 'ijThe center of jth dimension.
The distribution for continuing to ask for sample data jth dimension is openness：
λjIt is the standardization deviation in sample data after jth dimension specification.
Thus initial value is set：
In formula, S is expected class number；
Then the variance for directly asking sample data itself jth to tie up
Thus initial value is set
4th step：ISODATA algorithms are run according to Adaptive Criterion to be sorted
For the operation of whole algorithm, using adaptive principle, i.e., by setting the standard deviation of each data distribution in class
Upper limit θSThe lower limit θ of minimum range between all kinds of centersDTransformation criterion, reach the purpose of the optimal Clustering Effect of auto-feeding.
The inter- object distance assumed when all sample datas are same class is calculated first
ISODATA algorithms are run after arrange parameter.
After the algorithm clustered every time terminates, the inter- object distance Θ for extracting result is calculated respectivelyS' and between class distance ΘD′。
In formula：C ' is that algorithm runs the classification number produced, CpTo sort out the set of data, viFor the center of the i-th class, xnFor
Belong to CpThe data of class, vjFor with viThe center of different classes.
Only change θ after clustering for the first timeS, set adaptive principle as follows
ISODATA algorithms are run again
And it is as follows to set adaptive criterion：
And the condition for exiting adaptive iteration is：
Δs=| θs′-θs|=0 (21)
ΔD=| θD′-θD|=0 (22)
In formula：ΔS、ΔDFor the difference of the inter- object distance of double operation result and between class distance before and after algorithm.
Exit and sorting is completed after adaptive iteration, and then count separation results.
So far, the whole process knot of the radar emitter signal method for separating of a kind of parameter adaptive setting and adjust automatically
Beam.
The present invention has the advantages that：
1. screening accuracy is high.In the present invention, it is contemplated that relatively accurate and Adaptive Criterion the setting energy of class number setting
Accuracy rate is enough improved, in addition, introducing symmetrical this intrapulse feature of Holder coefficients, the separability of characteristic vector is considerably increased,
So separating effect relative ideal.
Brief description of the drawings
Fig. 1 is the flow chart based on the radar emitter signal method for separating adaptively adjusted；
Fig. 2 is search histogram peak statistics flat distribution map；
Fig. 3 is search histogram peak statistics design sketch；
Fig. 4 is ideal sort figure；
Fig. 5 is algorithm separating effect figure.
Embodiment
The present invention is described in further detail with reference to the accompanying drawings and examples.
Using the method shown in Fig. 1, radar emitter signal that choose 5 kinds of different modulating types and that parameter is closer to
Emulation experiment is carried out, running parameter change type and span are as shown in table 1.
The radar emitter signal characteristic parameter table of table 1
(1) selection of characteristic vector
Characteristic vector pickup is carried out to radar emitter signal, each type of radar produces 500 sample datas.
(2) method of histogram peak statistics is searched for estimate the substantially number of cluster
Determined to divide number according to sample data quantity
Sample data is distributed in plane as shown in Fig. 2 then via search histogram peak statistics, obtaining histogram peak
Figure is as shown in figure 3, peak number is after analysis
S=5
Then preset classification number
(3) lower limit of minimum range is pre-set between the upper limit of the standard deviation of each data distribution and all kinds of centers in class
The distribution for continuing to ask for sample data jth dimension is openness：
Obtain
λ1=0.2330, λ2=0.2248, λ3=0.2596
Thus initial value is set：
Then the variance for directly asking sample data itself jth to tie up
Obtain
σ1=1.7908, σ2=1.9398, σ3=2.6530
(4) ISODATA algorithms are run according to adaptive principle to be classified
1) inter- object distance when all sample datas are same class is calculated
2) basic parameter is set
The minimal data number allowed in each classification：θn=4；
The logarithm of most annexable classes during iteration：L=2
The number of times of the interative computation at most allowed：I=70；
It is expected that class number：C=8；
The upper limit of the standard deviation of each data distribution in class：θs=0.35816；
The lower limit of minimum range between inhomogeneity center：θD=1.124.
2) ISODATA algorithms are run
Obtain calculating inter- object distance and between class distance after first time result
Θs=1067.1
ΘD=2.4872
Change in class and run algorithm again after the upper limit of the standard deviation of each data distribution
Θs=1067.1
ΘD=2.4872
Algorithm is run again after changing parameter
Θs=1067.1
ΘD=2.4872
Now
Δs=| θs′-θs|=0
ΔD=| θD′-θD|=0
Algorithm is exited, separation results are obtained as shown in table 2.Sample data actual distribution is as shown in figure 4, classification is as schemed
Shown in 5, different labels represents different classes in figure.
The simulation result of table 2
Claims (4)
1. a kind of parameter adaptive setting and the radar emitter signal method for separating of adjust automatically, it is characterised in that this method
Step is as follows：
1. from radar emitter signal pulse angle of arrival and symmetrical Holder coefficients constitutive characteristic vector, wherein pulse angle of arrival
And the symmetrical Holder coefficients of signal spectrum and triangular signal constitute the characteristic vector of search histogram peak statistics；Pulse
Angle of arrival and signal spectrum and rectangular signal, the symmetrical Holder coefficients of triangular signal constitute separation characteristic vector；
2. the expection class number determined using the method for searching for histogram peak statistics, it is contemplated that class number is 1.5 times of peak number；
3. the upper limit θ of the standard deviation of each data distribution in the class in ISODATA algorithms is pre-setSThe most narrow spacing between all kinds of centers
From lower limit θD, it is specially
First to carrying out attribute normalization per one-dimensional in sample data：
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In formula：ΔS、ΔDFor the difference of the inter- object distance of double operation result and between class distance before and after algorithm.
2. radar emitter signal method for separating according to claim 1, it is characterised in that the step 1. in signal
Frequency spectrum is specially：The spectrum generated after the progress Fast Fourier Transform (FFT) of the signal received.
3. radar emitter signal method for separating according to claim 1, it is characterised in that the step 1. in triangle
Shape signal and rectangular signal are specially：Choosing the length of triangular signal and rectangular signal should match with the length of signal spectrum.
4. radar emitter signal method for separating according to claim 1, it is characterised in that the step 2. in search
Histogram peak is counted：By a vector matrix XN×MIn each element be depicted in figure with the form of vertical bar come,
If the columns of vector matrix is 1, obtained histogram is exactly an X-Y scheme；Columns is 2, and obtained histogram is exactly one three
Dimension figure, and then histogram is generalized to the situation that vector matrix columns is M (M >=3), one M+1 of obtained histogram
Figure, and then the vertical bar height of interval statistics value can be obtained, each is divided into statistical value in good interval is carried out with adjacent interval
Compare, if the interval statistical value is more than the statistical value of its all adjacent interval, it is a peak value interval to remember the interval；Finally
It is peak number to count the interval number of peak value.
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