CN111585662B - Classification identification and parameter estimation method and system for phase modulation signal - Google Patents
Classification identification and parameter estimation method and system for phase modulation signal Download PDFInfo
- Publication number
- CN111585662B CN111585662B CN202010484531.8A CN202010484531A CN111585662B CN 111585662 B CN111585662 B CN 111585662B CN 202010484531 A CN202010484531 A CN 202010484531A CN 111585662 B CN111585662 B CN 111585662B
- Authority
- CN
- China
- Prior art keywords
- signal
- communication
- signals
- communication signal
- 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.)
- Active
Links
Images
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B13/00—Transmission systems characterised by the medium used for transmission, not provided for in groups H04B3/00 - H04B11/00
- H04B13/02—Transmission systems in which the medium consists of the earth or a large mass of water thereon, e.g. earth telegraphy
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L27/00—Modulated-carrier systems
- H04L27/0006—Assessment of spectral gaps suitable for allocating digitally modulated signals, e.g. for carrier allocation in cognitive radio
-
- 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
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Spectroscopy & Molecular Physics (AREA)
- Digital Transmission Methods That Use Modulated Carrier Waves (AREA)
Abstract
The invention relates to the technical field of phase modulation signal classification identification and parameter estimation, and provides a phase modulation signal classification identification and parameter estimation method and a phase modulation signal classification identification and parameter estimation system, wherein the method comprises the following steps: s1: separating signals from noise to realize accurate extraction of communication signals under a noise background; s2: the classification identification of the communication signals and other interference signals is realized, and the classification identification of the broadband communication signals, the narrowband signals and the frequency modulation signals is realized; s3: the classification identification of the phase modulation signal and other signals identifies the phase modulation signal in various communication signals; s4: identifying phase modulation signals in class, calculating a square spectrum of the phase modulation signals, and identifying BPSK and QPSK signals according to the difference of spectral peak characteristics of the square spectrum; s5: and correcting parameters of the communication signals, and estimating the parameters of the communication signals. The problem that the stability of time domain characteristics and frequency domain characteristics of communication signals is influenced due to the fact that the existing ocean acoustic channel adopts time-frequency double-expansion channels, the bandwidth is narrow, the Doppler frequency shift is large, the background noise is strong is solved.
Description
Technical Field
The invention relates to the technical field of classification identification and parameter estimation of phase modulation signals, in particular to a method and a system for classification identification and parameter estimation of phase modulation signals, which are suitable for underwater sound time-frequency double-expansion weak channels and have good application prospects in detection, identification classification and parameter estimation of less prior or totally blind communication signals.
Background
With the development of new ocean technologies and ocean equipment, underwater ocean equipment is applied to various operation sites, and high-speed and reliable data transmission is needed among various devices in order to realize information interaction and sharing. Communication sonar, especially high-speed communication sonar, is widely applied to various devices, and realizes data and information sharing among various platforms. The underwater acoustic communication signals are monitored, the signal system of domestic and foreign communication sonars is deeply analyzed, the detection, classification and identification of the communication signals and the decoding of communication information are completed, the transparent transmission of underwater data is realized, and the requirements of underwater security and national defense safety are met.
The ocean sound channel is a typical time-frequency double-expansion channel and is characterized in that: narrow bandwidth, large doppler shift and strong background noise, which seriously affect the stability of the time domain and frequency domain characteristics of the communication signal.
Disclosure of Invention
In order to improve the recognition probability of the communication signals, the algorithm extracts the characteristic parameters with good robustness and stable performance of the communication signals, and reduces the influence of the underwater sound fading channel on the recognition algorithm according to a multi-parameter joint estimation method. And finishing the classification and identification of the phase modulation signals according to five steps of signal and noise classification and identification, broadband communication signal and narrowband signal and frequency modulation signal classification and identification, phase modulation signal and other communication signals classification and identification, phase modulation signal in-class identification and communication signal parameter correction.
In the signal and noise classification identification, the center frequency and the bandwidth of a communication signal are sensed in a self-adaptive mode, the signal to noise ratio of each frame of signal is calculated rapidly, and accurate extraction of the signal under the noise background is achieved by combining three characteristic parameters. And the change rate of the central frequency of the signal and the bandwidth change characteristic parameters of the signal are quickly calculated, so that the classification and identification of the broadband underwater acoustic communication signal, the narrowband signal and the frequency modulation signal are realized. The classification and identification of the phase modulation signal and other communication signals are to calculate three characteristic parameters of the symmetry of a frequency spectrum, the flat coefficient of the frequency spectrum and the normalized signal envelope variance, and identify the phase modulation signal in various underwater acoustic communication signals according to the three characteristic parameters. The identification in the phase modulation signal class is to identify BPSK and QPSK according to the difference of the spectral peak characteristics of the square spectrum by calculating the square spectrum of the communication signal. And correcting the center frequency, the bandwidth, the pulse width and the code rate of the communication signal by combining the frequency spectrum and the square spectrum of the signal, and estimating the parameters of the final communication signal.
The above object of the present invention is achieved by the following technical solutions:
a method for phase modulation signal classification identification and parameter estimation comprises the following steps:
s1: separating signals and noise aiming at input communication signals, performing framing processing on the communication signals, acquiring and calculating the center frequency, bandwidth and signal-to-noise ratio of the communication signals of each frame, and combining three characteristic parameters including the center frequency, the bandwidth and the signal-to-noise ratio to realize accurate extraction of the communication signals under the noise background;
s2: aiming at the classification identification of the communication signal and other interference signals, calculating the central frequency change rate and bandwidth change characteristic parameters of the signals, and realizing the classification identification of the communication signal, the narrow-band signal and the frequency modulation signal of the broadband;
s3: aiming at the classification identification of a phase modulation signal and other signals in the communication signal, calculating three characteristic parameters of the symmetry of the frequency spectrum, the flat coefficient of the frequency spectrum and the normalized signal envelope variance of the communication signal, and identifying the phase modulation signal in various communication signals according to the three characteristic parameters;
s4: aiming at the phase modulation signal in-class identification, calculating a square spectrum of the phase modulation signal, and identifying BPSK and QPSK signals according to the spectral peak characteristic difference of the square spectrum;
s5: and correcting parameters of the communication signal, correcting the center frequency, the bandwidth, the pulse width and the code rate of the communication signal by combining the frequency spectrum and the square spectrum of the communication signal, and estimating the final parameters of the communication signal.
Further, in step S1, the method further includes:
s11: performing framing processing on the communication signal, and collecting the communication signal [ x (0), x (1), … x (N-1) with the length of N points]Calculating the frequency spectrum of the communication signal for each frame by means of a fast Fourier transform
Wherein W is a Fourier rotation factor;
s12: performing spectrum peak search on the frequency spectrum of the communication signal of each frame, searching the maximum value of X (k), determining the position Index _ max of the maximum value, and calculating the bandwidth;
s13: calculating signal energy within a bandwidth
Wherein, X is the frequency spectrum of the signal, Lb represents the lower frequency point of the signal bandwidth, and Lu represents the upper frequency point of the signal bandwidth.
Further, in step S2, the method further includes:
s21: calculating a bandwidth of the communication signal for each frame of the communication signal, in particular: the bandwidth of the communication signal of the ith frame is as follows: delta _ fiCounting the bandwidth change of the communication signals of multiple frames according to Lu-Lb;
s22: calculating a center frequency of the communication signal for each frame of the communication signal, in particular: the center frequency of the communication signal of the ith frame is as follows: fc (Fc)iCounting the change of the center frequency of the communication signals of multiple frames as Index _ max;
s23: modeling is performed for the center frequency, and the modeling is a linear model, wherein Fc (k) is multiplied by Fci+ b, the coefficients k, b are estimated using the least squares method.
Further, in step S3, the method further includes calculating the symmetry of the frequency spectrum of the communication signal and the flatness coefficient of the frequency spectrum, specifically:
s31: coefficient of symmetry K of the spectrumFThe characteristic parameters are used for characterizing the symmetry of the communication signal spectrum about the center frequency, and specifically include:
wherein E isbIs the lower band energy, EuIs the upper frequency band energy, Index _ max is the center frequency of the signal, and the symmetry coefficient K of the frequency spectrum is calculatedFComprises the following steps:
KF＝Eb/Eu
s32: the flatness coefficient of the spectrum is a parameter for measuring the energy distribution of the spectrum, and specifically includes:
further, step S4 includes: calculating a square spectrum of the phase modulation signal, specifically:
s41: receiving the framing processing of the communication signal, collecting signals [ x (0), x (1), … x (N-1) ] with the length of N points, and calculating the square signal of the communication signal as:
y(n)＝x2(n)
s42: calculating a squared spectrum of the communication signal as:
further, in step S5, the method further includes:
and searching a spike pulse signal in the square spectrum of the communication signal, and accurately correcting the carrier frequency and the communication code rate of the communication signal according to the position of the spike pulse signal.
A system for classification identification and parameter estimation of phase modulated signals, comprising: the system comprises a signal noise separation module, a broadband communication signal detection and extraction module, a phase modulation signal classification and identification module, a phase modulation signal in-class identification module and a communication signal parameter estimation module;
the signal noise separation module is used for separating signals and noise aiming at input communication signals, performing framing processing on the communication signals, acquiring and calculating the center frequency, the bandwidth and the signal-to-noise ratio of the communication signals of each frame, and combining three characteristic parameters including the center frequency, the bandwidth and the signal-to-noise ratio to realize accurate extraction of the communication signals under a noise background;
the broadband communication signal detection and extraction module is used for calculating the central frequency change rate and the bandwidth change characteristic parameters of the signals aiming at the classification identification of the communication signals and other interference signals, and realizing the classification identification of the communication signals, narrow-band signals and frequency modulation signals of the broadband;
the phase modulation signal classification identification module is used for calculating three characteristic parameters of the symmetry of a frequency spectrum, a flat coefficient of the frequency spectrum and normalized signal envelope variance of the communication signal aiming at the classification identification of the phase modulation signal and other signals in the communication signal, and identifying the phase modulation signal in various communication signals according to the three characteristic parameters;
the phase modulation signal in-class identification module is used for calculating a square spectrum of the phase modulation signal aiming at the phase modulation signal in-class identification, and identifying BPSK and QPSK signals according to the difference of spectral peak characteristics of the square spectrum;
and the communication signal parameter estimation module is used for carrying out parameter correction on the communication signal, correcting the center frequency, the bandwidth, the pulse width and the code rate of the communication signal by combining the frequency spectrum and the square spectrum of the communication signal, and estimating the final parameter of the communication signal.
Further, the signal noise separation module further includes:
a framing processing unit for framing the communication signal and collecting the communication signal [ x (0), x (1), … x (N-1) with length of N points]Calculating the frequency spectrum of the communication signal for each frame by means of a fast Fourier transform
Wherein W is a Fourier rotation factor;
a spectral peak searching unit, configured to perform spectral peak search for the frequency spectrum of the communication signal of each frame, find a maximum value of x (k), determine a position Index _ max thereof, and calculate a bandwidth;
a signal energy calculating unit for calculating signal energy within the bandwidth
Wherein, X is the frequency spectrum of the signal, Lb represents the lower frequency point of the signal bandwidth, and Lu represents the upper frequency point of the signal bandwidth.
Further, the wideband communication signal detection and extraction module further includes:
a bandwidth variation statistics unit, configured to calculate, for each frame of the communication signal, a bandwidth of the communication signal, specifically: the bandwidth of the communication signal of the ith frame is as follows: delta _ fiCounting the bandwidth change of the communication signals of multiple frames according to Lu-Lb;
a center frequency variation statistical unit for calculating a center frequency of the communication signal for each frame of the communication signal, specifically: the center frequency of the communication signal of the ith frame is as follows: fc (Fc)iCounting the change of the center frequency of the communication signals of multiple frames as Index _ max;
a center frequency modeling unit for modeling the center frequency as a linear model, wherein the model is Fc (k x Fc)i+ b, estimating the coefficient k, b by adopting a least square method;
the phase modulation signal classification and identification module further comprises:
a symmetry coefficient calculation unit for calculating a symmetry coefficient K of the frequency spectrumFThe characteristic parameters for characterizing symmetry of the communication signal spectrum with respect to the center frequency specifically include:
wherein E isbIs the lower band energy, EuIs the upper frequency band energy, Index _ max is the center frequency of the signal, and the symmetry coefficient K of the frequency spectrum is calculatedFComprises the following steps:
KF＝Eb/Eu
the flat coefficient calculation unit is used for calculating a flat coefficient of a frequency spectrum, is a parameter for measuring the energy distribution of the frequency spectrum, and specifically comprises the following steps:
further, the phase modulation signal class-in identification module further includes:
a square spectrum calculating unit for receiving the communication signal and performing frame division processing to acquire signals [ x (0), x (1), … x (N-1) with the length of N points]Calculating a squared signal of the communication signal as: y (n) ═ x2(n) calculating a squared spectrum of the communication signal as:
the communication signal parameter estimation module further comprises:
and the spike signal correction unit is used for searching a spike signal in a square spectrum of the communication signal and accurately correcting the carrier frequency and the communication code rate of the communication signal according to the position of the spike signal.
Compared with the prior art, the invention has the beneficial effects that:
the classification and identification of the phase modulation signals are completed by design methods of signal extraction, large class identification and intra-class identification, and a plurality of parameters of the phase modulation signals are estimated. The signal processing flow of the invention is divided into five steps, namely signal and noise classification identification, wideband communication signal and narrowband signal and frequency modulation signal classification identification, phase modulation signal and other communication signal classification identification, phase modulation signal in-class identification and communication signal parameter correction. In each signal processing flow link, a plurality of stable signal characteristic quantities are analyzed and extracted, and phase modulation signal identification and parameter estimation have high accuracy under a complex underwater acoustic environment.
Drawings
FIG. 1 is a block diagram of an overall process of a method for phase modulation signal classification identification and parameter estimation according to the present invention;
FIG. 2 is a diagram of a decision tree structure of a method for phase modulation signal classification identification and parameter estimation according to the present invention;
fig. 3 is a BPSK time domain waveform of the present invention;
FIG. 4 is a BPSK time-frequency diagram of the invention;
FIG. 5 is a diagram of the QPSK time domain waveform of the present invention;
FIG. 6 is a QPSK time-frequency diagram according to the invention;
FIG. 7 is an overall block diagram of a phase modulation signal classification identification and parameter estimation system according to the present invention;
FIG. 8 is a block diagram of a signal noise separation module in a phase modulation signal classification identification and parameter estimation system according to the present invention;
FIG. 9 is a block diagram of a wideband communication signal detection and extraction module in a phase modulation signal classification identification and parameter estimation system according to the present invention;
FIG. 10 is a block diagram of a phase modulation signal class identification module in a phase modulation signal class identification and parameter estimation system according to the present invention;
FIG. 11 is a block diagram of an identification module within a phase modulation signal class in a phase modulation signal class identification and parameter estimation system according to the present invention;
fig. 12 is a block diagram of a communication signal parameter estimation module in a phase modulation signal classification identification and parameter estimation system according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments that can be derived by a person skilled in the art from the embodiments given herein without making any inventive work are intended to be within the scope of the present disclosure.
As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
The application scenarios of the invention are as follows: the underwater marine equipment is applied to various operation sites, and high-speed reliable data transmission is needed among various devices in order to realize information interaction and sharing. Communication sonar, especially high-speed communication sonar, is widely applied to various devices, and realizes data and information sharing among various platforms. The underwater acoustic communication signals are monitored, the signal system of domestic and foreign communication sonars is deeply analyzed, the detection, classification and identification of the communication signals and the decoding of communication information are completed, the transparent transmission of underwater data is realized, and the requirements of underwater security and national defense safety are met. The ocean sound channel is a typical time-frequency double-expansion channel and is characterized in that: narrow bandwidth, large doppler shift and strong background noise, which seriously affect the stability of the time domain and frequency domain characteristics of the communication signal. Therefore, it is necessary to find a method for classifying and identifying phase modulation signals and estimating parameters, which can improve the identification probability of communication signals.
Based on the application scenarios, the core thought of the invention is as follows: the method is suitable for classification identification and parameter estimation algorithm of phase modulation (phase shift keying) signals in underwater acoustic coherent communication, automatically senses and identifies the modulation mode of communication signals on the premise of no need of prior knowledge, and estimates four parameters of bandwidth, center frequency, pulse width and communication code rate of the signals. The algorithm is divided into four steps to complete the classification identification and parameter estimation of signals. Firstly, in the detection and extraction stage of the broadband communication signal, the separation and extraction of the communication signal in background noise are completed. The algorithm extracts three characteristic parameters of the frequency spectrum center frequency change rate, the frequency bandwidth and the signal-to-noise ratio of the communication signal, automatically senses and extracts the broadband communication signal, and estimates the pulse width of the signal. Secondly, phase modulation signals are classified and identified, and phase modulation signals are extracted from broadband communication signals. And calculating three characteristic parameters of the symmetry of the frequency spectrum, the flat coefficient of the frequency spectrum and the normalized signal envelope variance of the extracted broadband communication signal, perceiving and identifying the phase modulation signal, and estimating the center frequency and the bandwidth of the signal. Again, identification within the phase modulation signal class is a process of further subdividing within the phase modulation signal class, mainly for identifying BPSK and QPSK signals. And calculating a signal square spectrum, and identifying BPSK and QPSK signals according to the difference of spectral peak characteristics of the square spectrum. And finally, finely correcting the characteristic parameters of the communication signals, comprehensively estimating the characteristic parameters of the signal frequency spectrum and the square spectrum, estimating the communication code rate, correcting the center frequency and the bandwidth, and realizing the classification identification and the characteristic parameter estimation of the communication signals.
The invention has the following remarkable characteristics: on the premise of no need of prior knowledge, the modulation mode of the communication signal is automatically sensed and identified, and characteristic parameters of the signal are given, so that a previous basis is provided for blind demodulation and information decoding of a later-stage signal.
The invention is tested and verified in south China sea, underwater acoustic communication signal recording submerged buoy is distributed on the sea bottom, and acoustic signals are collected and recorded. The recording subsurface buoy adopts a receiver design with a wide frequency band and a large dynamic range, and can acquire sound wave signals below 50 kHz. In the test process, communication signals of various systems are transmitted, and the designed signals cover various frequency bands and various communication code rates.
Example one
As shown in the overall processing block diagram of the method for identifying the class of the phase modulation signal and estimating the parameters in fig. 1 and the decision tree structure diagram of the method for identifying the class of the phase modulation signal and estimating the parameters in fig. 2, the present embodiment provides a method for identifying the class of the phase modulation signal and estimating the parameters, which includes:
s1: separating signals and noise aiming at input communication signals, performing framing processing on the communication signals, acquiring and calculating the center frequency, bandwidth and signal-to-noise ratio of the communication signals of each frame, and combining three characteristic parameters including the center frequency, the bandwidth and the signal-to-noise ratio to realize accurate extraction of the communication signals under the noise background.
Specifically, in this embodiment, the first thing to do with the present invention is to remove noise from the communication signal, and the noise removal is performed for the communication signal of each frame in combination with the center frequency, bandwidth and signal-to-noise ratio of the communication signal.
The step S1 further includes the steps of:
s11: performing framing processing on the communication signal, and collecting the communication signal [ x (0), x (1), … x (N-1) with the length of N points]Go through quicklyFast Fourier transform computing the frequency spectrum of the communication signal for each frame
Where W is the fourier rotation factor.
S12: a spectral peak search is performed for the spectrum of the communication signal of each frame, the maximum value of x (k) is found, its position Index _ max is determined, and the bandwidth is calculated.
S13: calculating signal energy within a bandwidth
Wherein, X is the frequency spectrum of the signal, Lb represents the lower frequency point of the signal bandwidth, and Lu represents the upper frequency point of the signal bandwidth.
S2: aiming at the classification identification of the communication signal and other interference signals, the center frequency change rate and the bandwidth change characteristic parameters of the signals are calculated, and the classification identification of the communication signal, the narrow-band signal and the frequency modulation signal of the broadband is realized.
Specifically, in this embodiment, after removing noise with respect to the communication signal, other interference signals need to be removed, identification of the communication signal in the wideband, the narrowband signal and the frequency modulation signal is achieved by calculating a center frequency change rate and a bandwidth change characteristic parameter of the signal, other interference signals including the narrowband signal and the frequency modulation signal are removed, and the communication signal in the wideband is retained.
The specific steps of step S2 are:
s21: calculating a bandwidth of the communication signal for each frame of the communication signal, in particular: the bandwidth of the communication signal of the ith frame is as follows: delta _ fiCounting the bandwidth change of the communication signals of multiple frames according to Lu-Lb;
s22: calculating a center frequency of the communication signal for each frame of the communication signal, in particular: the center frequency of the communication signal of the ith frame is as follows: fc (Fc)iCounting the change of the center frequency of the communication signals of multiple frames as Index _ max;
s23: modeling is performed for the center frequency, and the modeling is a linear model, wherein Fc (k) is multiplied by Fci+ b, the coefficients k, b are estimated using the least squares method.
S3: aiming at the classification identification of the phase modulation signal and other signals in the communication signal, three characteristic parameters of the symmetry of the frequency spectrum, the flat coefficient of the frequency spectrum and the normalized signal envelope variance of the communication signal are calculated, and the phase modulation signal is identified in various communication signals according to the three characteristic parameters.
Specifically, in this step, the phase modulation signal is mainly screened out, and other signals are removed.
In step S3, the method further includes calculating the symmetry of the frequency spectrum of the communication signal and the flatness coefficient of the frequency spectrum, specifically:
s31: coefficient of symmetry K of the spectrumFThe characteristic parameters are used for characterizing the symmetry of the communication signal spectrum about the center frequency, and specifically include:
wherein E isbIs the lower band energy, EuIs the upper frequency band energy, Index _ max is the center frequency of the signal, and the symmetry coefficient K of the frequency spectrum is calculatedFComprises the following steps:
KF＝Eb/Eu
s32: the flatness coefficient of the spectrum is a parameter for measuring the energy distribution of the spectrum, and specifically includes:
s4: and aiming at the phase modulation signal in-class identification, calculating a square spectrum of the phase modulation signal, and identifying BPSK and QPSK signals according to the spectral peak characteristic difference of the square spectrum.
Calculating a square spectrum of the phase modulation signal, specifically:
s41: receiving the framing processing of the communication signal, collecting signals [ x (0), x (1), … x (N-1) ] with the length of N points, and calculating the square signal of the communication signal as:
y(n)＝x2(n)
s42: calculating a squared spectrum of the communication signal as:
s5: and correcting parameters of the communication signal, correcting the center frequency, the bandwidth, the pulse width and the code rate of the communication signal by combining the frequency spectrum and the square spectrum of the communication signal, and estimating the final parameters of the communication signal.
In step S5, the method further includes:
and searching a spike pulse signal in the square spectrum of the communication signal, and accurately correcting the carrier frequency and the communication code rate of the communication signal according to the position of the spike pulse signal.
The invention fully utilizes offshore test data to carry out detailed analysis and mathematical statistics on BPSK and QPSK, the recognition rate is better than 90%, and the parameter estimation error is less than 5%. The time domain waveform of the BPSK acoustic signal is shown in fig. 3, the time frequency waveform diagram is shown in fig. 4, the time domain waveform diagram of the QPSK acoustic signal is shown in fig. 5, and the time frequency waveform diagram is shown in fig. 6.
Example two
As shown in fig. 7, the present embodiment provides a system for identifying classes of phase-modulated signals and estimating parameters, including: the system comprises a signal noise separation module 1, a broadband communication signal detection and extraction module 2, a phase modulation signal classification and identification module 3, a phase modulation signal in-class identification module 4 and a communication signal parameter estimation module 5;
the signal noise separation module 1 is configured to separate a signal from noise for an input communication signal, perform framing processing on the communication signal, acquire and calculate a center frequency, a bandwidth and a signal-to-noise ratio of the communication signal of each frame, and combine three characteristic parameters including the center frequency, the bandwidth and the signal-to-noise ratio to realize accurate extraction of the communication signal under a noise background;
the broadband communication signal detection and extraction module 2 is used for calculating the center frequency change rate and the bandwidth change characteristic parameters of the signals aiming at the classification identification of the communication signals and other interference signals, and realizing the classification identification of the communication signals, narrow-band signals and frequency modulation signals of the broadband;
a phase modulation signal classification identification module 3, configured to calculate three feature parameters, namely symmetry of a frequency spectrum of the communication signal, a flat coefficient of the frequency spectrum, and a normalized signal envelope variance, for classification identification of a phase modulation signal and other signals in the communication signal, and identify the phase modulation signal in the multiple types of communication signals according to the three feature parameters;
the phase modulation signal in-class identification module 4 is configured to calculate a square spectrum of the phase modulation signal for the phase modulation signal in-class identification, and identify BPSK and QPSK signals according to a spectrum peak characteristic difference of the square spectrum;
and the communication signal parameter estimation module 5 is configured to perform parameter correction on the communication signal, and correct the center frequency, the bandwidth, the pulse width, and the code rate of the communication signal by combining the frequency spectrum and the square spectrum of the communication signal, so as to estimate a final parameter of the communication signal.
Further, the signal-noise separation module 1 further includes:
a framing processing unit 11, configured to perform framing processing on the communication signal, and collect the communication signal [ x (0), x (1), … x (N-1) with a length of N points]Calculating the frequency spectrum of the communication signal for each frame by means of a fast Fourier transform
Wherein W is a Fourier rotation factor;
a spectrum peak searching unit 12, configured to perform spectrum peak search for the frequency spectrum of the communication signal of each frame, find a maximum value of x (k), determine a position Index _ max of x (k), and calculate a bandwidth;
a signal energy calculating unit 13 for calculating signal energy within the bandwidth
Wherein, X is the frequency spectrum of the signal, Lb represents the lower frequency point of the signal bandwidth, and Lu represents the upper frequency point of the signal bandwidth.
