CN115378776A - MFSK modulation identification method based on cyclic spectrum parameters - Google Patents

MFSK modulation identification method based on cyclic spectrum parameters Download PDF

Info

Publication number
CN115378776A
CN115378776A CN202211008040.1A CN202211008040A CN115378776A CN 115378776 A CN115378776 A CN 115378776A CN 202211008040 A CN202211008040 A CN 202211008040A CN 115378776 A CN115378776 A CN 115378776A
Authority
CN
China
Prior art keywords
signal
cyclic
frequency
spectrum
cyclic spectrum
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202211008040.1A
Other languages
Chinese (zh)
Inventor
王茂法
杨武
李文欣
巩超
钱高峰
薛静泽
徐楚臻
朱振经
仇宝春
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Hangzhou Dianzi University
Original Assignee
Hangzhou Dianzi University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Hangzhou Dianzi University filed Critical Hangzhou Dianzi University
Priority to CN202211008040.1A priority Critical patent/CN115378776A/en
Publication of CN115378776A publication Critical patent/CN115378776A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L27/00—Modulated-carrier systems
    • H04L27/0012—Modulated-carrier systems arrangements for identifying the type of modulation
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L27/00—Modulated-carrier systems
    • H04L27/10—Frequency-modulated carrier systems, i.e. using frequency-shift keying
    • H04L27/106—M-ary FSK

Landscapes

  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Noise Elimination (AREA)

Abstract

The invention relates to a novel MFSK modulation identification method based on cyclic spectrum parameters. The method is directed to the identification of 2FSK and 4FSK modulated signals in a Gaussian channel. The existing MFSK class internal identification technology based on feature extraction, such as high-order cumulant and transient parameters, can not effectively identify under the condition of low signal-to-noise ratio, and an algorithm based on the number of cyclic spectrum peaks depends on the quality of cyclic spectrum images, so that signals are easily misjudged under the condition that the cyclic spectrum peaks are not obvious. The invention uses other cyclic spectrum parameters to replace the number of cyclic spectrum peaks to carry out modulation identification of 2FSK and 4FSK signals. Through 300 Monte Carlo experiments, the method has the advantages that the recognition accuracy can reach 92% under the condition that the signal-to-noise ratio SNR =1dB, and when the signal-to-noise ratio SNR is greater than 3dB, the recognition accuracy of the two signals can be kept above 99%.

Description

MFSK modulation identification method based on cyclic spectrum parameters
Technical Field
The invention belongs to the technical field of digital communication, and relates to an MFSK modulation identification method based on cyclic spectrum parameters.
Background
Multi-ary Frequency Shift Keying (MFSK) is often used for wireless short-wave channel communication, and is one of the important modulation modes in digital communication. FSK signals are widely used in transmission over fading channels because of their relatively strong resistance to fading. With the wide development of signal communication technology, the demand for monitoring, identifying and demodulating digital modulation signals has increased dramatically. The method effectively identifies the digital communication modulation modes including MFSK and is a great research hotspot in the current communication field.
According to the previous research on identification of relevant modulation modes, technical routes taken by scholars can be largely divided into two technologies, namely a modulation identification technology based on feature extraction (such as high-order cumulant and instantaneous parameter) and a modulation identification technology based on cyclic spectrum parameter. For the class-internal identification of MFSK signals, some scholars use statistical identification based on feature extraction, and these methods can effectively identify 2FSK and 4FSK signals under the condition of high signal-to-noise ratio, but have poor effect under the condition of low signal-to-noise ratio. Some scholars use an identification method based on the number of cyclic spectral peaks, but the method has requirements on an input MFSK signal, and the situation that the number of spectral peaks cannot be distinguished obviously and misjudgment is carried out frequently occurs.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a novel MFSK modulation identification method based on cyclic spectrum parameters, the method judges the order of an MFSK signal through the variance of the cyclic spectrum parameters when the cyclic frequency is alpha =0, and in addition, the algorithm also adopts three-level stationary wavelet denoising, so that the anti-noise performance of the algorithm is improved.
The invention comprises the following steps:
step one, performing one-dimensional discrete stationary wavelet denoising processing on a received signal: and (3) performing three-level db1 stable wavelet decomposition, returning three-level stationary wavelet coefficients, and performing next processing by using the third-level coefficient of the three-level stationary wavelet coefficients as a denoised signal.
Step two, setting the noise-reduced signal obtained in the step one as x (t), and processing the x (t) by using a frequency domain smoothing period method to obtain a cyclic spectrum parameter matrix;
step three, taking out all values of the cyclic frequency alpha =0 in the cyclic spectrum parameter matrix, and storing the values as a new row vector matrix S α＝0 。
Step four, solving the row vector matrix S obtained in the step three α＝0 And comparing the variance value Var with a variance threshold η =8 set in advance, wherein a signal greater than the threshold η is regarded as a 2FSK signal, and a signal smaller than the threshold η is regarded as a 4FSK signal
Preferably, in the first step, the received signal is received through a gaussian channel.
Preferably, in the second step, the frequency domain smoothing period method specifically includes:
intercepting the denoised signal X (T) obtained in the step one by using a rectangular window (the central time is T, the width is T), the frequency spectrum of the X (T) signal is X (f), and the frequency spectrum of the intercepted signal of the X (T) signal is defined as X (f) T (t,f)，
Figure BDA0003809724200000021
X is to be T The frequency spectrums (t, f) are respectively shifted to two sides by alpha/2 and-alpha/2, and a time-varying spectrum U of the intercepted signal is obtained T (t, f) and V T (t, f), where α is the cycle frequency,
U T (t,f)＝X T (t,f+α/2)
V T (t,f)＝X T (t,f-α/2) (1.2)
using the method shown in the following formula for U T (t, f) and
Figure BDA0003809724200000022
after multiplication, the window width T is taken 1 Carrying out mean value smoothing treatment on the operation result to finally obtain a circulating spectrum density function
Figure BDA0003809724200000023
Figure BDA0003809724200000024
T in the above equation represents the width of a rectangular window when the signal x (T) is subjected to short-time fourier transform, and is the same as T in equation (1.1).
Figure BDA0003809724200000025
Is a time-varying spectrum V T Conjugation of (t, f). Will U T (t, f) and V T Substituting the value of (t, f) into the formula:
Figure BDA0003809724200000026
and traversing each specific cyclic frequency alpha, calculating a cyclic spectrum density function of the specific cyclic frequency alpha, and obtaining a complete cyclic spectrum parameter matrix of the de-noised signal after traversing is finished.
Preferably, in the fourth step, the row vector matrix S α＝0 The variance value Var of (a) is obtained as shown in the following formula:
Figure BDA0003809724200000031
in the formula S α＝0 Is a row vector matrix at a cyclic frequency α =0 obtained by the step three, n is S α＝0 The number of elements in the matrix is,
Figure BDA0003809724200000032
is S α＝0 The mean of the matrix.
Drawings
FIG. 1 is a general flow diagram of the process;
FIG. 2 is a plot of variance values as a function of signal to noise ratio for 2FSK and 4FSK cyclic spectral density functions;
FIG. 3 is a graph of 2FSK and 4FSK signal identification accuracy;
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more clearly understood, the present application is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
The present invention will be described in further detail with reference to the following embodiments.
1. And (3) carrying out one-dimensional discrete stationary wavelet denoising treatment on the received signals, specifically, carrying out 3-layer stationary wavelet decomposition on the original signals by using db1 wavelets, and returning to three-level stationary wavelet coefficients swd, wherein Gaussian noise is mainly concentrated in the first two levels of wavelet coefficients, and the returned third level wavelet coefficients are extracted as denoised signals to be processed in the next step. The noise-reduced signal is used for further processing.
2. And solving a cyclic spectrum parameter matrix of the obtained de-noising signal by using a frequency domain smoothing period method.
According to the wiringkinson theorem, the cyclic spectrum correlation function of a signal is a fourier transform of its autocorrelation function, that is:
Figure BDA0003809724200000041
in the formula
Figure BDA0003809724200000042
Is a function of the cyclic spectral density of the signal,
Figure BDA0003809724200000043
alpha is the cyclic frequency, which is a cyclic autocorrelation function of the signal.
The following finds the cyclic spectral density function of the finite signal
Figure BDA0003809724200000044
Firstly, intercepting a denoised signal X (T) obtained in the step one by using a rectangular window (the central moment is T, the width is T), wherein the frequency spectrum of the X (T) signal is X (f), and the frequency spectrum of the intercepted signal of the X (T) signal is defined as X (f) T (t,f)，
Figure BDA0003809724200000045
The short-time fourier transform of the signal x (T) in the right equation corresponds to the output signal spectrum of the signal x (T) after passing through a frequency smoother, the transmission characteristic of which is a sinc-type function, the center frequency of which is f, and the bandwidth of which is 1/T. X is to be T The frequency spectrums (t, f) are respectively shifted to two sides by alpha/2 and-alpha/2, and a time-varying spectrum U of the intercepted signal is obtained T (t, f) and V T (t, f), α is the cycle frequency,
U T (t,f)＝X T (t,f+α/2)
V T (t,f)＝X T (t,f-α/2) (1.8)
using the method shown in the following formula for U T (t, f) and
Figure BDA0003809724200000046
after multiplication, the window width T is taken 1 Carrying out mean value smoothing treatment on the operation result to finally obtain a circulating spectrum density function
Figure BDA0003809724200000047
Figure BDA0003809724200000048
In the above equation, T represents the width of a rectangular window when the signal x (T) is subjected to short-time fourier transform, and is the same as T in equation (1.7).
Figure BDA0003809724200000049
Is a time-varying spectrum V T (t,f) Conjugation of (1). Will U T (t, f) and V T Substituting the value of (t, f) into the formula:
Figure BDA00038097242000000410
and traversing each specific cyclic frequency alpha, calculating a cyclic spectrum density function of the specific cyclic frequency alpha, and obtaining a complete cyclic spectrum parameter matrix of the de-noised signal after traversing is finished.
3. Taking out all values of the cyclic frequency alpha =0 in the cyclic spectrum parameter matrix, and storing the result as a new row vector matrix S α＝0 。
4. The obtained row vector matrix S α＝0 Is compared with a threshold η, where Var is:
Figure BDA0003809724200000051
in the formula S α＝0 Is a matrix of row vectors at a cyclic frequency α =0 obtained in step three, n is S α＝0 The number of elements in the matrix is,
Figure BDA0003809724200000052
is S α＝0 The mean of the matrix.
The threshold eta is set in advance, and the column vector matrix S of the 2FSK and 4FSK signals in the cyclic frequency alpha =0 is obtained from FIG. 2 α＝0 The standard value of the variance value Var is
Standard value of variance 2FSK 4FSK
Var 10 5.5
Thereby setting a column vector matrix S α＝0 The MFSK signal is identified with a variance threshold η =8, and a signal greater than the threshold η is considered to be a 2FSK signal, and a signal smaller than the threshold η is considered to be a 4FSK signal.
In actual simulation testing, the simulation modulation is set as: the symbol rate of the MFSK signal is set to 500Baud, the carrier frequency is set to 2000Hz, the sampling frequency of the receiver is set to 12000Hz, the channel adopts a Gaussian channel, the SNR range of the signal to noise ratio is set to 1-10 dB, 300 Monte Carlo experiments are carried out to simulate under each SNR, and the identification correct rates of the 2FSK and 4FSK signals under the method are collected, and the graph is shown in FIG. 3. Fig. 3 shows that the accuracy of the algorithm can reach 92% already when the SNR is =1dB, and the accuracy of the identification of the two signals can be kept above 99% when the SNR is >3 dB.
The above description is only a part of the embodiments of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention.

Claims (4)

1. A MFSK modulation identification method based on cyclic spectrum parameters is characterized by comprising the following steps:
step one, performing one-dimensional discrete stationary wavelet denoising processing on a received signal: performing three-level db1 stable wavelet decomposition, returning three-level stationary wavelet coefficients, and performing next processing by using the third level coefficient of the three-level stationary wavelet coefficients as a denoised signal;
step two, setting the noise-reduced signal obtained in the step one as x (t), and processing the x (t) by using a frequency domain smoothing period method to obtain a cyclic spectrum parameter matrix;
step three, takingAll values of the cyclic frequency alpha =0 in the cyclic spectrum parameter matrix are stored as a new row vector matrix S α＝0 ；
Step four, solving the row vector matrix S obtained in the step three α＝0 And comparing the variance value Var with a variance threshold η =8 set in advance, wherein a signal greater than the threshold η is regarded as a 2FSK signal, and a signal smaller than the threshold η is regarded as a 4FSK signal.
2. The MFSK modulation identification method based on cyclic spectral parameters as claimed in claim 1, wherein in the first step, the received signal is received through a Gaussian channel.
3. The MFSK modulation identification method based on cyclic spectrum parameters as claimed in claim 2, wherein in the second step, the frequency domain smoothing period method specifically comprises:
intercepting the denoised signal X (T) obtained in the first step by using a rectangular window with the center time of T and the width of T, wherein the frequency spectrum of the X (T) signal is X (f), and the frequency spectrum of the signal obtained by intercepting the X (T) signal is defined as X (f) T (t,f)，
Figure FDA0003809724190000011
Mixing X T The frequency spectrums (t, f) are respectively shifted to two sides by alpha/2 and-alpha/2, and a time-varying spectrum U of the intercepted signal is obtained T (t, f) and V T (t, f), where α is the cycle frequency,
U T (t,f)＝X T (t,f+α/2)
V T (t,f)＝X T (t,f-α/2)
using the method shown in the following formula for U T (t, f) and
Figure FDA0003809724190000012
after multiplication, the window width T is taken 1 Carrying out mean value smoothing treatment on the operation result to obtain the final resultTo cyclic spectral density function
Figure FDA0003809724190000013
Figure FDA0003809724190000014
T in the above equation represents the rectangular window width when the signal x (T) is subjected to short-time fourier transform,
Figure FDA0003809724190000015
is a time-varying spectrum V T Conjugation of (t, f); will U T (t, f) and V T Substituting the value of (t, f) into the formula:
Figure FDA0003809724190000021
and traversing each specific cyclic frequency alpha, calculating a cyclic spectrum density function of the specific cyclic frequency alpha, and obtaining a complete cyclic spectrum parameter matrix of the de-noised signal after traversing is finished.
4. The MFSK modulation identification method based on cyclic spectral parameters as claimed in claim 3, wherein in the fourth step, the row vector matrix S α＝0 The variance value Var of (a) is obtained as shown in the following formula:
Figure FDA0003809724190000022
in the above formula S α＝0 Is a row vector matrix at a cyclic frequency α =0 obtained by the step three, n is S α＝0 The number of elements in the matrix is,
Figure FDA0003809724190000023
is S α＝0 The mean of the matrix.
CN202211008040.1A 2022-08-22 2022-08-22 MFSK modulation identification method based on cyclic spectrum parameters Pending CN115378776A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202211008040.1A CN115378776A (en) 2022-08-22 2022-08-22 MFSK modulation identification method based on cyclic spectrum parameters

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202211008040.1A CN115378776A (en) 2022-08-22 2022-08-22 MFSK modulation identification method based on cyclic spectrum parameters

Publications (1)

Publication Number Publication Date
CN115378776A true CN115378776A (en) 2022-11-22

Family

ID=84068465

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202211008040.1A Pending CN115378776A (en) 2022-08-22 2022-08-22 MFSK modulation identification method based on cyclic spectrum parameters

Country Status (1)

Country Link
CN (1) CN115378776A (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116593971A (en) * 2023-07-13 2023-08-15 南京誉葆科技股份有限公司 Radar signal modulation identification method of instantaneous frequency characteristic

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116593971A (en) * 2023-07-13 2023-08-15 南京誉葆科技股份有限公司 Radar signal modulation identification method of instantaneous frequency characteristic

Similar Documents

Publication Publication Date Title
CN105785324B (en) Linear frequency-modulated parameter estimating method based on MGCSTFT
CN107392123B (en) Radio frequency fingerprint feature extraction and identification method based on coherent accumulation noise elimination
CN113325277A (en) Partial discharge processing method
CN112737992B (en) Underwater sound signal modulation mode self-adaptive in-class identification method
CN112380939A (en) Deep learning signal enhancement method based on generation countermeasure network
CN115378776A (en) MFSK modulation identification method based on cyclic spectrum parameters
CN108737302A (en) The symbol rate estimation method and its device of accidental resonance joint wavelet transformation under Low SNR
CN113098638B (en) Weak signal detection method based on grouped range diagram
CN109004996B (en) Signal detection method based on multi-sine-window power spectrum peak value
CN114422311A (en) Signal modulation identification method and system combining deep neural network and expert prior characteristics
Zhang et al. Efficient residual shrinkage CNN denoiser design for intelligent signal processing: Modulation recognition, detection, and decoding
CN112235077A (en) BPSK signal blind processing result credibility self-adaption checking method based on Gaussian Copula
CN109167744B (en) Phase noise joint estimation method
CN110190917B (en) Frequency spectrum hole sensing method, device and equipment for LTE230MHz power wireless private network
CN116032709B (en) Method and device for blind demodulation and modulation feature analysis of FSK signal without priori knowledge
CN104270328B (en) A kind of signal to noise ratio real-time estimation method
CN114268393B (en) Cognitive radio spectrum sensing method based on number characteristics of connected components
CN111147168B (en) Signal detection method with power spectrum and statistics fused
CN112333123B (en) Non-cooperative PSK underwater acoustic communication signal multi-wavelet-based automatic preferred baseband demodulation method
CN114374450A (en) Maximum eigenvalue detector based on oversampling
Batra et al. A Study of Automatic Modulation Classification for Rayleigh Fading Channel
CN111740930B (en) Multi-type non-cooperative underwater acoustic signal identification method based on multi-feature hierarchical processing
CN114268389B (en) Multi-point cooperative spectrum sensing method combined with wavelet transformation
CN112083448B (en) Satellite navigation system-oriented interference signal classification recognition feature extraction method and system
CN116032310B (en) Signal self-adaptive detection reconstruction method based on channelized filtering

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination