CN115563481A - Gravitational wave signal classification and identification method and system based on image domain features - Google Patents
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Abstract
The invention discloses a gravitational wave signal classification and identification method and a system based on image domain characteristics.A continuous gravitational wave is separated by the number of connected components of a received signal power spectrogram; secondly, separating out random gravitational waves through the number of connected components of the autocorrelation function graph; and finally, separating out the Chirp gravitational wave by calculating the number of connected components of the time-lag product spectrum modulus diagram of the received signal and the delayed signal thereof. The simulation result shows that: when the signal-to-noise ratio is not lower than-3 dB, the average recognition rate of the algorithm can reach more than 95%, and the algorithm is low in implementation complexity and has a certain engineering application value.
Description
Technical Field
The invention relates to a gravitational wave signal classification and identification method and system based on image domain characteristics, and belongs to the technical field of signal processing.
Background
The gravitational wave is a 'space-time ripple' generated by collision of black holes, neutron stars and the like, and is an invisible wave. Since gravitational waves attenuate much less than electromagnetic waves during propagation and provide information that cannot be obtained by other means such as electromagnetic waves, how to efficiently analyze gravitational waves based on observed gravitational wave data has become an important issue in the related art. Analysis for gravitational wave data includes two tasks, detection and parameter estimation.
Since gravitational waves have weak interaction, their detection is also difficult. Currently, the detection of gravitational wave signals can be classified into energy detection, matched filter detection and feature detection based on deep learning. The energy detection method is used for detecting and judging the energy of a signal as statistic to realize the detection of gravitational wave signals, but the uncertainty of noise variance can greatly reduce the energy detection performance, and the method can only detect whether a signal to be detected exists in a target frequency band and cannot identify the specific type of the signal. The matched filter can optimally judge whether the signal to be detected exists, but has higher requirements on timing and frequency synchronization, and meanwhile, the prior information of the signal to be detected must be acquired, so that the method is not easy to realize. The feature detection method based on deep learning can automatically distinguish and detect gravitational waves, but has the disadvantage that a large amount of data needs to be trained in advance.
At present, typical gravitational wave signals mainly include continuous gravitational waves, chirp gravitational waves, burst type gravitational waves and random gravitational waves. The continuous gravitational wave is from collision and combination of two black holes, is a periodic signal with longer duration, has the sine wave characteristic that the amplitude and the frequency are close to constant, and has the main parameters of amplitude, phase and frequency; the Chirp gravitational wave comes from double-neutron star convolute, the frequency is approximately linear frequency modulation characteristic, and the main parameters are starting frequency, end point frequency, frequency modulation slope, bandwidth and the like; the burst type gravitational wave comes from a nuclear collapse supernova and has discontinuity and mutability; random gravitational waves generally result from the superposition of unresolved signals from many independent sources, with waveforms exhibiting randomness, no deterministic law, and noise-like behavior. The gravitational waves of different types come from different stars, and the external forms and parameters are different. In order to further acquire more accurate information of the gravitational wave signal, the parameter of the gravitational wave signal needs to be estimated, and the identification of the gravitational wave signal is a precondition for parameter estimation. However, at present, only the study on the glotch signal classification algorithm is conducted on classification and identification of gravitational wave signals, and documents on classification algorithms of gravitational wave real signals are not available.
Disclosure of Invention
The invention provides a method and a system for classifying and identifying gravitational wave signals based on image domain characteristics, aiming at the classification problems of continuous, chirp, burst and random four types of gravitational wave signals under a noise background.
In order to solve the technical problem, the invention provides a gravitational wave signal classification and identification method based on image domain characteristics, which comprises the following steps:
(1) Calculating the power spectrum of the signal to be identified, normalizing and quantizing the power spectrum, converting the normalized and quantized power spectrum into a graph, and calculating the number C of connected components of the graph 1 The number of connected components C of the graph 1 With a preset threshold η 1 Making a comparison if C 1 ＞η 1 Judging the wave to be a continuous gravitational wave; if C 1 ≤η 1 Entering the step (2);
(2) Calculating the autocorrelation function of the signal to be identified, normalizing and quantizing the autocorrelation function, converting the normalized and quantized autocorrelation function into a graph, and calculating the number C of connected components of the graph 2 The number of connected components C of the graph 2 With a preset threshold η 2 Making a comparison if C 2 ＞η 2 Judging the wave to be a random gravitational wave; if C 2 ≤η 2 Entering the step (3);
(3) Multiplying a signal to be identified with a delay signal thereof to obtain a time lag product, obtaining a time lag frequency spectrum module value according to the time lag product, finding out a maximum value of the time lag product frequency spectrum module value, taking data from a position of the maximum value to an end point of a time lag product frequency spectrum module value data section, and normalizing the data from the position of the maximum value to the end point of the time lag product frequency spectrum module value data sectionAnd quantization processing, converting the normalized and quantized data into a graph, and calculating the number C of connected components of the graph 3 The number of connected components C of the graph 3 With a preset threshold η 3 Making a comparison if C 3 ＞η 3 Judging the wave to be Chirp gravitational wave; if C 3 ≤η 3 Then, it is judged as a burst-type gravitational wave.
Further, the step (1) includes:
(1.1) the signal to be identified is represented as X (n) = s (n) + w (n), wherein s (n) and omega (n) are respectively a gravitational wave signal and complex white Gaussian noise, M-point fast Fourier transform is carried out on X (n) and the square of a module of X (n) is taken to obtain a power spectrum X (M) of the signal to be identified, and the power spectrum X (M) is represented as
In the formula, N is the serial number of a sample point of a signal to be identified, M is the serial number of a point of Fourier transform, N is the total number of the sample points of the signal to be identified, and M represents the number of the fast Fourier transform points;
(1.2) normalizing and uniformly quantizing the power spectrum X (m), and converting the quantized normalized power spectrum into a graph, wherein the normalized power spectrum comprises the following steps:
the maximum and minimum amplitudes of X (m) are respectively
The normalized signal spectrum U X(m) Comprises the following steps:
let the quantization level be γ, a set of discrete amplitudes {1/γ,2/γ, \ 82301 } is obtained, whereby the normalized power spectrum amplitude is rounded down to the nearest quantization value, and the normalized power spectrum Q is quantized X(m) Is composed of
Normalizing the power spectrum Q after quantization X(m) Is the vertex of gamma quantization values
Defining the connection line of each vertex as the edge of the graph, the set of the edges of the graph is
Wherein
Obtaining a map-domain representation of the power spectrum of the signal, G = (V, E);
Δ Υ means to perform a gamma-level quantization of the normalized power spectral amplitude, v γ Representing the gamma vertices, e, obtained after the quantized data has been converted into a graph δ,β Representing the edges obtained after the quantized data has been converted into a graph, δ and β representing the edge e δ,β Corresponding two vertices, N γ Representing the set to which the vertex belongs,
v, E represent the vertex and edge of the graph respectively;
(1.3) calculating the Laplace matrix L (G) of the plot, expressed as:
L(G)＝(l ij ) γ×γ ，
in the formula (I), the compound is shown in the specification,
deg(v i ) Obtaining a characteristic value through characteristic decomposition for the degree of the vertex, and further obtaining the number C of the connected components according to the number of zero characteristic values in the characteristic value 1 (ii) a i and j each indicate a vertex number.
Further, the step (2) includes:
(2.1) performing mean value removing processing on the x (n) to obtain a mean value removing processing result y (n), wherein the mean value removing processing result y (n) is expressed as:
in the formula (I), the compound is shown in the specification,
represents the mean of x (n);
solving the autocorrelation function r of y (n) yy (u), expressed as:
wherein N is the length of the signal to be identified, y * (n-u) represents the conjugation of y (n) shifted by u;
(2.2) applying the method of step (1.2) to the autocorrelation function r yy (u) normalizing and uniformly quantizing, and converting the quantized normalized autocorrelation function into a graph;
(2.3) calculating the number C of connected components of the graph of the autocorrelation function by adopting the method in the step (1.3) 2 。
Further, the step (3) includes:
(3.1) setting the delay v to
The delay signal of x (n) is denoted as x * (n-ν)；
Calculating the product of x (n) and its delayed signal to obtain time lag product
r(n)＝x(n)x * (n-ν)
(3.2) obtaining a spectral modulus R (m) of the time lag product, which is expressed as:
wherein, L is the effective length of r (n), and L represents the time lag product frequency spectrum module value serial number;
finding out the position n corresponding to the maximum value in the time lag product spectrum modulus 1 Taking out n 1 Data between the end points of the time lag product frequency spectrum module value data section and adopting the method of step (1.2) to convert n 1 Normalizing and uniformly quantizing the data between the end points of the time-lag product frequency spectrum module value data segments, and converting the quantized normalized frequency spectrum into a graph;
(3.3) calculating the number C of connected components of the quantized graph of the normalized frequency spectrum by adopting the method in the step (1.3) 3 。
A gravitational wave signal classification and identification system based on map domain features comprises:
a first identification module for calculating the power spectrum of the signal to be identified, normalizing and quantizing the power spectrum, converting the normalized and quantized power spectrum into a graph, and calculating the number C of connected components in the graph 1 The number of connected components C of the graph 1 With a preset threshold η 1 Making a comparison if C 1 ＞η 1 Judging the wave to be a continuous gravitational wave; if C 1 ≤η 1 Entering a second identification module;
a first identification module for calculating the autocorrelation function of the signal to be identified, normalizing and quantizing the autocorrelation function, converting the normalized and quantized autocorrelation function into a graph, and calculating the number C of connected components in the graph 2 The number of connected components C of the graph 2 With a preset threshold η 2 Making a comparison if C 2 ＞η 2 Judging the wave to be a random gravitational wave; if C 2 ≤η 2 Entering a third identification module;
the third identification module is used for multiplying the signal to be identified and the delay signal thereof to obtain a time lag product, obtaining a time lag frequency spectrum module value according to the time lag product, finding out a maximum value of the time lag product frequency spectrum module value, taking data from a maximum value position to an end point of a time lag product frequency spectrum module value data section, carrying out normalization and quantization processing on the data from the maximum value position to the end point of the time lag product frequency spectrum module value data section, converting the normalized and quantized data into a graph, and calculating the number C of connected components of the graph 3 Number of connected components C of the graph 3 With a preset threshold η 3 Make a comparisonIf C is 3 ＞η 3 Judging the wave to be a Chirp gravitational wave; if C 3 ≤η 3 Then, it is judged as a burst-type gravitational wave
A computer readable storage medium storing one or more programs, wherein the one or more programs comprise instructions, which when executed by a computing device, cause the computing device to perform any of the methods.
A computing device, comprising, in combination,
one or more processors, memory, and one or more programs stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for performing any of the methods.
The invention achieves the following beneficial effects:
firstly, separating out continuous gravitational waves by calculating the number of connected components of a received signal power spectrogram; secondly, separating out random gravitational waves by calculating the number of connected components of the autocorrelation function diagram; and finally, separating out the Chirp gravitational wave by calculating the number of connected components of a frequency spectrum modulus graph of the time lag product of the received signal and the delay signal thereof. The method has better performance when the signal-to-noise ratio is not lower than-3 dB. Has certain engineering application prospect.
Drawings
FIG. 1 is a flow chart of a gravitational wave signal classification and identification algorithm based on map domain features;
FIG. 2 (a) is a graph domain feature of a random gravitational wave signal when screening continuous gravitational waves;
FIG. 2 (b) is a diagram domain feature of continuous gravitational wave signal in screening continuous gravitational wave;
fig. 2 (c) is a graph domain characteristic of a Chirp type gravitational wave signal when a continuous type gravitational wave is screened;
FIG. 2 (d) is a graph domain feature of a burst-type gravitational wave signal when screening continuous gravitational waves;
FIG. 3 (a) is a graph domain feature of a random gravitational wave signal in the screening of random gravitational waves;
FIG. 3 (b) is a graph domain characteristic of a Chirp type gravitational wave signal when a random type gravitational wave is screened;
FIG. 3 (c) is a graph domain feature of a burst-type gravitational wave signal when a random gravitational wave is screened;
fig. 4 (a) is a graph domain characteristic of a Chirp type gravitational wave signal when the Chirp type gravitational wave is screened;
fig. 4 (b) is a graph domain characteristic of a Chirp type gravitational wave signal when a Chirp type gravitational wave is screened.
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.
The invention relates to a gravitational wave signal classification and identification method based on image domain characteristics, which comprises the steps of firstly, separating continuous gravitational waves by calculating the number of connected components of a received signal power spectrogram; secondly, separating out random gravitational waves by calculating the number of connected components of the autocorrelation function diagram; and finally, distinguishing the Chirp gravitational wave and the burst type gravitational wave by calculating the number of connected components of a frequency spectrum modulus value graph of the time lag product of the received signal and the delay signal thereof.
Referring to the attached fig. 1, a flowchart of a gravitational wave signal classification and identification algorithm based on map domain features is shown, and the specific process includes the following steps:
the step (1) specifically comprises:
(1.1) setting a received signal as x (n) = s (n) + w (n), wherein s (n) and omega (n) are respectively a gravitational wave signal and complex white Gaussian noise; performing Fast Fourier Transform (FFT) on x (n) and taking the square of the Fast Fourier Transform (FFT) to obtain a power spectrum of
In the formula, N is the serial number of a sample point of a signal to be identified, M is the serial number of a point of Fourier transform, N is the total number of the sample points of the signal to be identified, and M represents the number of the points of fast Fourier transform.
And (1.2) normalizing and uniformly quantizing the power spectrum, and converting the quantized normalized power spectrum into a graph.
Suppose that the maximum and minimum amplitudes of X (m) are respectively
The normalized signal spectrum is
If the quantization level is γ, then a set of discrete amplitudes {1/γ,2/γ,. 1} is obtained, so that the normalized power spectral amplitude is rounded up to the nearest quantized value. The quantized normalized power spectrum is
Taking gamma quantized values of the quantized normalized power spectrum as vertexes
Defining each vertex connecting line as the edge of the graph, the set of the edge of the graph is
Wherein
A map-domain representation of the power spectrum of the signal G = (V, E) may thus be obtained.
Δ Υ Means that the normalized power spectral amplitude is quantized in gamma-order, v γ Representing the gamma vertices, e, obtained after the quantized data has been converted into a graph δ,β Representing the edges obtained after the quantized data has been converted into a graph, δ and β representing the edge e δ,β Two corresponding vertexes, N γ Representing the set to which the vertex belongs,
v, E represent the vertices and edges of the graph, respectively.
(1.3) Laplace matrix of computation graph
L(G)＝(l ij ) γ×γ ，
In the formula (I), the compound is shown in the specification,
deg(v i ) Is the degree of the vertex. Obtaining characteristic values through characteristic decomposition, and further obtaining the number C of connected components according to the number of zero characteristic values 1 I, j respectively represent vertex sequence numbers; the threshold η is then empirically set 1 And subtracting 4 from the fixed point number of the graph, and finally comparing the number of the connected components with a threshold value to judge whether the connected components are continuous gravitational waves or not. If so C 1 ＞η 1 Judging the wave to be a continuous gravitational wave; if C 1 ≤η 1 Step (2) is entered.
The step (2) specifically comprises:
and (2.1) carrying out mean value removing processing on the received signals, and then calculating the autocorrelation function of the received signals.
Performing mean value removing treatment on the x (n) to obtain
In the formula (I), the compound is shown in the specification,
represents the mean of x (n).
Solving the autocorrelation function of y (n) as
Where N is the received signal length, y * (n-u) represents the conjugation of y (n) shifted by u.
And (2.2) normalizing and uniformly quantizing the autocorrelation function, and converting the quantized normalized autocorrelation function into a graph. The concrete method is consistent with the step (1.2) (let X (m)) Is replaced by r yy (u))。
(2.3) calculating the number C of connected components of the graph of the autocorrelation function according to the step (1.3) 2 (the graph of the power spectrum is replaced by a graph of the autocorrelation function), and then a threshold η is set 2 And subtracting 4 from the fixed point number of the graph, and finally comparing the number of the connected components with a threshold value to judge whether the connected components are random gravitational waves. I.e. if C 2 ＞η 2 Judging the wave to be a random gravitational wave; if C 2 ≤η 2 Then step (3) is entered.
The step (3) specifically comprises:
(3.1) setting the delay to
The delay signal of x (n) is denoted as x * (n-ν)；
Calculating the product of x (n) and its delayed signal, the instantaneous product of hysteresis being
r(n)＝x(n)x * (n-ν)
The frequency spectrum module value of the time lag product is calculated,
wherein L is the effective length of r (n). Finding out the position n corresponding to the maximum value in the time lag product frequency spectrum module value 1 Taking out n 1 Data to the end of the data segment. Normalizing and uniformly quantizing the spectrum, and converting the quantized normalized spectrum into a graph in the same step (1.2) (replacing X (m) with n) 1 Data to the end of the data segment).
(3.3) calculating the number C of connected components of the graph according to the step (1.3) 3 (the graph of the power spectrum is replaced by the graph of the quantized normalized spectrum), and then a threshold η is set 3 Subtracting 4 from the fixed point number of the graph, and finally, calculating the number of connected components of the graph and eta 3 And comparing and judging whether the received signals are Chirp gravitational waves. If so C 3 ＞η 3 Judging the wave to be a Chirp gravitational wave; if C 3 ≤η 3 Then, it is judged as a burst-type gravitational wave.
Referring to fig. 2 (a) to 2 (d), fig. 3 (a) to 3 (c), and fig. 4 (a) to 4 (b), graph characteristics of various gravitational wave signals when various gravitational wave signals are screened under the condition of SNR =0dB are shown.
The graph features of the power spectrums of the four gravitational wave signals during the continuous gravitational wave screening are shown in fig. 2 (a) -2 (d), and it can be seen that the power spectrum of the continuous gravitational wave has the most connected components, and the number of the connected components of the other three signals is far less than that of the continuous gravitational wave. By using this feature, if the threshold is set to subtract 4 from the number of vertices in the graph, the continuous gravitational wave signal can be screened out.
The domain features of the autocorrelation function of various gravitational wave signals when screening random gravitational waves are shown in fig. 3 (a) to 3 (c). Therefore, the random gravitational wave signal can be screened by setting a threshold value to be the vertex number of the graph minus 4.
Time-lag product spectrum modulus domain features of two signals and delay signals of the signals when a Chirp type gravitational wave and a burst type gravitational wave are distinguished are shown in fig. 4 (a) -4 (b), and therefore the number of connected components of the signals and the delay signals is obviously different, and the two gravitational waves can be distinguished through the statistic.
Table 1 shows performance statistics of the gravitational wave signal classification and identification algorithm based on the map domain features. In simulation, the received signals are respectively random type, continuous type, chirp type and burst type gravitational wave signals, SNR =3dB,0dB and-3 dB, and the result is the percentage of correct judgment after each received signal is subjected to 100 times of simulation respectively. It can be seen that, although the percentage of correct judgment is reduced with the reduction of low signal-to-noise ratio, when the signal-to-noise ratio is not lower than-3 dB, the overall correct recognition rate of the method is more than 95%, and the method has better robustness.
TABLE 1
Correspondingly, the invention also provides a system for classifying and identifying the gravitational wave signals based on the map domain characteristics, which comprises the following steps:
a first identification module for calculating the power spectrum of the signal to be identified, normalizing and quantizing the power spectrum, converting the normalized and quantized power spectrum into a graph, and calculating the number C of connected components in the graph 1 The number of connected components C of the graph 1 With a preset threshold η 1 Making a comparison if C 1 ＞η 1 Judging the wave to be a continuous gravitational wave; if C 1 ≤η 1 Entering a second identification module;
a first identification module for calculating the autocorrelation function of the signal to be identified, normalizing and quantizing the autocorrelation function, converting the normalized and quantized autocorrelation function into a graph, and calculating the number C of connected components in the graph 2 The number of connected components C of the graph 2 With a preset threshold η 2 Making a comparison if C 2 ＞η 2 Judging the wave to be a random gravitational wave; if C 2 ≤η 2 Entering a third identification module;
the third identification module is used for multiplying the signal to be identified and the delayed signal thereof to obtain a time lag product, obtaining a time lag spectrum module value according to the time lag product, finding out the maximum value of the time lag product spectrum module value, taking data from the position of the maximum value to the end point of the time lag product spectrum module value data section, normalizing and quantizing the data from the position of the maximum value to the end point of the time lag product spectrum module value data section, converting the normalized and quantized data into a graph, and calculating the number C of connected components of the graph 3 The number of connected components C of the graph 3 With a preset threshold η 3 Making a comparison if C 3 ＞η 3 Judging the wave to be Chirp gravitational wave; if C 3 ≤η 3 Then, it is judged as a burst-type gravitational wave
The present invention accordingly also provides a computer readable storage medium storing one or more programs, wherein the one or more programs include instructions, which when executed by a computing device, cause the computing device to perform any of the methods described.
The invention also provides a computing device, comprising,
one or more processors, memory, and one or more programs stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for performing any of the methods.
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 (7)
1. A gravitational wave signal classification and identification method based on image domain features is characterized by comprising the following steps:
(1) Calculating the power spectrum of the signal to be identified, normalizing and quantizing the power spectrum, converting the normalized and quantized power spectrum into a graph, and calculating the number C of connected components in the graph 1 The number of connected components C of the graph 1 With a preset threshold η 1 Making a comparison if C 1 ＞η 1 Judging the wave to be a continuous gravitational wave; if C 1 ≤η 1 Entering the step (2);
(2) Calculating the autocorrelation function of the signal to be identified, normalizing and quantizing the autocorrelation function, converting the normalized and quantized autocorrelation function into a graph, and calculating the number C of connected components of the graph 2 Number of connected components C of the graph 2 With a preset threshold η 2 Making a comparison if C 2 ＞η 2 Judging the wave to be a random gravitational wave; if C 2 ≤η 2 Entering the step (3);
(3) Multiplying the signal to be identified with the delayed signal to obtain a time lag product, obtaining a time lag frequency spectrum module value according to the time lag product, finding out the maximum value of the time lag product frequency spectrum module value, and taking the maximum value position to the time lag product frequencyNormalizing and quantizing the data between the end points of the spectrum module value data segment from the maximum position to the end points of the time lag product spectrum module value data segment, converting the normalized and quantized data into a graph, and calculating the number C of connected components of the graph 3 Number of connected components C of the graph 3 With a preset threshold η 3 Making a comparison if C 3 ＞η 3 Judging the wave to be a Chirp gravitational wave; if C 3 ≤η 3 Then, it is judged as a burst-type gravitational wave.
2. The gravitational wave signal classification and identification method based on map domain features as claimed in claim 1, wherein said step (1) comprises:
(1.1) the signal to be identified is represented as X (n) = s (n) + w (n), wherein s (n) and omega (n) are respectively a gravitational wave signal and complex white Gaussian noise, M-point fast Fourier transform is carried out on X (n) and the square of a module of X (n) is taken to obtain a power spectrum X (M) of the signal to be identified, and the power spectrum X (M) is represented as
In the formula, N is the serial number of a sample point of a signal to be identified, M is the serial number of a point of Fourier transform, N is the total number of the sample points of the signal to be identified, and M represents the number of the fast Fourier transform points;
(1.2) normalizing and uniformly quantizing the power spectrum X (m), and converting the quantized normalized power spectrum into a graph, wherein the graph comprises the following steps:
the maximum and minimum amplitudes of X (m) are respectively
The normalized signal spectrum U X(m) Comprises the following steps:
if the quantization level is gamma, a group of discrete amplitudes { 1/gamma, 2/gamma, \8230isobtained1, such that the normalized power spectrum amplitude is rounded quantized to the nearest quantized value, the quantized normalized power spectrum Q X(m) Is composed of
Normalizing the power spectrum Q after quantization X(m) Is the vertex of the gamma quantized values
Defining each vertex connecting line as the edge of the graph, the set of the edge of the graph is
Wherein
Obtaining a map-domain representation of the power spectrum of the signal G = (V, E);
Δ Υ means that the normalized power spectral amplitude is quantized in gamma-order, v γ Representing the gamma vertices, e, obtained after the quantized data has been converted into a graph δ,β Representing the edges obtained after the quantized data has been converted into a graph, δ and β representing the edge e δ,β Corresponding two vertices, N γ Represents the set to which the vertex belongs to,
v, E represent the vertex and edge of the graph respectively;
(1.3) calculating the Laplace matrix L (G) of the graph, expressed as:
L(G)＝(l ij ) γ×γ ，
in the formula (I), the compound is shown in the specification,
deg(v i ) Obtaining a characteristic value through characteristic decomposition for the degree of the vertex, and further obtaining the number C of connected components according to the number of zero characteristic values in the characteristic value 1 (ii) a i and j each indicate a vertex number.
3. The gravitational wave signal classification and identification method based on map domain features as claimed in claim 2, wherein said step (2) comprises:
(2.1) performing mean value removing processing on the x (n) to obtain a mean value removing processing result y (n), wherein the mean value removing processing result y (n) is expressed as:
in the formula (I), the compound is shown in the specification,
represents the mean of x (n);
solving the autocorrelation function r of y (n) yy (u), expressed as:
wherein N is the length of the signal to be identified, y * (n-u) represents the conjugation of y (n) shifted by u;
(2.2) applying the method of step (1.2) to the autocorrelation function r yy (u) normalizing and uniformly quantizing, and converting the quantized normalized autocorrelation function into a graph;
(2.3) calculating the number C of connected components of the graph of the autocorrelation function by adopting the method in the step (1.3) 2 。
4. The gravitational wave signal classification and identification method based on map domain features as claimed in claim 2, wherein said step (3) comprises:
(3.1) setting the delay v to
Delay of x (n)The signal is represented as x * (n-ν)；
Calculating the product of x (n) and its delayed signal to obtain time lag product
r(n)＝x(n)x * (n-ν)
(3.2) obtaining a spectral modulus R (m) of the time lag product, which is expressed as:
wherein L is the effective length of r (n), and L represents the time-lag product frequency spectrum module value serial number;
finding out the position n corresponding to the maximum value in the time lag product frequency spectrum module value 1 Taking out n 1 Data between the end points of the time lag product frequency spectrum module value data section and adopting the method of step (1.2) to convert n 1 Normalizing and uniformly quantizing the data between the end points of the time-lag product frequency spectrum module value data segments, and converting the quantized normalized frequency spectrum into a graph;
(3.3) calculating the number C of connected components of the quantized graph of the normalized frequency spectrum by adopting the method in the step (1.3) 3 。
5. A gravitational wave signal classification and identification system based on map domain features is characterized by comprising:
a first identification module for calculating the power spectrum of the signal to be identified, normalizing and quantizing the power spectrum, converting the normalized and quantized power spectrum into a graph, and calculating the number C of connected components in the graph 1 The number of connected components C of the graph 1 With a preset threshold η 1 Making a comparison if C 1 ＞η 1 Judging the wave to be a continuous gravitational wave; if C 1 ≤η 1 Entering a second identification module;
a first identification module for calculating autocorrelation function of the signal to be identified, normalizing and quantizing the autocorrelation function, converting the normalized and quantized autocorrelation function into a graph, and calculating the number C of connected components in the graph 2 Number of connected components C of the graph 2 With a preset threshold η 2 Making a comparison if C 2 ＞η 2 Judging the wave to be a random gravitational wave; if C 2 ≤η 2 Entering a third identification module;
the third identification module is used for multiplying the signal to be identified and the delay signal thereof to obtain a time lag product, obtaining a time lag frequency spectrum module value according to the time lag product, finding out a maximum value of the time lag product frequency spectrum module value, taking data from a maximum value position to an end point of a time lag product frequency spectrum module value data section, carrying out normalization and quantization processing on the data from the maximum value position to the end point of the time lag product frequency spectrum module value data section, converting the normalized and quantized data into a graph, and calculating the number C of connected components of the graph 3 The number of connected components C of the graph 3 With a preset threshold η 3 Making a comparison if C 3 ＞η 3 Judging the wave to be a Chirp gravitational wave; if C 3 ≤η 3 Then, it is judged as a burst-type gravitational wave.
6. A computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a computing device, cause the computing device to perform any of the methods of claims 1-4.
7. A computing device, comprising,
one or more processors, memory, and one or more programs stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for performing any of the methods of claims 1-4.
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