CN110390253A - The modulation mode of communication signal recognition methods extracted based on a variety of points of shape spectrum signatures - Google Patents
The modulation mode of communication signal recognition methods extracted based on a variety of points of shape spectrum signatures Download PDFInfo
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Abstract
The present invention relates to cognition wireless electrical domains, and for the automatic signal identification technology for finding a kind of High-efficiency Sustainable, the technological progress for recognizing electronic warfare provides new solution technical solution.For this reason, the technical scheme adopted by the present invention is that obtaining a discrete series of signal data firstly, carrying out sampling processing to the signal received based on the modulation mode of communication signal recognition methods that a variety of points of shape spectrum signatures are extracted；Then, by this sequence be put into m dimension theorem in Euclid space to a point set, by analyzing each point range distribution situation in this point set, obtain correlation integral, the slope of correlation integral curve approximation straightway represents the multi-fractal spectrum signature of signal, different slopes represents different signals, thus distinguishes modulated signal.Present invention is mainly applied to radio signals to identify occasion.
Description
Technical field
The present invention relates to cognition wireless electrical domains, are a kind of novel method for waveform identification.More particularly to one kind based on more
The modulation mode of communication signal recognition methods that kind divides shape spectrum signature to extract.
Background technique
By the identification and analysis processing to signal of communication, next information processing and application can be provided more
Information has very important significance in terms of military surveillance, electronic countermeasure, wireless network secure, machine.
Recognition methods based on feature extraction is generally divided into two stages: first is feature extraction, it will be completed from difference
The task of feature is extracted in signal, and to guarantee that the feature extracted has good differentiation to the signal of different modulating type
Degree；Second is pattern-recognition, and the modulation system of signal is determined its task is to the feature obtained according to the first stage.This method
The blind recognition of signal may be implemented, it is easy to operate, it is easy to accomplish, more modulation signal can be distinguished.It is proposed that it is a kind of for
The method for identifying modulation mode of communication signal, is the envelope feature [1] based on communication signal waveforms.If signal modulation for identification
The amount of type is R, R representation signal envelope variance and signal envelope mean value square ratio.The equation of R can be regarded as signal
The function of carrier-to-noise ratio, it can be used for establishing Classification and Identification system after being found out.Identification system is generally such that count first
The R value for receiving signal is calculated, then according to the modulation type of the range determining signals of R value decline.It is special that this is based on signal envelope
Property Modulation Mode Recognition system, tetra- kinds of FM (PM, FSK), AM, DSB and SSB signals can be realized with reliable separation and known
Not.And this system is suitable for application in real time, because record length required for identifying is very short, just to one section of very short signal
It may be implemented successfully to identify, and it is short to calculate the time.Digital communication is believed with the thought that wavelet analysis is combined with neural network
Number modulation type carries out knowledge method for distinguishing [2].Signal is converted with specific small echo first, then according to obtained small echo
Coefficient carries out feature extraction, recycles probabilistic neural network to identify signal later.It has chosen PNN neural network work
For classifier, this is the important deformation pattern of one kind of radial base (RBF) neural network.This method can to ASK, BPSK,
The signal of tetra- kinds of different modulating types of FSK, OOK is identified, and has good anti-noise ability.Helsinki, Finland science and engineering is big
It learns and just proposed the feature extracting method based on Wigner and Choi-William time-frequency distributions early in 2007[3].Harbin work
The radar waveform recognition methods for proposing various ways fusion is learned by sparetime university, and has made the Wave data of a variety of communications and radar signal
Collection, first pre-processes signal (obtain Choi-Williams distribution, binary system picture, denoising) first, then extracts the system of signal
Feature, the feature of power spectrum, temporal characteristics and picture are counted, CNN classifier and Elman neural network is finally designed, is extracted
22 kinds of features realize waveform recognition[4].This kind of signal recognition method needs first to extract signal characteristic, such as time-frequency distributions feature, height
Rank accumulates measure feature, cyclostationary characteristic, fractal characteristic etc..
Point shape be a quasi-complexity it is very high, without characteristic length, but figure with the self-similarity under definite meaning and
Structure.The slave integer that fractal dimension refers to is expanded to score, and the dimension of negated integer is just referred to as fractal dimension.Multifractal spectra is
It can accurately reflect the geometrical characteristic information of signal waveform, it can scrambling to signal, complexity and global canonical
Property is quantitatively described, and can accurately depict the difference between different modulating type.Can overcome single fractal dimension without
Method accurately describes the information that signal waveform geometrical characteristic is included.Signal of communication is also a kind of random fractal in itself, so
It says it is with fractal characteristic.The method that proposed adoption parting dimension method of the present invention is input to neural network realizes that modulation system is known
Not.
