CN112769722A

CN112769722A - Automatic identification method and system for communication signal modulation type

Info

Publication number: CN112769722A
Application number: CN202110008169.1A
Authority: CN
Inventors: 王峰; 黄珊珊
Original assignee: Hohai University HHU
Current assignee: Hohai University HHU
Priority date: 2021-01-05
Filing date: 2021-01-05
Publication date: 2021-05-07

Abstract

The invention discloses a method, a system, a device and a storage medium for automatically identifying the modulation type of a communication signal, which comprises the following steps of extracting the number of spectrum peaks of a signal in an unknown signal to be identified, and identifying a 2FSK signal and a 4FSK signal from the unknown signal based on the number of the spectrum peaks; acquiring a time domain envelope standard deviation of the identification unknown signal after the 2FSK signal and the 4FSK signal are extracted, and identifying a 16QAM signal and a BPSK QPSK signal based on the time domain envelope standard deviation; and acquiring a zero-crossing point number ratio by adopting an instantaneous autocorrelation algorithm, and distinguishing the BPSK signal and the QPSK signal according to the zero-crossing point number ratio. The automatic identification method of the invention improves the identification performance of each signal through three-level classification identification.

Description

Automatic identification method and system for communication signal modulation type

Technical Field

The invention relates to an automatic identification method, in particular to an automatic identification method of a communication signal modulation type, and belongs to the technical field of communication signal processing.

Background

In the prior art, the modulation type of a communication signal with unknown modulation information needs to be identified, and the main method is to use the existing knowledge to judge so as to obtain the modulation type and parameter information. However, the automatic identification method in the prior art mainly has the problems that phase coding signal modulation can be easily identified, or frequency coding signal modulation can be easily identified, and the classification adaptability is weak. Therefore, it is necessary to design an automatic identification method with strong classification adaptability.

Disclosure of Invention

The invention aims to overcome the defect of weak classification adaptability of an automatic identification method in the prior art, and provides an automatic identification method of a communication signal modulation type, which has the following technical scheme:

the automatic identification method of the communication signal modulation type comprises the following steps:

extracting the number of spectrum peaks of signals in unknown signals to be identified, and identifying 2FSK signals and 4FSK signals in the unknown signals based on the number of the spectrum peaks;

acquiring a time domain envelope standard deviation of the identification unknown signal after the 2FSK signal and the 4FSK signal are extracted, and identifying a 16QAM signal and a BPSK QPSK signal based on the time domain envelope standard deviation;

and acquiring a zero-crossing point number ratio by adopting an instantaneous autocorrelation algorithm, and distinguishing the BPSK signal and the QPSK signal according to the zero-crossing point number ratio.

Further, the acquisition of the number of spectral peaks includes the following steps:

obtaining a spectrogram through discrete Fourier transform;

and extracting the number of spectral peaks based on the spectrogram.

An automatic identification system for the modulation type of a communication signal, comprising

The frequency spectrum peak value number extraction module is used for identifying 2FSK signals and 4FSK signals from unknown signals to be identified;

the time domain envelope standard deviation acquisition module is used for identifying a 16QAM signal and a BPSK QPSK signal from an unknown signal to be identified;

and the zero crossing point number ratio acquisition module is used for identifying the BPSK signal and the QPSK signal from the unknown signal to be identified.

The automatic identification device for the modulation type of the communication signal comprises a memory, a processor and a computer program which is stored in the memory and can run on the processor, wherein when the processor executes the computer program, the steps of the method are realized.

A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, realizes the steps of the method of any of the preceding claims.

Compared with the prior art, the invention has the following beneficial effects:

the automatic identification method of the invention improves the identification performance of each signal through three-level classification identification.

Drawings

FIG. 1 is a flow chart of an automatic identification method of the present invention;

FIG. 2 is a fast Fourier transform spectrum acquired in example 1;

fig. 3 is a waveform of the real part of the instantaneous autocorrelation of the BPSK signal in example 1;

FIG. 4 is a waveform diagram of the instantaneous real part of autocorrelation of a QPSK signal in example 1;

FIG. 5 is a graph of the number of peaks of the spectrum obtained in example 1;

fig. 6 is a time domain envelope standard deviation feature diagram obtained in example 1;

FIG. 7 is a zero-crossing point number ratio characteristic diagram based on instantaneous autocorrelation in the embodiment 1;

fig. 8 is a graph showing the discrimination performance of each signal in example 1.

Detailed Description

The invention is further described below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.

