CN108108712A - A kind of Emitter Fingerprint feature extracting method based on variance dimension - Google Patents
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Abstract
A kind of Emitter Fingerprint feature extracting method based on variance dimension, the present invention relates to Emitter Fingerprint feature extracting methods.The problem of being difficult to meet specific emitter identification validity and reliability demand, cause specific emitter identification accuracy low the purpose of the present invention is to solve traditional characteristic parameter.A kind of Emitter Fingerprint feature extracting method detailed process based on variance dimension is：First, segment processing is carried out to the one-dimensional emitter Signals received, obtains one-dimensional emitter Signals section；2nd, the one-dimensional emitter Signals section obtained to one carries out variance Dimension Characteristics extraction, obtains variance Dimension Characteristics vector.The present invention is used for specific emitter identification field.
Description
Technical field
The present invention relates to Emitter Fingerprint feature extracting methods.
Background technology
Specific emitter identification, also known as " Emitter Fingerprint identification " or " Specific Emitter Identification ", it is main using passive
The emitter Signals of reception analyze its fine feature parameter, and the difference of same type radiation source is told using Finger print characteristic abstract
Individual.Due to the difference of radiation source physical component so that each radiation source waveform suffers from specific characteristic, is known as fingerprint characteristic,
And the key technology of specific emitter identification is the selection and extraction of fingerprint characteristic.Specific emitter identification is dual-use
Aspect has many applications.Military aspect carries out individual identification mainly for Radar emitter；Civilian aspect essentially consists in radio frequency
Identification, network node is recognized each other, information is collected evidence and the fields such as communication security.In addition, fractal dimension is as the important interior of fractal theory
Hold, compared and be widely applied to the fields such as seismic wave detection, image procossing and material science.Radar emitter with noise
Signal can regard a kind of time series with certain self-similarity nature as, it be able to effectively be portrayed with point shape.
Existing radiation source Feature Extraction Technology is mainly special using the temporal signatures of emitter Signals, frequency domain character, statistics
The traditional parameters such as sign, with increasingly complicated and hardware technique the raising of radiation source waveform, traditional characteristic parameter is difficult to meet
Specific emitter identification validity and reliability demand causes specific emitter identification accuracy low.
The content of the invention
The purpose of the present invention is to solve traditional characteristic parameters to be difficult to meet specific emitter identification validity and reliable
Property demand, the problem of causing specific emitter identification accuracy low, and propose that a kind of Emitter Fingerprint based on variance dimension is special
Levy extracting method.
A kind of Emitter Fingerprint feature extracting method detailed process based on variance dimension is：
Step 1: carrying out segment processing to the one-dimensional emitter Signals received, one-dimensional emitter Signals section S is obtained1,
S2,…Si…,Sn；
SiFor i-th of one-dimensional emitter Signals section, 1≤i≤n, n values are positive integer；
Process is：
The one-dimensional emitter Signals S that step 1 one, input receive, according to the total of the one-dimensional emitter Signals S received
Long and feature extraction segments demand, setting signal segment length w and segments n, each signal segment length are identical；
Step 1 two determines sliding window step delta, and sliding window step delta is obtained by formula (1)：
WhereinRepresent positive bracket function；
Step 1 three, call function G=enframe (S, w, Δ) obtain return value G, the G form of function enframe such as
Under：
G is that a line number is n, and columns is the matrix of w；
Obtain one-dimensional emitter Signals section S1,S2,…Si…,Sn；
Step 2: carrying out variance Dimension Characteristics extraction to the one-dimensional emitter Signals section that step 1 obtains, variance dimension is obtained
Number feature vector.
Beneficial effects of the present invention are：
The present invention starts with from Fractal Domain, by the use of variance dimension as the fingerprint characteristic of emitter Signals, utilizes signal subsection
Processing increases intrinsic dimensionality, completes the extraction of Emitter Fingerprint feature, meets specific emitter identification validity and reliability need
It asks, improves specific emitter identification accuracy.Emulation experiment the results show that in signal-to-noise ratio under conditions of 13dB, to utilize
The variance Dimension Characteristics of extraction are classified, and the accuracy of specific emitter identification is more than 95%.
Specific emitter identification refers to distinguish very high same type of of similarity using fingerprint (individual exclusive) feature
Different radiation source individuals.Specific emitter identification is an important development direction in Radar recognition field, although type identification
Technology is gradually ripe, but the individual information included in type identification is less, and has radiated source information needed for engineer application
Type information is not limited to, often also needs to radiation source individual information.Individual identification precisely in order to obtain radiation source individual information, can
To say that this is the Studies on Emitters ID more higher level than type identification.With military application examples, Radar emitter individual identification can
To distinguish the Different Individual of same kind radar, this is vital for commanding and decision-making, while is also that type identification is difficult
With what is reached.And the existing research to specific emitter identification often utilizes the tradition such as temporal signatures, frequency domain character, statistical nature
Parameter, the present invention improve specific emitter identification validity and reliability using variance Dimension Characteristics
The raising of specific emitter identification validity and reliability, in the equal important in inhibiting of dual-use aspect.Army
Individual identification is carried out mainly for Radar emitter with aspect, helps to improve electronic reconnaissance ability, it is excellent to improve our electronic warfare
Gesture captures electronic warfare battlefield commanding elevation；Civilian aspect contributes to radio frequency identification, network node to recognize each other, information is collected evidence and communication security
Technology being improved and improving.
Description of the drawings
Fig. 1 realizes the general frame for one method of the specific embodiment of the invention；
Fig. 2 is one signal subsection flow chart of the specific embodiment of the invention；
Fig. 3 extracts flow chart for variance Dimension Characteristics of the present invention；
Fig. 4 is the simulation experiment result figure the present invention is based on variance dimension.
Specific embodiment
Specific embodiment one：Illustrate present embodiment with reference to Fig. 1, Fig. 2, Fig. 3, one kind of present embodiment is based on variance
The Emitter Fingerprint feature extracting method detailed process of dimension is：
Step 1: carrying out segment processing to the one-dimensional emitter Signals received, one-dimensional emitter Signals section S is obtained1,
S2,…Si…,Sn, to show the fine feature of signal and increase intrinsic dimensionality；SiFor i-th of one-dimensional emitter Signals section, 1≤i
≤ n, n value are positive integer；
Process is：
In order to describe emitter Signals from subtleer angle, and also between better discriminating between different emitter Signals
Difference, emitter Signals are first divided into several signal segments.The concrete methods of realizing of signal subsection is to utilize to slide window function, again
Framing function (enframe) is, signal adding window and the framing being usually used in Speech processing；
The one-dimensional emitter Signals S that step 1 one, input receive, according to the total of the one-dimensional emitter Signals S received
Long and feature extraction demand (segments) setting signal segment length (slip window width) w and segments n, each signal segment length phase
Together；
Step 1 two determines sliding window step delta, and sliding window step delta is obtained by formula (1)：
WhereinRepresent positive bracket function (real number x is mapped to the smallest positive integral more than or equal to x)；
Step 1 three, call function G=enframe (S, w, Δ) obtain return value G, the G form of function enframe such as
Under：
G is that a line number is n, and columns is the matrix of w, therefore the calculating of fractal dimension must carry out line by line.
Obtain one-dimensional emitter Signals section S1,S2,…Si…,Sn；
Step 2: carrying out variance Dimension Characteristics extraction to the one-dimensional emitter Signals section that step 1 obtains, variance dimension is obtained
Number feature vector；
Using variance Dimension Characteristics vector as the input of grader, categorized device is exporting the identification of one-dimensional emitter Signals just
True rate.
Radiation source individual can be distinguished by grader, increases feature samples, you can obtain discrimination.
The grader is support vector machines (SVM, support vector machine) or backpropagation (BP, back
Propagation) neural network classifier.
Specific embodiment two：The present embodiment is different from the first embodiment in that：To step in the step 2
One obtained one-dimensional emitter Signals section carries out variance Dimension Characteristics extraction, obtains variance Dimension Characteristics vector；Detailed process is：
Variance dimension is a kind of fractal dimension based on Hurst indexes, can be used to the Fractal Properties of analysis time sequence.Side
Poor dimension DσIt is shown below with the relation of Hurst indexes H：
Dσ=E+1-H
Wherein E represents Euclid dimensions, for One-dimension Time Series, takes E=1；
The classical calculation of Hurst indexes is rescaled range method.R/S analytic approach is initially by Britain hydrologist
The method that H.E.Hurst is proposed when studying the storage capability of Nile reservoir.R/S analytic approach calculates simply, only needs average, pole
Three difference, mean square deviation amounts, have unique advantage, in stock in the fractal characteristic of analysis time sequence and long-term memory process
Have preferable application in the analysis in market, and the present invention uses it for the extraction of radiation source " fingerprint " feature expansionaryly.
The present invention using R/S analysis methods calculate variance dimension basic step be：
Step 2 one, as i=1, by SiThe isometric subinterval that β initial length is A is divided into, with { dtRepresent sub-district
Interior sequence；β values are positive integer；
Step 2 two, calculating add up the very poor of deviation in each subinterval；
For each subinterval, if：
Wherein, EqFor d in q-th of subintervalpAverage value, q=1,2 ... β, dpFor { dtIn p-th of element, Zt,qFor
The accumulative deviation in q subinterval；
The very poor of all accumulative deviation values is obtained by formula (4)：
Rq=max (Zt,q)-min(Zt,q) (4)
Step 2 three calculates each subinterval standard deviation：
If SqRepresent sequence { d in q-th of subintervaltStandard deviation, then：
Sequence { dtRepresent sequence in each subinterval；
Step 2 four calculates each subinterval rescaled range, and averages；
The length for making subinterval is A changing with 2 for the exponential law at bottom as shown in formula (6),
Each subinterval rescaled range is calculated, and is averaged：
Step 2 one is repeated to step 2 four, untilPerform step 2 five；
Step 2 five, take the logarithm to the value of corresponding rescaled range R/S and length A after linear fit (it is corresponding be
Cycle-index is identical), obtain Hurst indexes H(i)；
Step 2 six utilizes Hurst indexes H(i)Calculate variance Dimension Characteristics vector
It is provided by formula (8)：
Wherein E represents Euclid dimensions；
Step 2 seven judges whether i is equal to n, if so,Terminate；If not,
Step 2 one is performed, until i is equal to n.
Other steps and parameter are same as the specific embodiment one.
Specific embodiment three：The present embodiment is different from the first and the second embodiment in that：In the step 2 one
As i=1, by SiIt is divided into the isometric subinterval that β initial length is A；β values are positive integer；Process is：
The one-dimensional emitter Signals section S that signal segment length for interception is wi, by one-dimensional emitter Signals section SiIt is divided into β
A initial length is the isometric subinterval of A, with sequence { dtRepresent sequence in each subinterval.
Other steps and parameter are the same as one or two specific embodiments.
Specific embodiment four：Unlike one of present embodiment and specific embodiment one to three：The step 2
Linear fit (corresponding is that cycle-index is identical) after taking the logarithm in five to the value of corresponding rescaled range R/S and length A,
Obtain Hurst indexes H(i)；Process is：
Hurst establishes following relation by prolonged data preparation and practice summary：
Wherein c be constant, H(i)For Hurst indexes；Both members are taken the logarithm simultaneously：
Log (R/S)=H(i)log(A)+log(c) (10)
Found out by formula (10), for dividing for shape (self similarity) sequence, logA and log (R/S) is linear relationship, so as to profit
With least square method linear regression, the absolute value of fitting a straight line slope is required one-dimensional emitter Signals section SiHurst indexes H(i)。
Other steps and parameter are identical with one of specific embodiment one to three.
Specific embodiment five：Unlike one of present embodiment and specific embodiment one to four：The Euclid
Dimension E values are 1.
Other steps and parameter are identical with one of specific embodiment one to four.
Beneficial effects of the present invention are verified using following embodiment：
Embodiment one：Variance dimension emulation experiment compared with traditional characteristic performance parameters
The present embodiment compares variance Dimension Characteristics parameter and traditional characteristic performance parameters, mainly from discrimination angle point
Analysis.Specifically follow the steps below：
Step 1：Emitter Signals under the conditions of the multigroup different signal-to-noise ratio of emulation generation；
Step 2：Using it is according to the present invention based on the Emitter Fingerprint feature extracting method of variance dimension to step 1
The emitter Signals of generation carry out feature extraction, and obtaining feature vector, (segments n is 10, that is, the feature vector dimension generated is
10)；
Step 3：Using the feature vector of step 2, categorized device calculates discrimination；
Step 4：The discrimination that step 3 is obtained is compared with the discrimination obtained using traditional characteristic parameter, and analysis is real
Test result.
The simulation experiment result is as shown in figure 4, show the effect and and general characteristics of variance Dimension Characteristics identification in figure
Parameter recognition effect[1](Bihl T J,Bauer K W,Temple M A.Feature Selection for RF
Fingerprinting With Multiple Discriminant Analysis and Using ZigBee Device
Emissions[J].IEEE Transactions on Information Forensics and Security,2016,11
(8):Comparison 1862-1874.).It can be seen that the discrimination curve of variance dimension is under general characteristics discrimination baseline,
This is because general characteristics discrimination baseline is the data in the case where dimension is 729, but this needs to consume a large amount of calculating costs.Therefore
Under conditions of considering recognition performance and calculating cost, the analysis of general characteristics performance often first reduces the dimension of feature vector, dimension
Number 50 can be used as a rational selection.
The experimental results showed that：Under the conditions of all signal-to-noise ratio set by emulation experiment, the variance dimension that dimension is 10 is special
Sign all achieves recognition performance more better than the general characteristics that dimension is 50；And under conditions of sample rate permission, pass through increase
Feature vector dimension can further improve recognition performance.On the whole, with the increase of signal-to-noise ratio, based on variance Dimension Characteristics
Discrimination gradually increase, and be maintained on general characteristics parameter discrimination.When signal-to-noise ratio is more than 13dB, tieed up based on variance
Several recognition correct rates is more than 95%.
The present invention can also have other various embodiments, without deviating from the spirit and substance of the present invention, this field
Technical staff makes various corresponding changes and deformation in accordance with the present invention, but these corresponding changes and deformation should all belong to
The protection domain of appended claims of the invention.
Claims (5)
1. a kind of Emitter Fingerprint feature extracting method based on variance dimension, it is characterised in that：The method detailed process is：
Step 1: carrying out segment processing to the one-dimensional emitter Signals received, one-dimensional emitter Signals section S is obtained1,S2,…
Si…,Sn；
SiFor i-th of one-dimensional emitter Signals section, 1≤i≤n, n values are positive integer；
Process is：
Step 1 one, the one-dimensional emitter Signals S that receives of input, according to the overall length of the one-dimensional emitter Signals S received and
Feature extraction segments demand, setting signal segment length w and segments n, each signal segment length are identical；
Step 1 two determines sliding window step delta, and sliding window step delta is obtained by formula (1)：
WhereinRepresent positive bracket function；
Step 1 three, call function G=enframe (S, w, Δ), return value G, the G form for obtaining function enframe are as follows：
G is that a line number is n, and columns is the matrix of w；
Obtain one-dimensional emitter Signals section S1,S2,…Si…,Sn；
Step 2: carrying out variance Dimension Characteristics extraction to the one-dimensional emitter Signals section that step 1 obtains, variance dimension spy is obtained
Sign vector.
2. a kind of Emitter Fingerprint feature extracting method based on variance dimension according to claim 1, it is characterised in that：Institute
It states in step 2 and variance Dimension Characteristics extraction is carried out to the one-dimensional emitter Signals section that step 1 obtains, obtain variance Dimension Characteristics
Vector；Detailed process is：
Step 2 one, as i=1, by SiThe isometric subinterval that β initial length is A is divided into, with { dtRepresent in subinterval
Sequence；β values are positive integer；
Step 2 two, calculating add up the very poor of deviation in each subinterval；
For each subinterval, if：
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The very poor of all accumulative deviation values is obtained by formula (4)：
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Step 2 three calculates each subinterval standard deviation：
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Step 2 five, take the logarithm to the value of corresponding rescaled range R/S and length A after linear fit, obtain Hurst indexes H(i)；
Step 2 six utilizes Hurst indexes H(i)Calculate variance Dimension Characteristics vector
It is provided by formula (8)：
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Wherein E represents Euclid dimensions；
Step 2 seven judges whether i is equal to n, if so,Terminate；If not, it performs
Step 2 one, until i is equal to n.
3. a kind of Emitter Fingerprint feature extracting method based on variance dimension according to claim 2, it is characterised in that：Institute
It states in step 2 one as i=1, by SiIt is divided into the isometric subinterval that β initial length is A；β values are positive integer；Process
For：
The one-dimensional emitter Signals section S that signal segment length for interception is wi, by one-dimensional emitter Signals section SiIt is divided at the beginning of β
Beginning length is the isometric subinterval of A, with sequence { dtRepresent sequence in each subinterval.
4. a kind of Emitter Fingerprint feature extracting method based on variance dimension according to claim 3, it is characterised in that：Institute
Linear fit after taking the logarithm in step 2 five to the value of corresponding rescaled range R/S and length A is stated, obtains Hurst indexes H(i)；Process is：
Establish following relation：
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Wherein c be constant, H(i)For Hurst indexes；Both members are taken the logarithm simultaneously：
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Found out by formula (10), log A and log (R/S) is linear relationship, and so as to utilize least square method linear regression, fitting is straight
The absolute value of line slope is required one-dimensional emitter Signals section SiHurst indexes H(i)。
5. a kind of Emitter Fingerprint feature extracting method based on variance dimension according to claim 4, it is characterised in that：Institute
Euclid dimension E values are stated as 1.
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| CN109271855A (en) * | 2018-08-09 | 2019-01-25 | 深圳市永达电子信息股份有限公司 | A kind of feature extracting method of industry control signal |
| CN110147848A (en) * | 2019-05-24 | 2019-08-20 | 哈尔滨工业大学 | A kind of radiation source personal feature Enhancement Method based on time-variable filtering theory |
| CN111337975A (en) * | 2020-03-24 | 2020-06-26 | 吉林大学 | Automatic microseism event identification method based on variance fractal dimension |
| CN111695444A (en) * | 2020-05-21 | 2020-09-22 | 哈尔滨工业大学 | Radiation source individual feature extraction method based on wave atomic transformation |
| CN113343897A (en) * | 2021-06-25 | 2021-09-03 | 中国电子科技集团公司第二十九研究所 | Method for accelerating signal processing based on slope change of radiation signal |
| CN114139579A (en) * | 2021-11-23 | 2022-03-04 | 吉林大学 | Deep learning microseism event detection method based on variance fractal |
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