[1] Chan Y T, Gadibois L G, Yansounip. Identification of modulation
type of a signal[C]. IEEEInternational Conerence on Acoustic,Speech and
Signal Processing IEEE1985:838—841.
[2] signal modulate [J] the communication technology of Ge Chun, He Bing, Gao Jiang based on wavelet neural network, 2009,42
(10): 32-34.
[3]J.Lunden and V.Koivunen,"Automatic Radar Waveform Recognition,"
IEEE Journal ofSelected Topics in Signal Processing,vol.1,no.1,pp.124-136,
Jun.2007.
[4]Khan F N,Zhong K,Al-Arashi W H,et al.Modulation Format
Identification in CoherentReceivers Using Deep Machine Learning[J].IEEE
Photonics Technology Letters,2016,28(17):1-1。
Summary of the invention
In order to overcome the deficiencies of the prior art, the present invention is directed to find a kind of automatic signal identification technology of High-efficiency Sustainable,
Technological progress for recognizing electronic warfare provides new solution technical solution.For this reason, the technical scheme adopted by the present invention is that being based on
The modulation mode of communication signal recognition methods that a variety of points of shape spectrum signatures are extracted, firstly, sampling processing is carried out to the signal received,
Obtain a discrete series of signal data；Then, by this sequence be put into m dimension theorem in Euclid space to a point set, lead to
It crosses and analyzes each point range distribution situation in this point set, obtain correlation integral, the slope of correlation integral curve approximation straightway represents
The multi-fractal spectrum signature of signal, different slopes represent different signals, thus distinguish modulated signal.
X is RnSet, { AiIt is one of set X limited δ covering, i=1,2 ..., N, piIt is that the element of set X is fallen in
Set AiProbability, define multi-fractal spectrum signature:
The extracting method of multi-fractal spectrum signature is to extract letter using based on the method for phase space reconfiguration and correlation integral
Number multi-fractal spectrum signature: specific step is as follows:
Firstly, carrying out sampling processing to the signal received, signal is reduced to a long sequence, sampling is regarded as to sample
Between be divided into distance in order take out Chief Signal Boatswain sequence in value, obtain a discrete series { x of signal datak, k=1,2,
3,...,N}；
Then obtained sample sequence is put into a m dimension theorem in Euclid space, if this m dimension theorem in Euclid space is Rm, this
Sample has just obtained point set vector set J (m) in other words, and element therein is denoted as:
xn(m, τ)=(xn,xn+τ,…xn+ (m-1) τ), n=1,2 ..., Nm (2)
τ=k Δ t element represents fixed time interval in above formula, and Δ t table is the interval between neighbouring sample, k round numbers, N
Count maximum Nm=N- (m-1) τ；
Next the distribution situation of distance pair in the various points in point set J (m) is analyzed again: first from NmIt is arbitrarily selected in a point
One point XiIt is as a reference point, calculate remaining (Nm- 1) a point is expressed as r to the distance of Xiij:
According to two above formula, in conjunction with the following formula for calculating correlation integral:
The formula of q rank correlation integral is obtained after substitution:
After obtaining q rank correlation integral, select the value range of r, acquire when q takes different value multistage correlation integral C (q,
R), then lnC (q, r)~lnr relational graph is made, the slope of curve approximation straightway is exactly multi-fractal spectroscopic eigenvalue in figure
Dq。
The features of the present invention and beneficial effect are:
It is a novel method for waveform identification, the multifractal spectra for extracting communication modulation signal using correlation integral is special
Sign realizes the identification of different modulating mode.Signal processing flow is as shown in Figure 1.
Tri- kinds of digital modulation signals of BFSK, BPSK and 4ASK are generated first, and wherein symbol signal is randomly generated, carrier frequency
fcIt is 20kHz.To these three signals do 4.2 described in processing, taking 2-9 in the order q of correlation integral, these are different
When value, lnC (q, r)~lnr relational graph is drawn out in MATLAB.When q=2, the digital modulation signals waveform of generation and
LnC (2, r)~lnr relational graph depicts the 2 rank correlation integral curves of three kinds of signals as shown in Fig. 2, wherein in (b) figure,
Wherein the line of three colors respectively represents three kinds of different digital modulation signals, and corresponding relationship is as follows: blue-BFSK signal,
Green-BPSK signal, red-4ASK signal.
When q=7, digital modulation signals waveform and lnC (7, r)~lnr relational graph of generation are as follows.(b) figure in Fig. 3
In, the 7 rank correlation integral curves of three kinds of signals are depicted, wherein the corresponding relationship of the line representation signal of three colors is as follows:
Blue-BFSK signal, green-BPSK signal, red-4ASK signal.
Simulation result is analyzed it is found that when q takes 2-9, the q rank correlation integral curve of BFSK and bpsk signal, which compares always, to be connect
Closely, their near linear section slope of a curve, that is, our the multi-fractal spectroscopic eigenvalue Dq to be looked for are also more close；And
The entire correlation integral curve and near linear slope over 10 multi-fractal spectroscopic eigenvalue Dq of 4ASK signal all with upper two kinds of signals
Difference is larger.This phenomenon can be explained theoretically in fact, because BFSK and bpsk signal modulation waveform are inherently very
It is similar, visually it is difficult to distinguish the two from its waveform patterns, and 4ASK will have significant difference with them, see waveform
Pattern is easy for identify 4ASK signal.But fortunately have difference to a certain extent between three, it can basis
This feature is completed the identification to these three digital modulation signals and is distinguished.
Detailed description of the invention:
Multifractal spectra feature extracting method flow chart of the Fig. 1 based on phase space reconfiguration and correlation integral
Simulation result diagram when Fig. 2 q=2.In figure:
(a) digital modulation signals waveform；
(b) lnC (2, r)~lnr.
Simulation result diagram when Fig. 3 q=7.In figure:
(a) digital modulation signals waveform.
(b) lnC (7, r)~lnr.
Specific embodiment
The modulation system of signal of communication is its more important technical characteristic, identifies signal of communication using which kind of side
Formula, which is modulated to handle for the further analysis of signal, provides foundation.In non-cooperating communication, signal modulation pattern-recognition
It is the key that one step of signal analysis and processing as the intermediate steps that signal detection and signal demodulate.Increasingly with the communication technology
Development, the type of signal of communication is more and more various, and signal environment also becomes increasingly complex.It is a kind of high it is an object of the invention to find
Sustainable automatic signal identification technology is imitated, it is most important for the technological progress for recognizing electronic warfare.
The present invention proposes a kind of modulation mode of communication signal recognition methods based on a variety of points of shapes spectrum.In signal modulation mode
In the task of identification, it may be implemented effectively to identify using multi-fractal spectrum signature.Firstly, being carried out at sampling to the signal received
Reason, obtains a discrete series of signal data.Then, by this sequence be put into m dimension theorem in Euclid space to a point set.
By analyzing each point range distribution situation in this point set, correlation integral is obtained.The slope generation of correlation integral curve approximation straightway
Table the multi-fractal spectrum signature of signal, different slopes represent different signals.Modulation can be effectively distinguished using this feature
Signal.
(1) multifractal spectra
Fractal characteristic is the important feature that can reflect the geometrical characteristic of signal waveform variation, and fractal dimension divides shape again
Important feature, so, the extraction to signal fractal dimension is be unable to do without to the analysis of signal fractal characteristic.The concept of point shape is exactly one
Quasi-complexity is very high, does not have characteristic length, but figure and structure with the self-similarity under definite meaning.Signal of communication is at this
It is also a kind of random fractal in matter, so saying it is with fractal characteristic.Fractal dimension is used to most description signals
Fractal characteristic physical quantity, it can effectively describe this irregular geometrical body complexity.Due to different modulating mode
The signal of generation has larger difference on wave character, it is possible to using the fractal characteristic of signal as the foundation of identification signal.
In theorem in Euclid space, the meaning of geometric object dimension is exactly the number of independent variable needed for describing a point.It is expanded to a point shape
Theory, dimension also accordingly change, and are expanded to score from integer, the dimension of negated integer is just referred to as fractal dimension.And it is more
Multifractal spectrum is most important one kind in fractal characteristic, it can accurately reflect the geometrical characteristic information of signal waveform, can
It is quantitatively described with scrambling, complexity and the Global Regularity to signal, different modulating type can accurately be depicted
Between difference.Although in essence, the box counting dimension of signal and information dimension are all the special cases of multifractal spectra, multiple point
The appearance of this feature of type spectrum can also overcome single fractal dimension to be not enough to accurately describe signal waveform geometrical characteristic to be wrapped
The defect of the information contained.
There are many kinds of fractal dimensions, has the box counting dimension by extracting signal come to ASK, FSK, BPSK, QPSK and 16PSK five
The method that kind signal is classified, there is while being extracted the box counting dimension of signal and information dimension is used to be modulated identification[23].But
It is that there are many defects for single fractal dimension, it is not enough to Fractal object made of exact picture develops by cumbersome procedure,
Also lack the description to Fractal object local characteristics of scale.Pith one of of the multifractal spectra as multi-fractal features,
A variety of fractal dimension information can be included, and compensate for the defect and deficiency of single fractal dimension, can accurately be reflected
The geometrical characteristic information of signal waveform entirety, and then there is the ability for distinguishing unlike signal modulation type.
If X is RnSet, { Ai(i=1,2 ..., N) be one of X limited δ covering, piIt is that the element of set X is fallen in
Set AiProbability.Define multi-fractal spectrum signature:
(2) extracting method of multi-fractal spectrum signature
Here using the multi-fractal spectrum signature for extracting signal based on the method for phase space reconfiguration and correlation integral.
Firstly, carrying out sampling processing to the signal received.Due to the discrete nature of digital modulation signals itself, signal
It can simplify as a long sequence, sampling is considered as being that distance takes out value in Chief Signal Boatswain sequence in order with the sampling interval,
Pay attention to having to follow sampling thheorem simultaneously, i.e. sample frequency is greater than 2 times of highest frequency in signal, is just able to maintain signal in this way
The integrality of middle information.After handling in this way, so that it may obtain a discrete series { x of signal datak, k=1,2,3 ...,
N}。
Then obtained sample sequence is put into a m dimension theorem in Euclid space, if this m dimension theorem in Euclid space is Rm, this
Sample has just obtained a point set, it is understood that is a vector set J (m), element therein is denoted as:
xn(m, τ)=(xn,xn+τ,…xn+ (m-1) τ), n=1,2 ..., Nm (2)
τ=k Δ t element represents fixed time interval in above formula, and Δ t table is the interval between neighbouring sample, k round numbers.N
Count maximum Nm=N- (m-1) τ.
Next the distribution situation of distance pair in the various points in point set J (m) is analyzed again.First from NmIt is arbitrarily selected in a point
One point XiIt is as a reference point, calculate remaining (Nm- 1) a point is expressed as r to the distance of Xiij:
According to two above formula, in conjunction with the following formula for calculating correlation integral:
The formula of available q rank correlation integral after substitution:
After obtaining q rank correlation integral, the value range of suitable r is selected, acquires multistage correlation integral when q takes different value
C (q, r), then make lnC (q, r)~lnr relational graph, in figure the slope of curve approximation straightway be exactly we require it is multiple
Divide shape spectroscopic eigenvalue Dq。
Specific steps of the present invention are summarized as follows:
(1) sampling processing is carried out to the signal received.
(2) distribution situation of the various interior point distances pair in point set is analyzed.
(3) q rank correlation integral is calculated.
(4) lnC (q, r)~lnr relational graph is made；
(5) according to the slope of the curve approximation straightway of relational graph, multi-fractal spectroscopic eigenvalue D is obtainedq。
(6) modulation system is judged according to the plant of multi-fractal spectrum signature.
Claims (3)
1. a kind of modulation mode of communication signal recognition methods extracted based on a variety of points of shape spectrum signatures, characterized in that firstly, docking
The signal received carries out sampling processing, obtains a discrete series of signal data；Then, this sequence is put into a m dimension
Theorem in Euclid space obtains correlation integral, correlation integral is bent by analyzing each point range distribution situation in this point set to a point set
The slope of line near linear section represents the multi-fractal spectrum signature of signal, and different slopes represents different signals, thus area
Divide modulated signal.
2. the modulation mode of communication signal recognition methods extracted as described in claim 1 based on a variety of points of shape spectrum signatures, special
Sign is that X is RnSet, { AiIt is one of set X limited δ covering, i=1,2 ..., N, piIt is that the element of set X falls in collection
Close AiProbability, define multi-fractal spectrum signature:
3. the modulation mode of communication signal recognition methods extracted as described in claim 1 based on a variety of points of shape spectrum signatures, special
Sign is that the extracting method of multi-fractal spectrum signature is to extract signal using based on the method for phase space reconfiguration and correlation integral
Multi-fractal spectrum signature: specific step is as follows:
Firstly, carrying out sampling processing to the signal received, signal is reduced to a long sequence, sampling was regarded as with the sampling interval
It takes out the value in Chief Signal Boatswain sequence in order for distance, obtains a discrete series { x of signal datak, k=1,2,3 ...,
N}；
Then obtained sample sequence is put into a m dimension theorem in Euclid space, if this m dimension theorem in Euclid space is Rm, thus
To a point set, a vector set J (m), element therein are denoted as in other words:
xn(m, τ)=(xn,xn+τ,…xn+ (m-1) τ), n=1,2 ..., Nm (2)
τ=k Δ t element represents fixed time interval in above formula, and Δ t table is the interval between neighbouring sample, k round numbers, the meter of N
Number maximum value Nm=N- (m-1) τ；
Next the distribution situation of distance pair in the various points in point set J (m) is analyzed again: first from NmOne is arbitrarily selected in a point
Point XiIt is as a reference point, calculate remaining (Nm- 1) a point is expressed as r to the distance of Xiij:
According to two above formula, in conjunction with the following formula for calculating correlation integral:
The formula of q rank correlation integral is obtained after substitution:
After obtaining q rank correlation integral, the value range of r is selected, acquires the multistage correlation integral C (q, r) when q takes different value, then
LnC (q, r)~lnr relational graph is made, the slope of curve approximation straightway is exactly multi-fractal spectroscopic eigenvalue D in figureq。
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