Example 1

As shown in fig. 1, the method for automatically identifying the modulation type of a communication signal includes the following steps:

In this embodiment, the method further includes changing core parameters of the signals to construct training samples, and mainly after parameters such as symbol rate, bandwidth, signal-to-noise ratio and the like of each signal are changed, ensuring the ergodicity of the parameters. In this embodiment, five common signals of different modulation types, i.e., 2FSK, 4FSK, BPSK, QPSK, and 16QAM, are classified and identified, and the parameter settings of the signals are shown in table 1. Gaussian white noise is adopted in network training simulation, the change range of the signal-to-noise ratio is 10-18dB, and data is acquired once every 2 dB.

TABLE 1 parameter settings of signals

The acquisition of the number of the spectrum peaks comprises the following steps:

obtaining a spectrogram through discrete Fourier transform;

and extracting the number of spectral peaks based on the spectrogram.

The first-stage feature extraction based on discrete Fourier transform is the same as a Fourier transform algorithm, the discrete Fourier transform can realize the transformation of a signal from a time domain to a frequency domain, and the difference is that the discrete Fourier transform is in a discrete form in the time domain and the frequency domain. Specifically, in the first-stage network training, namely the first-stage classification, discrete fourier transform is mainly selected to extract the number characteristics of the spectral peaks, so that identification of frequency modulation signals and BPSK, QPSK, and 16QAM signals is realized. Based on the parameter settings in table 1, the spectral peak number features of each signal are cyclically extracted under different signal-to-noise ratios, and 2500 feature data are generated in total.

As shown in fig. 5, the characteristics of the number of spectral peaks of five signals are shown, and the number of spectral peaks of two signals, 2FSK and 4FSK, is relatively fixed, and is 2 and 4 respectively, as shown in fig. 5. The number of frequency peaks in the BPSK, QPSK and 16QAM bands is unstable, and the number of peaks increases with the increase of the bandwidth, and the number of spectrum peaks increases from 10 to about 310. BPSK compared to two frequency modulated signalsThe number of peaks is significantly greater for the three signals QPSK and 16 QAM. According to the characteristic diagram obtained by training, the threshold epsilon of the number of peak values can be obtained₁

Settings

2 and 4. When the number of frequency peaks of the unknown signal is 2, attributing the unknown signal to 2 FSK; when the number of frequency peaks of the unknown signal is 4, attributing the signal to 4 FSK; when the number of frequency peaks of the unknown signal is greater than 4, the signal is classified into the category of BPSK QPSK and 16 QAM. Through the first stage of identification, 2FSK and 4FSK can be identified and classified with other three signals BPSK, QPSK and 16 QAM.

Wherein, the continuous Fourier transform formula is as formula (1):

where x (t) represents the received time domain signal, f_cIs the carrier frequency. F (f) refers to the signal output spectrum, f refers to the signal frequency, j represents a complex number, and t is a time variable.

The corresponding discrete fourier transform is as in equation (2):

where x (N) represents a time-domain discrete signal sequence, k is the discrete frequency, and N is the total frequency number. F (k) refers to the digital frequency spectrum, N_sRefers to the number of sampling points, n refers to a discrete time variable, and j refers to a complex representation.

The Fourier transform transforms each communication signal to be identified from the time domain to the frequency domain, and the fast Fourier transform is a fast algorithm of discrete Fourier transform and can improve the calculation speed of the transform from the time domain to the frequency domain.

Fig. 2 shows a frequency domain characteristic, i.e., a spectrogram after fast fourier transform, where fig. 2(a) is 2FSK, fig. 2(b) is 4FSK, and fig. 2(c) is QPSK. As can be seen from the frequency spectrum in fig. 2, different modulation signals exhibit different characteristics in the frequency spectrum. On the basis of discrete Fourier transform, extracting the number of spectral peaks, and the number mu of the spectral peaks₁The following is obtained by equation (3):

μ₁＝Num{|S(k)|>η₁},k＝0,1,2,…N-1 (3)

wherein S (k) is the frequency value corresponding to the kth point, N is the total number of sampling points, |, represents the modulus operation, η₁Is the frequency peak threshold. The frequency peak threshold in this embodiment is 0.5 times the maximum value. Because the 2FSK signal has two frequency components, the 4FSK signal has four frequency components, the number of corresponding peak values is relatively fixed, and the number of peak values in signal frequency bands such as BPSK, QPSK, 16QAM and the like is relatively large. By setting the threshold of the number of frequency peak values, the 2FSK and 4FSK are distinguished from other signals.

The second-stage feature extraction based on time domain envelope has more obvious feature difference in time domain due to different modulation modes of different modulation signals. In the second-stage feature, the BPSK, QPSK and 16QAM signals are mainly classified, and the features of the signals in the time domain are extracted. The 16QAM signal is a signal modulated by combining amplitude and phase, and has a characteristic of multiple amplitude jumps, so that the envelope is not constant. BPSK and QPSK are signals modulated only by phase, have constant amplitude, and have the characteristic of constant envelope. Therefore, the envelope standard deviation feature of the signal is extracted as the second-stage classification feature aiming at the problem of whether the signal time domain envelope is constant or not. In this embodiment, specifically, in the second-stage network training, time-domain envelope standard deviation features of three signals are cyclically extracted under different signal-to-noise ratios based on the parameters in table 1, and each signal generates 500 pieces of feature data. Fig. 6 shows the time-domain envelope standard deviation characteristics of BPSK, QPSK, and 16 QAM. As can be seen from the figure, since the 16QAM signal is a signal that is modulated by both phase and amplitude, the envelope of the 16QAM signal is not constant, and the standard deviation of the envelope is large. BPSK and QPSK are only formed by phase modulation, and the envelope is constant, so the standard deviation of the envelope is small. Along with the improvement of the signal-to-noise ratio, the time domain envelope fluctuation of BPSK and QPSK signals is reduced, the corresponding envelope standard deviation is reduced, the difference with the envelope standard deviation of 16QAM is more obvious, the recognition effect is better, and the envelope standard deviation threshold epsilon is known through training₂It may be set to 0.14, i.e. the horizontal straight line in fig. 6. If notEnvelope standard deviation of known signal is greater than epsilon₂(i.e., located in the upper portion of the horizontal line), 16QAM signals are assigned, whereas BPSK and QPSK classes are assigned. Through the second-stage identification, the identification and classification of 16QAM, BPSK and QPSK can be completed.

Further, the time-domain envelope standard deviation μ of the signal x (n)₂The following is obtained by equation (4):

in the formula, N_sRepresents the total number of sample points in the sample,

representing the time-domain envelope mean.

And a third-level feature extraction based on a transient autocorrelation algorithm, wherein the transient autocorrelation algorithm is an important signal analysis algorithm and can be used for extracting transient features of signals. Compared with a common autocorrelation algorithm, the instantaneous autocorrelation has the characteristic that the instantaneous characteristics of the signal can be effectively reserved because the instantaneous autocorrelation does not have an integration process on time.

The instantaneous autocorrelation expression for the received signal x (t) is:

R(t,τ)＝x(t)·x^*(t-τ) (5)

wherein |. non chlorine^*Denotes a conjugate operation, and τ denotes a delay time. Signals of different modulation types may exhibit different characteristics in the temporal autocorrelation algorithm, where a PSK signal (phase shift keying signal) may be expressed as:

wherein A is amplitude, j is complex number, n is discrete time, f_cIs the carrier frequency and is,

representing a code group. If the signal is a BPSK signal,

there are two values, usually 0 and π; if the signal is a QPSK signal,

there are four values, usually 0,

Pi and

the corresponding instantaneous autocorrelation expression is:

wherein R (n, m) is instantaneous autocorrelation, A is signal amplitude, j is complex number representation, f_cRefers to carrier frequency, m refers to discrete time delay, n refers to discrete time, i refers to integer, phi_i+1Is meant to indicate phase. p is the number of samples in a sub-code and should be greater than the delay time m. The corresponding real part expression of instantaneous autocorrelation can be expressed as

In the formula, when the delay time m is kept unchanged, instantaneous autocorrelation expressions are all direct current levels in the same subcode; when different sub-codes are transformed, phase jump is generated, and the phase jump is mainly divided into the following quantities: the phase difference between adjacent symbols is 0 (i.e., the phase difference between adjacent symbols is 0

) (ii) a The other is that the phase difference between adjacent symbols is not 0 (i.e., the phase difference between adjacent symbols is not 0)

). For the case between the subcodes, pairSeveral phase modulated signals were specifically analyzed.

(1) When the signal is BPSK, if

The real auto-correlation part of the BPSK signal exhibits positive phase jumps; if it is

The real autocorrelation part of the BPSK signal exhibits a negative phase jump, so the phase of the BPSK signal has a two-value jump characteristic. Fig. 3 shows the waveform of the instantaneous real part of the autocorrelation of a BPSK signal, as can be seen from the figure. Within the subcode, the output of the instantaneous real autocorrelation part is DC level, and between the subcodes, the jump is made between-1 and 1 amplitude.

② when the signal is QPSK, if

The phase of the autocorrelation real part of the QPSK signal still shows positive value jump; if it is

The phase of the autocorrelation real part of the QPSK signal jumps to zero (the real-axis projection shows 0); if it is

When the phase of the real auto-correlation part of the QPSK signal jumps due to the negative value, the real instantaneous auto-correlation part of the QPSK signal has the characteristic of three-value jump. As shown in fig. 4, a waveform diagram of the instantaneous autocorrelation real part of a QPSK signal. As can be seen from the figure, in the sub-codes, the instantaneous autocorrelation real part output is in a direct current level, and jump among the sub-codes among three amplitudes of-1, 0 and 1.

The BPSK signal and the QPSK signal have larger difference on the characteristics of the real part of the instantaneous autocorrelation, and the ratio of the number of zero-crossing points based on the real part of the instantaneous autocorrelation can be extracted to serve as the third-level classification characteristic, so that the classification and identification of the BPSK signal and the QPSK signal are realized. Zero-crossing point number ratio mu based on real part of instantaneous autocorrelation₃Calculated by equation (9).

Further, the zero-crossing point number ratio is given by equation (9):

in the formula, N_sIs the total sampling point, a (i) represents the number of zero crossings, Num {. cndot } represents the count, η₃Is a set of values. In this embodiment, is set to be between-0.001 and 0.001. The number difference of BPSK and QPSK signals is large at zero time, the BPSK instantaneous autocorrelation real part waveform is binary jump between-1 and 1, the number difference is large at zero time, the BPSK instantaneous autocorrelation real part waveform is binary jump between-1 and 1, the number at zero time is almost 0, the QPSK instantaneous autocorrelation real part waveform is in three-value jump, and the number at zero time is large. And selecting the zero crossing point number ratio based on the real part of the instantaneous autocorrelation as an identification characteristic to realize the identification of BPSK and QPSK signals.

The signal identified by the automatic identification method of the present invention has an identification performance test structure as shown in tables 2, 3 and 4.

On the basis of obtaining threshold thresholds of all levels of features through training, test samples are generated according to parameters in the table 1, and the recognition performance of the algorithm is tested. The signal-to-noise ratio range is set to 6-18dB and the test sample is set to 1250. Under different signal-to-noise ratios, the signal identification accuracy in the three-stage network is shown in tables 2, 3 and 4.

TABLE 2 first-level recognition accuracy table

TABLE 3 second-level recognition accuracy Table

TABLE 4 third-level recognition accuracy table

According to the recognition rate of each stage of signal, a total recognition performance curve of the five signals is drawn, as shown in fig. 8.

As can be seen from the identification performance curves of tables 2, 3, and 4 and fig. 8, the identification performance of each signal is improved as the signal-to-noise ratio increases. In the second class, under the condition of low signal-to-noise ratio, the waveform fluctuation caused by noise interference of BPSK and QPSK is large, and further the time domain envelope standard deviation is increased, which affects the identification of 16QAM signals. When the signal-to-noise ratio is greater than or equal to 12dB, the average identification precision of all signals can reach more than 97%, the identification performance is good, and the feasibility of the three-layer classification decision identification algorithm is verified.

A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the aforementioned method.

As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

The above description is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, several modifications and variations can be made without departing from the technical principle of the present invention, and these modifications and variations should also be regarded as the protection scope of the present invention.

Claims

1. The automatic identification method of the communication signal modulation type is characterized by comprising the following steps:

extracting the number of spectrum peaks of signals in unknown signals to be identified, and identifying 2FSK signals and 4FSK signals from the unknown signals based on the number of the spectrum peaks;

acquiring a time domain envelope standard deviation of an identification unknown signal after extracting 2FSK signals and 4FSK signals, and identifying 16QAM signals and BPSK QPSK signals based on the time domain envelope standard deviation;

2. The method of claim 1, wherein said obtaining of the number of spectral peaks comprises the steps of:

obtaining a spectrogram through discrete Fourier transform;

and extracting the number of the spectrum peaks based on the spectrogram.

3. The method of claim 2, wherein the number of spectral peaks μ is greater than the number of spectral peaks₁The following is obtained by equation (3):

μ₁＝Num{|S(k)|>η₁},k＝0,1,2,…N-1 (3)

wherein S (k) is the frequency value corresponding to the kth point, N is the total number of sampling points, |, represents the modulus operation, η₁Is the frequency peak threshold.

4. The method of claim 1, wherein the time-domain envelope standard deviation is derived from the formula (4):

in the formula, N_sRepresents the total number of sample points in the sample,

representing the time-domain envelope mean.

5. Method for the automatic identification of the modulation type of a communication signal according to claim 1, characterized in that said zero-crossing number ratio is given by the formula (9):

in the formula, N_sIs the total sampling point, a (i) represents the number of zero crossings, Num {. cndot } represents the count, η₃Is a set of values.

6. An automatic identification system for the modulation type of a communication signal, comprising

7. Device for the automatic identification of the modulation type of a communication signal, comprising a memory, a processor and a computer program stored in said memory and executable on said processor, characterized in that said processor, when executing said computer program, carries out the steps of the method according to any one of claims 1 to 5.

8. Computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 5.