Further, the broadband communication signal detection and extraction module 2 further includes:
a bandwidth variation statistical unit 21, configured to calculate a bandwidth of the communication signal for each frame of the communication signal, specifically: the bandwidth of the communication signal of the ith frame is as follows: delta _ fiCounting the bandwidth change of the communication signals of multiple frames according to Lu-Lb;
a center frequency variation statistical unit 22, configured to calculate a center frequency of the communication signal for each frame of the communication signal, specifically: the center frequency of the communication signal of the ith frame is as follows: fc (Fc)iCounting the change of the center frequency of the communication signals of multiple frames as Index _ max;
a center frequency modeling unit 23 for modeling the center frequency as a linear model, where Fc is k × Fci+ b, estimating the coefficient k, b by adopting a least square method;
further, the phase modulation signal classification and identification module 3 further includes:
a symmetry coefficient calculation unit 31 for calculating a symmetry coefficient K of the spectrumFThe characteristic parameters for characterizing symmetry of the communication signal spectrum with respect to the center frequency specifically include:
wherein E isbIs the lower band energy, EuIs the upper band energy, Index _ max is the center frequency of the signal, the spectrum is calculatedCoefficient of symmetry KFComprises the following steps:
KF＝Eb/Eu
the flat coefficient calculating unit 32 is configured to calculate a flat coefficient of a spectrum, which is a parameter for measuring energy distribution of the spectrum, and specifically includes:
further, the phase modulation signal class internal identification module 4 further includes:
a square spectrum calculating unit 41 for receiving the communication signal and performing frame division processing, and acquiring signals [ x (0), x (1), … x (N-1) with the length of N points]Calculating a squared signal of the communication signal as: y (n) ═ x2(n) calculating a squared spectrum of the communication signal as:
further, the communication signal parameter estimation module 5 further includes:
and a spike signal correction unit 51, configured to search for a spike signal in a square spectrum of the communication signal, and accurately correct the carrier frequency and the communication code rate of the communication signal according to a position of the spike signal.
The above description is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above embodiments, and all technical solutions belonging to the idea of the present invention belong to the protection scope of the present invention. It should be noted that modifications and embellishments within the scope of the invention may occur to those skilled in the art without departing from the principle of the invention, and are considered to be within the scope of the invention.
Claims (8)
1. A method for phase modulation signal classification identification and parameter estimation is characterized by comprising the following steps:
s1: separating signals and noise aiming at input communication signals, performing framing processing on the communication signals, acquiring and calculating the center frequency, bandwidth and signal-to-noise ratio of the communication signals of each frame, and combining three characteristic parameters including the center frequency, the bandwidth and the signal-to-noise ratio to realize accurate extraction of the communication signals under the noise background;
s2: aiming at the classification identification of the communication signal and other interference signals, calculating the central frequency change rate and bandwidth change characteristic parameters of the signals, and realizing the classification identification of the communication signal, the narrow-band signal and the frequency modulation signal of the broadband;
s3: aiming at the classification identification of a phase modulation signal and other signals in the communication signal, calculating three characteristic parameters of the symmetry of the frequency spectrum, the flat coefficient of the frequency spectrum and the normalized signal envelope variance of the communication signal, and identifying the phase modulation signal in various communication signals according to the three characteristic parameters;
s4: aiming at the phase modulation signal in-class identification, calculating a square spectrum of the phase modulation signal, and identifying BPSK and QPSK signals according to the spectral peak characteristic difference of the square spectrum;
s5: correcting parameters of the communication signals, correcting the center frequency, the bandwidth, the pulse width and the code rate of the communication signals by combining the frequency spectrum and the square spectrum of the communication signals, and estimating the final parameters of the communication signals;
in step S1, the method further includes:
s11: performing framing processing on the communication signal, and collecting the communication signal [ x (0), x (1), … x (N-1) with the length of N points]Calculating the frequency spectrum of the communication signal for each frame by means of a fast Fourier transform
Wherein W is a Fourier rotation factor, and N and N are integers of 0 or more;
s12: performing spectrum peak search on the frequency spectrum of the communication signal of each frame, searching the maximum value of X (k), determining the position Index _ max of the maximum value, and calculating the bandwidth;
s13: calculating signal energy within a bandwidth
Wherein, X is the frequency spectrum of the signal, Lb represents the lower frequency point of the signal bandwidth, and Lu represents the upper frequency point of the signal bandwidth.
2. The method for class identification and parameter estimation of phase-modulated signals according to claim 1, further comprising, in step S2:
s21: calculating a bandwidth of the communication signal for each frame of the communication signal, in particular: the bandwidth of the communication signal of the ith frame is as follows: delta _ fiCounting the bandwidth change of the communication signals of multiple frames according to Lu-Lb;
s22: calculating a center frequency of the communication signal for each frame of the communication signal, in particular: the center frequency of the communication signal of the ith frame is as follows: fc (Fc)iCounting the change of the center frequency of the communication signals of multiple frames as Index _ max;
s23: modeling is performed for the center frequency, and the modeling is a linear model, wherein Fc (alpha Fc)i+ b, estimating coefficients a, b of the linear model by using a least square method.
3. The method for class identification and parameter estimation of phase-modulated signal according to claim 1, further comprising calculating the symmetry of the spectrum of the communication signal and the flatness coefficient of the spectrum in step S3, specifically:
s31: coefficient of symmetry K of the spectrumFThe characteristic parameters are used for characterizing the symmetry of the communication signal spectrum about the center frequency, and specifically include:
wherein E isbIs the lower band energy, EuIs the upper frequency band energy, Index _ max is the center frequency of the signal, and the symmetry coefficient K of the frequency spectrum is calculatedFComprises the following steps:
KF＝Eb/Eu
s32: the flatness coefficient of the spectrum is a parameter for measuring the energy distribution of the spectrum, and specifically includes:
4. the method for class identification and parameter estimation of phase-modulated signals according to claim 1, wherein step S4 further comprises: calculating a square spectrum of the phase modulation signal, specifically:
s41: receiving the framing processing of the communication signal, collecting signals [ x (0), x (1), … x (N-1) ] with the length of N points, and calculating the square signal of the communication signal as:
y(n)＝x2(n)
s42: calculating a squared spectrum of the communication signal as:
5. the method for class identification and parameter estimation of phase-modulated signals according to claim 1, further comprising, in step S5:
and searching a spike pulse signal in the square spectrum of the communication signal, and accurately correcting the carrier frequency and the communication code rate of the communication signal according to the position of the spike pulse signal.
6. A system for classification identification and parameter estimation of phase modulated signals, comprising: the system comprises a signal noise separation module, a broadband communication signal detection and extraction module, a phase modulation signal classification and identification module, a phase modulation signal in-class identification module and a communication signal parameter estimation module;
the signal noise separation module is used for separating signals and noise aiming at input communication signals, performing framing processing on the communication signals, acquiring and calculating the center frequency, the bandwidth and the signal-to-noise ratio of the communication signals of each frame, and combining three characteristic parameters including the center frequency, the bandwidth and the signal-to-noise ratio to realize accurate extraction of the communication signals under a noise background;
the broadband communication signal detection and extraction module is used for calculating the central frequency change rate and the bandwidth change characteristic parameters of the signals aiming at the classification identification of the communication signals and other interference signals, and realizing the classification identification of the communication signals, narrow-band signals and frequency modulation signals of the broadband;
the phase modulation signal classification identification module is used for calculating three characteristic parameters of the symmetry of a frequency spectrum, a flat coefficient of the frequency spectrum and normalized signal envelope variance of the communication signal aiming at the classification identification of the phase modulation signal and other signals in the communication signal, and identifying the phase modulation signal in various communication signals according to the three characteristic parameters;
the phase modulation signal in-class identification module is used for calculating a square spectrum of the phase modulation signal aiming at the phase modulation signal in-class identification, and identifying BPSK and QPSK signals according to the difference of spectral peak characteristics of the square spectrum;
a communication signal parameter estimation module, configured to perform parameter correction on the communication signal, and combine the frequency spectrum and the square spectrum of the communication signal to correct the center frequency, the bandwidth, the pulse width, and the code rate of the communication signal, so as to estimate a final parameter of the communication signal; the signal noise separation module further includes:
a framing processing unit for framing the communication signal and collecting the communication signal [ x (0), x (1), … x (N-1) with length of N points]Calculating the frequency spectrum of the communication signal for each frame by means of a fast Fourier transform
Wherein W is a Fourier rotation factor, N and N are greater than or equal toAn integer at 0;
a spectral peak searching unit, configured to perform spectral peak search for the frequency spectrum of the communication signal of each frame, find a maximum value of x (k), determine a position Index _ max thereof, and calculate a bandwidth;
a signal energy calculating unit for calculating signal energy within the bandwidth
Wherein, X is the frequency spectrum of the signal, Lb represents the lower frequency point of the signal bandwidth, and Lu represents the upper frequency point of the signal bandwidth.
7. The system for class identification and parameter estimation of phase modulated signals according to claim 6, wherein said wideband communication signal detection extraction module further comprises:
a bandwidth variation statistics unit, configured to calculate, for each frame of the communication signal, a bandwidth of the communication signal, specifically: the bandwidth of the communication signal of the ith frame is as follows: delta _ fiCounting the bandwidth change of the communication signals of multiple frames according to Lu-Lb;
a center frequency variation statistical unit for calculating a center frequency of the communication signal for each frame of the communication signal, specifically: the center frequency of the communication signal of the ith frame is as follows: fc (Fc)iCounting the change of the center frequency of the communication signals of multiple frames as Index _ max;
a center frequency modeling unit for modeling aiming at the center frequency, wherein the modeling is a linear model, and Fc (Fc ═ aFc)i+ b, estimating coefficients a and b of the linear model by adopting a least square method;
the phase modulation signal classification and identification module further comprises:
a symmetry coefficient calculation unit for calculating a symmetry coefficient K of the frequency spectrumFThe characteristic parameters for characterizing symmetry of the communication signal spectrum with respect to the center frequency specifically include:
wherein E isbIs the lower band energy, EuIs the upper frequency band energy, Index _ max is the center frequency of the signal, and the symmetry coefficient K of the frequency spectrum is calculatedFComprises the following steps:
KF＝Eb/Eu
the flat coefficient calculation unit is used for calculating a flat coefficient of a frequency spectrum, is a parameter for measuring the energy distribution of the frequency spectrum, and specifically comprises the following steps:
8. the system for class identification and parameter estimation of phase modulated signals according to claim 6, wherein said phase modulated signal class identification module further comprises:
a square spectrum calculating unit for receiving the communication signal and performing frame division processing to acquire signals [ x (0), x (1), … x (N-1) with the length of N points]Calculating a squared signal of the communication signal as: y (n) ═ x2(n) calculating a squared spectrum of the communication signal as:
the communication signal parameter estimation module further comprises:
and the spike signal correction unit is used for searching a spike signal in a square spectrum of the communication signal and accurately correcting the carrier frequency and the communication code rate of the communication signal according to the position of the spike signal.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010484531.8A CN111585662B (en) | 2020-06-01 | 2020-06-01 | Classification identification and parameter estimation method and system for phase modulation signal |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010484531.8A CN111585662B (en) | 2020-06-01 | 2020-06-01 | Classification identification and parameter estimation method and system for phase modulation signal |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111585662A CN111585662A (en) | 2020-08-25 |
