CN107770108A - A kind of combined modulation recognition methods of K mean clusters and classification training SVM classifier - Google Patents
A kind of combined modulation recognition methods of K mean clusters and classification training SVM classifier Download PDFInfo
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Abstract
The invention discloses the combined modulation recognition methods of a kind of K mean clusters and classification training SVM classifier, it is characterised in that comprises the following steps：Step S1：Unknown signaling is pre-processed, obtains the data set X of the in-phase component comprising signal and quadrature component；Step S2：Cluster computing is carried out to the sample point of the data set X, obtains subordinated-degree matrix of each sample point to cluster centre；Step S3：The subordinated-degree matrix is handled with Validity Function, obtains some characteristic parameters for distinguishing different modulating mode；Step S4：Using the combination of some characteristic parameters as the input of SVM classifier, the SVM classifier is trained；Step S5：The unknown signaling is identified using the SVM classifier after training, the modulation type of the unknown signaling is obtained in the output of SVM classifier.The problem of computation complexity is high when identifying multi-class problem is the method overcome, convergence rate significantly improves, and improves the efficiency of Modulation Signals Recognition.
Description
Technical field
The invention belongs to signal modulate technical field, and in particular to SVM points of a kind of K- mean clusters and classification training
The combined modulation recognition methods of class device.
Background technology
It is military with civilian wireless communication field that Modulation Identification is widely used in signal monitoring, inquiry, disturbance ecology etc., is to recognize
Know the basis of the area researches such as radio, frequency spectrum perception.Therefore it is how continuous in terms of the extraction of characteristic value and classifier algorithm
Innovation, turn into one of important method of research Modulation Identification.However, existing identifying multi-class problem based on SVMs
When, its computation complexity is high, and when snr of received signal is relatively low, signal modulate rate is low, therefore, how further to carry
The efficiency of high RST Modulation Identification turns into the problem of urgent need to resolve.
The content of the invention
In order to solve the above problems, it is an object of the invention to provide a kind of K- mean clusters and classification to train SVM classifier
Combined modulation recognition methods, this method have higher recognition efficiency.
To achieve the above object, the present invention is achieved by following technical scheme：
The combined modulation recognition methods of a kind of K- mean clusters of the present invention and classification training SVM classifier, including
Following steps：
Step S1：Unknown signaling is pre-processed, obtains the data set of the in-phase component comprising signal and quadrature component
X；
Step S2：Cluster computing is carried out to the sample point of the data set X, obtains each sample point to cluster centre
Subordinated-degree matrix；
Step S3：The subordinated-degree matrix is handled with Validity Function, obtains being used to distinguish different modulating mode
Some characteristic parameters；
Step S4：Using the combination of some characteristic parameters as the input of SVM classifier, the SVM classifier is entered
Row training；
Step S5：The unknown signaling is identified using the SVM classifier after training, in exporting for SVM classifier
To the modulation type of the unknown signaling.
Further, in step sl, unknown signaling is pre-processed specially：Down coversion, low pass are carried out to carrier wave
Filtering and sample process.
Further, the data set X={ x1,x2,…xN, wherein, N is the number of element in data set, i.e., unknown letter
Number sample point symbolic number.
Further, in step s 2, clustering algorithm is specifically calculated using K- mean clusters, and its classification matrix is V(0), by its
Table
Mathematical programming problem is shown as, object function is：
Constraints is：K is cluster centre number, 1 ＜ K ＜ N.
Further, in step s 2, modulated signal corresponding to different cluster centre numbers is iterated, detailed process
Including：
Step S21：Provide iteration criterion epsilon>0, and initialize classification matrix V(0), n=0；
Step S22：Calculate renewal Iterative Matrix U(n),
Step S23：Calculate cluster centre matrix V(n+1),
Step S24：With matrix norm | | | | compare V(n+1)With V(n)If | | V (n+1)-V(n) | |≤ε is set up, then is stopped
Iteration, otherwise, make K=K+1, repeat step step S22 to step S24；In iterative process, optimization object functionObtain subordinated-degree matrix.
Further, step S3 detailed process is：Have to the cluster result obtained during different cluster centre number K values
The analysis of effect property, whether reasonable, Validity Function value is obtained, as distinguishing difference if judging received signal points being divided into K classes
Some characteristic parameters of modulation system.
Further, in step s 4, SVM classifier is trained using the algorithm of classification.
Further, unknown modulated signal is：2PSK, 4PSK, 8PSK, 16QAM, 32QAM and 64QAM；Corresponding modulation
Exponent number is respectively：1,2,3,4,5 and 6；Take cluster centre number K={ 2,4,8,16,32,64 }
Compared with prior art, the beneficial effects of the invention are as follows：
The present invention optimizes processing to the characteristic value of unknown signaling extraction due to using clustering algorithm, while with K averages
The combined modulation recognizer of cluster and classification training SVM, overcomes the problem of computation complexity is high when identifying multi-class problem,
Convergence rate significantly improves, and improves the efficiency of Modulation Signals Recognition.
Brief description of the drawings
The embodiment of the present invention is described in further detail below in conjunction with the accompanying drawings, wherein：
Fig. 1 is the combined modulation recognition methods of a kind of K- mean clusters of the present invention and classification training SVM classifier
Flow chart；
Fig. 2 is the characteristic ginseng value of six kinds of modulation systems of the present invention under different cluster centre numbers；
Fig. 3 is the classification training schematic diagram of SVM classifier of the present invention；
Fig. 4 is the computational complexity emulation comparison schematic diagram of the present invention；
Fig. 5 is the Modulation Identification rate analogous diagram of the present invention when individually using K- means clustering algorithms；
Fig. 6 be it is of the present invention based on conventional BP algorithm SVM classifier Modulation Identification method when Modulation Identification rate emulation
Figure；
Fig. 7 is that the Modulation Identification rate of the present invention using when K mean cluster and SVM combined modulation recognizers emulates
Figure.
Embodiment
The preferred embodiments of the present invention are illustrated below in conjunction with accompanying drawing, it will be appreciated that described herein preferred real
Apply example to be merely to illustrate and explain the present invention, be not intended to limit the present invention.
The present invention provides the combined modulation recognition methods of a kind of K- mean clusters and classification training SVM classifier, for reality
The Modulation Identification of existing signal, the SVM classifier in text refer to supporting vector grader.The present embodiment is with based on planisphere modulation methods
Exemplified by the MPSK/MQAM modulated signals of formula, the course of work of this method is introduced, as shown in figure 1, comprising the following steps：
Step S1：Unknown signaling is pre-processed, obtains the data set of the in-phase component comprising signal and quadrature component
X.Unknown signaling is pre-processed mainly carrier wave is carried out the Signal Pretreatment such as down coversion, LPF and sampling so as to
The in-phase component of reception signal and the value of quadrature component are obtained, is set to data set X={ x1,x2,…xN, wherein, N is data set
The number of middle element, that is, the unknown signaling sample point symbolic number received, sample point xj=[xjI,xjQ]T, i.e., by reception symbol
In-phase component xjIWith quadrature component xjQThe bivector of composition, the reception planisphere of signal can be formed.
Step S2：Cluster computing is carried out to the sample point of the data set X, obtains each sample point to cluster centre
Subordinated-degree matrix.Unknown signaling is pre-processed after obtaining data set X, and the sample point that can be concentrated to data carries out cluster computing, this
The clustering algorithm of embodiment is specifically calculated using K- mean clusters, and its classification matrix is V(0).K- means clustering algorithms can automatic logarithm
Classified according to object, the degree of membership of cluster centre and each sample point to class center is obtained by optimization object function, so as to
Determine the ownership of sample point.
K- mean cluster problems are represented by mathematical programming problem,
Its object function is：
Constraints is：K is cluster centre number, 1 ＜ K ＜ N.
Modulated signal corresponding to different cluster centre numbers is iterated, detailed process includes：
Step S21：Provide iteration criterion epsilon>0, and initialize classification matrix V(0), n=0；
Step S22：Calculate renewal Iterative Matrix U(n),
Step S23：Calculate cluster centre matrix V(n+1),
Step S24：With matrix norm | | | | compare V(n+1)With V(n)If | | V(n+1)-V(n) | |≤ε is set up, then stops changing
In generation, otherwise, make K=K+1, repeat step step S22 to step S24；In iterative process, optimization object functionObtain subordinated-degree matrix.
Step S3：The subordinated-degree matrix is handled with Validity Function, obtains being used to distinguish different modulating mode
Some characteristic parameters.Due to the signal with different modulating exponent number, its optimal cluster centre number is different, thus to be obtained
To the characteristic parameter that can distinguish different modulating mode, the cluster result obtained in different cluster centre number K values can be entered
Row efficiency analysis, whether reasonable, obtain Validity Function value if judging received signal points being divided into K classes, different as distinguishing
The characteristic parameter of modulation type, so as to distinguish different modulated signals.In the present embodiment, unknown modulated signal is：2PSK,
4PSK, 8PSK, 16QAM, 32QAM and 64QAM；Corresponding order of modulation is respectively：1,2,3,4,5 and 6；Take cluster centre number K
={ 2,4,8,16,32,64 }, the signaling point received is carried out more than in the case of six kinds of different cluster centre numbers respectively
Computing is clustered, and calculates the Validity Function value T in different K values respectivelyk, as the feature for distinguishing different modulating type
Parameter.In different signal to noise ratio, different cluster centre numbers, characteristic parameter used by six kinds of modulation systems of differentiation is calculated respectively
T2、T4、T8、T16、T32。
Step S3 result is as shown in Fig. 2 under different signal to noise ratio, modulated signal 2PSK characteristic parameter T2Value it is substantially high
In other five kinds of modulation systems, therefore, pass through characteristic parameter T2Modulation system 2PSK can be distinguished with other modulation types
Come；Similarly under different signal to noise ratio, 4PSK characteristic parameter T4It is worth apparently higher than remaining four kinds of modulation system；8PSK feature ginseng
Number T8It is worth apparently higher than its excess-three kind modulation system；16QAM characteristic parameter T16It is worth apparently higher than remaining two kinds of modulation system；
32QAM characteristic parameter T32Value is also slightly above 64QAM modulation systems；Therefore, T is passed through respectively4、T8、T16、T32Can be modulation methods
Formula 2PSK, 8PSK, 16QAM, 32QAM, which are sorted out, to be come, and realizes the Modulation Identification to six kinds of signals.
Step S4：Using the combination of some characteristic parameters as the input of SVM classifier, the SVM classifier is entered
Row training.The present embodiment is from the Modulation Identification performance of the system of raising, the six kinds of features extracted using above clustering algorithm
Input of the Combination nova of parameter as SVM, is trained to SVM classifier, then using the SVM classifier after training to the above
Six kinds of modulation systems based on planisphere are identified.Run into simultaneously in order to overcome during conventional SVMs identification multi-class problem
The high shortcoming of computation complexity, and reach the required precision of setting；As shown in figure 3, herein using classification algorithm to support to
Amount machine is trained.
Step S5：The unknown signaling is identified using the SVM classifier after training, in exporting for SVM classifier
To the modulation type of the unknown signaling.
The advantages of in order to verify the SVM classifier using classification training algorithm training, the present embodiment provides one to having a competition
Test, the SVM classifier using the training of classification training algorithm and conventional training algorithm is compared, computational complexity emulation ratio
Compared with as shown in figure 4, SVM classifications training algorithm computational complexity compared with conventional SVM training algorithms of the present embodiment use is big
To lower, the time as used in emulation data understand SVM classification training algorithms is 338.57 seconds, conventional SVM training algorithms institute's used time
Between 589.65 seconds, SVM classification training algorithm computational complexity greatly lower.
The present embodiment individually using clustering method, conventional SVM classifier Modulation Identification method and set forth herein joint tune
Recognition methods processed is emulated and is compared.The present embodiment is to common using 2PSK, 4PSK, 8PSK, 16QAM, 32QAM, 64QAM
Six kinds of modulated signals, its carrier signal are sine wave, frequency fc=4MHZ, using frequency fs=120MHZ, chip rate fd=
1MHZ, single analysis is using points (data length) Ns=2048.When signal to noise ratio is respectively -2dB, 0dB, 4dB, 8dB, 10dB
To individually using clustering method, conventional SVM classifier Modulation Identification method and set forth herein combined modulation recognition methods progress
Emulation, each case are respectively tested 1000 times, obtain the statistics of correct recognition rata.And assume the probability that every kind of modulation system occurs
It is identical, so as to calculate under different signal to noise ratio system to the average recognition rate of various modulation systems.Fig. 5, Fig. 6, Fig. 7 distinguish
For individually using K- means clustering algorithms, based on conventional SVM classifier Modulation Identification method and using cluster and SVM combined modulations
The Modulation Identification rate analogous diagram of recognizer constantly.
In Figure 5, six kinds of signals to be identified, with the difference of order of modulation, K- means Clusterings value is different, six kinds
Signal modulation correct recognition rata difference is larger, especially when signal to noise ratio is relatively low, due to the influence of noise, receives planisphere and compares point
Dissipate, now directly use cluster result and unreliable.
In figure 6, six kinds of signals to be identified, when being modulated identification using conventional BP algorithm SVM classifier, its discrimination
It is more or less the same, and in the case of low signal-to-noise ratio, correct recognition rata is not also high.
Fig. 7 is understood compared with Fig. 5, Fig. 6 respectively, under different signal to noise ratio, with individually using clustering algorithm or based on normal
Rule BP algorithm SVM classifier is compared, and the discrimination of algorithm presented here is significantly improved.When signal to noise ratio is 4dB, four
Kind modulation system 2PSK, 4PSK, 8PSK, 16QAM discrimination up to more than 90%, 32QAM and 64QAM Modulation Identification rate also have
Significantly improve, 32QAM has reached 98%, 64QAM in SNR=6dB, and in SNR=2dB, Modulation Identification is up to 70%, far above list
Solely using discrimination during cluster.The main reason for system identification performance improves：One is due to using clustering algorithm, to be identified
The characteristic value of signal extraction optimizes processing；Second, herein using K mean cluster and classification training SVM combined modulation identification
Algorithm, overcome the problem of computation complexity is high when identifying multi-class problem, convergence rate significantly improves so that in reception signal
When signal to noise ratio is relatively low, Modulation Identification rate is also greatly improved.
The above described is only a preferred embodiment of the present invention, any formal limitation not is made to the present invention, therefore
Every any modification that without departing from technical solution of the present invention content, the technical spirit according to the present invention is made to above example,
Equivalent variations and modification, in the range of still falling within technical solution of the present invention.
Claims (8)
1. the combined modulation recognition methods of a kind of K- mean clusters and classification training SVM classifier, it is characterised in that including as follows
Step：
Step S1：Unknown signaling is pre-processed, obtains the data set X of the in-phase component comprising signal and quadrature component；
Step S2：Cluster computing is carried out to the sample point of the data set X, obtains person in servitude of each sample point to cluster centre
Category degree matrix；
Step S3：The subordinated-degree matrix is handled with Validity Function, if obtaining for distinguishing different modulating mode
Dry characteristic parameter；
Step S4：Using the combination of some characteristic parameters as the input of SVM classifier, the SVM classifier is instructed
Practice；
Step S5：The unknown signaling is identified using the SVM classifier after training, institute is obtained in the output of SVM classifier
State the modulation type of unknown signaling.
2. the combined modulation recognition methods of a kind of K- mean clusters according to claim 1 and classification training SVM classifier,
Characterized in that,
In step sl, unknown signaling is pre-processed specially：Carrier wave is carried out at down coversion, LPF and sampling
Reason.
3. the combined modulation recognition methods of a kind of K- mean clusters according to claim 1 and classification training SVM classifier,
Characterized in that,
Data set X={ the x1,x2,…xN, wherein, N is the number of element in data set, i.e. the sample glyph of unknown signaling
Number.
4. the combined modulation recognition methods of a kind of K- mean clusters according to claim 3 and classification training SVM classifier,
Characterized in that,
In step s 2, clustering algorithm is specifically calculated using K- mean clusters, and its classification matrix is V(0), it is denoted as mathematics rule
The problem of drawing, object function are：
Constraints is：K is cluster centre number, 1 ＜ K ＜ N.
5. the combined modulation recognition methods of a kind of K- mean clusters according to claim 4 and classification training SVM classifier,
Characterized in that,
In step s 2, modulated signal corresponding to different cluster centre numbers is iterated, detailed process includes：
Step S21：Provide iteration criterion epsilon>0, and initialize classification matrix V(0), n=0；
Step S22：Calculate renewal Iterative Matrix U(n),
Step S23：Calculate cluster centre matrix V(n+1),
Step S24：With matrix norm | | | | compare V(n+1)With V(n)If | | V(n+1)-V(n)| |≤ε is set up, then stops iteration, no
Then, K=K+1, repeat step step S22 to step S24 are made；In iterative process, optimization object functionObtain subordinated-degree matrix.
6. the combined modulation recognition methods of a kind of K- mean clusters according to claim 5 and classification training SVM classifier,
Characterized in that,
Step S3 detailed process is：Efficiency analysis is carried out to the cluster result obtained during different cluster centre number K values, judged
It is whether reasonable that received signal points are divided into K classes, Validity Function value are obtained, as distinguishing some of different modulating mode
Characteristic parameter.
7. the combined modulation recognition methods of a kind of K- mean clusters according to claim 6 and classification training SVM classifier,
Characterized in that,
In step s 4, SVM classifier is trained using the algorithm of classification.
A kind of 8. combined modulation of the K- mean clusters and classification training SVM classifier according to claim any one of 3-7
Recognition methods, it is characterised in that unknown modulated signal is：2PSK, 4PSK, 8PSK, 16QAM, 32QAM and 64QAM；Corresponding tune
Exponent number processed is respectively：1,2,3,4,5 and 6；Take cluster centre number K={ 2,4,8,16,32,64 }.
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