| CN111585662B true CN111585662B (en) | 2021-09-07 |
Family
ID=72116081
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010484531.8A Active CN111585662B (en) | 2020-06-01 | 2020-06-01 | Classification identification and parameter estimation method and system for phase modulation signal |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111585662B (en) |
Families Citing this family (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111814777B (en) * | 2020-09-15 | 2020-12-08 | 湖南国科锐承电子科技有限公司 | Modulation pattern recognition method based on characteristic quantity grading |
| CN114826850B (en) * | 2022-04-21 | 2022-11-25 | 中国人民解放军国防科技大学 | Modulation identification method, device and equipment based on time-frequency diagram and deep learning |
| CN115065580B (en) * | 2022-07-28 | 2024-04-02 | 成都华日通讯技术股份有限公司 | Link16 data Link identification and parameter estimation method under broadband |
| CN116166935B (en) * | 2023-04-26 | 2023-08-25 | 中铁第一勘察设计院集团有限公司 | Multi-source background signal identification method and device for railway communication |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106059969A (en) * | 2016-06-16 | 2016-10-26 | 中国科学院信息工程研究所 | Modulation identification method and device based on envelope square spectrum analysis |
| US9516490B1 (en) * | 2016-04-20 | 2016-12-06 | Tm Ip Holdings, Llc | Identifying devices with transpositional modulation |
| CN107124381A (en) * | 2017-03-27 | 2017-09-01 | 华南理工大学 | A kind of Digital Communication Signal Modulation automatic identifying method |
| CN107561497A (en) * | 2017-07-27 | 2018-01-09 | 中国船舶重工集团公司第七二四研究所 | FSK and the identification of a variety of NLFM signals and parameter evaluation method |
| CN107948107A (en) * | 2017-11-16 | 2018-04-20 | 成都玖锦科技有限公司 | A kind of digital modulation signals sorting technique based on union feature |
| CN108683622A (en) * | 2018-05-22 | 2018-10-19 | 电子科技大学 | A kind of Modulation Identification method for composing flat coefficient based on signal square |
-
2020
- 2020-06-01 CN CN202010484531.8A patent/CN111585662B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9516490B1 (en) * | 2016-04-20 | 2016-12-06 | Tm Ip Holdings, Llc | Identifying devices with transpositional modulation |
| CN106059969A (en) * | 2016-06-16 | 2016-10-26 | 中国科学院信息工程研究所 | Modulation identification method and device based on envelope square spectrum analysis |
| CN107124381A (en) * | 2017-03-27 | 2017-09-01 | 华南理工大学 | A kind of Digital Communication Signal Modulation automatic identifying method |
| CN107561497A (en) * | 2017-07-27 | 2018-01-09 | 中国船舶重工集团公司第七二四研究所 | FSK and the identification of a variety of NLFM signals and parameter evaluation method |
| CN107948107A (en) * | 2017-11-16 | 2018-04-20 | 成都玖锦科技有限公司 | A kind of digital modulation signals sorting technique based on union feature |
| CN108683622A (en) * | 2018-05-22 | 2018-10-19 | 电子科技大学 | A kind of Modulation Identification method for composing flat coefficient based on signal square |
Non-Patent Citations (1)
| Title |
|---|
| 水声信号的调制制式识别研究;万莎;《厦门大学工程硕士学位论文》;20170630;第一章、第三至四章 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111585662A (en) | 2020-08-25 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111585662B (en) | Classification identification and parameter estimation method and system for phase modulation signal | |
| CN109495410B (en) | High dynamic PCM/FM signal carrier frequency accurate estimation method | |
| CN106130942B (en) | A kind of wireless communication signals Modulation Identification and method for parameter estimation based on Cyclic Spectrum | |
| CN108243130B (en) | Demodulation method, demodulation device, spectrum detector and computer readable storage medium | |
| CN112511477A (en) | Hybrid satellite communication modulation identification method and system based on constellation diagram and deep learning | |
| CN105071830B (en) | A kind of detection recognition method of direct sequence signal | |
| CN106357575A (en) | Multi-parameter jointly-estimated interference type identification method | |
| CN105785324A (en) | MGCSTFT-based chirp signal parameter estimation method | |
| CN111059066B (en) | Centrifugal pump cavitation state discrimination method based on autocorrelation spectrum and balanced square envelope spectrum | |
| CN102006252B (en) | Single-tone signal identification method | |
| CN101588191A (en) | Method and device for radio signal recognition | |
| Ming et al. | Intrapulse modulation recognition of radar signals based on statistical tests of the time-frequency curve | |
| CN116804736A (en) | Modulation type identification engineering realization method and computer readable storage medium | |
| CN112033656A (en) | Mechanical system fault detection method based on broadband spectrum processing | |
| CN102307166B (en) | SNR (signal to noise ratio) estimation method | |
| CN109660475B (en) | A kind of non-cooperation phase code water sound communication signal autonomous identifying method | |
| CN116961799A (en) | Signal interference detection method based on time-frequency domain distribution characteristics | |
| CN115659136A (en) | Wireless interference signal waveform identification method based on neural network | |
| CN115396270B (en) | Frequency modulation signal time-frequency synchronization system and method based on data type identification | |
| CN110190917A (en) | A kind of frequency spectrum cavity-pocket cognitive method, device and the equipment of LTE230MHz electric power wireless private network | |
| CN112202696B (en) | Underwater sound signal automatic modulation identification method based on fuzzy self-encoder | |
| CN112838909B (en) | Communication interference detection method based on Gaussian eye pattern texture entropy characteristics | |
| CN113824518A (en) | Non-cooperative signal detection method based on frequency spectrum | |
| Batra et al. | A Study of Automatic Modulation Classification for Rayleigh Fading Channel | |
| CN108243131A (en) | Demodulation method, device, spectrum detector and computer read/write memory medium |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |