CN111371715B - Feature extraction method for identifying ASK signals under low signal-to-noise ratio - Google Patents
Feature extraction method for identifying ASK signals under low signal-to-noise ratio Download PDFInfo
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Abstract
The invention discloses a feature extraction method for identifying ASK signals under low signal-to-noise ratio, which comprises the following steps: s1, reading in a signal sampling sequence of the signal to be detected; s2, obtaining the power spectrum density of the signal sampling sequence by carrying out Fourier transform on the autocorrelation function of the signal sampling sequence; s3, processing the power spectrum density; s4, calculating the standard deviation and the mean value of the power spectral density; s5, calculating a standard deviation coefficient mdask of the signal to be detected, and completing the feature extraction of the signal to be detected; and S6, identifying the signal to be detected, and finishing the identification of the ASK signal under the condition of low signal-to-noise ratio. The method provided by the invention can not only complete the task of effectively identifying the ASK signals under the condition of low signal-to-noise ratio, but also directly process the intermediate frequency signals without being influenced by communication parameters such as carrier frequency and the like, and does not need accurate parameter estimation. Meanwhile, the characteristic parameters extracted by the method cannot change greatly, and the method has effectiveness and robustness.
Description
Technical Field
The invention belongs to the technical field of signal modulation identification, and particularly relates to a feature extraction method for identifying ASK signals under a low signal-to-noise ratio.
Background
Extracting signal characteristic parameters for signal pattern recognition from received signals is a first step of realizing modulation pattern recognition and is also a key link. For how to identify the ASK-class signal from the signals of various modulation patterns, the existing method mostly extracts statistical information based on discrete instantaneous amplitude. These statistical characteristics based on discrete instantaneous amplitude inherently have excellent performance under the condition of high signal-to-noise ratio, but often cannot be used when the signal-to-noise ratio is low, noise interference cannot be effectively removed even if various preprocessing means such as adding a filter and performing sliding averaging are adopted, and a method for preprocessing signal sequences acquired under different communication parameters is also influenced by the communication parameters. Furthermore, such statistical features based on discrete instantaneous amplitudes are also not able to adapt to different carrier frequencies. If the received modulated frequency band signal is converted into a baseband complex signal, the carrier frequency infection can be removed, but operations such as carrier synchronization, symbol synchronization and parameter estimation are added, and other errors are inevitably introduced in the process. Based on the above situation, the invention provides a feature extraction method for identifying ASK signals under low signal-to-noise ratio.
Disclosure of Invention
The invention aims to solve the problem of identifying ASK signals under low signal-to-noise ratio, and provides a feature extraction method for identifying ASK signals under low signal-to-noise ratio.
The technical scheme of the invention is as follows: a feature extraction method for identifying ASK signals under low signal-to-noise ratio comprises the following steps:
s1, reading in a signal sampling sequence of the signal to be detected;
s2, obtaining the power spectrum density of the signal sampling sequence by carrying out Fourier transform on the autocorrelation function of the signal sampling sequence;
s3, processing the power spectral density to obtain the processed power spectral density;
s4, calculating the standard deviation and the mean value of the processed power spectral density;
s5, obtaining a standard deviation coefficient mdask of the signal to be detected according to the standard deviation and the mean value of the processed power spectral density, and completing feature extraction of the signal to be detected;
and S6, identifying the signal to be detected by using the standard deviation coefficient mdask to finish identifying the ASK signal under the condition of low signal-to-noise ratio.
The invention has the beneficial effects that: the method provided by the invention can not only complete the task of effectively identifying the ASK signals under the condition of low signal-to-noise ratio, but also directly process the intermediate frequency signals without being influenced by communication parameters such as carrier frequency and the like, and does not need accurate parameter estimation. Meanwhile, the characteristic parameters extracted by the method cannot be greatly changed, the method has effectiveness and robustness, and the problems of influence of load frequency and the like and poor noise adaptability of the existing method are solved.
Further, step S2 includes the following sub-steps:
s21: calculating an autocorrelation function R (k) of the signal sample sequence S (n);
s22: obtaining a power spectral density C of a sequence of signal samples by Fourier transforming an autocorrelation function R (k)1(n) the calculation formula is as follows:
C1(n)＝FFT[R(k)]
R(k)＝E[s(n)s(n+k)]
wherein n is used to traverse each sampling point of the signal sampling sequence s (n), k is the number of points delayed by the signal sequence itself when performing the autocorrelation operation, FFT is the fourier transform, and E is the averaging.
The beneficial effects of the further scheme are as follows: in the invention, the processing mode is selected because when the signal sampling sequence and the signal sampling sequence are subjected to autocorrelation, the signal sampling sequence can be used for measuring the similarity of values of the signal sampling sequence at different moments, and noise usually does not have periodicity, so that noise interference in a communication environment is effectively eliminated; meanwhile, Fourier transform is carried out on the autocorrelation function, and the estimation of the power spectral density of the signal sampling sequence can be obtained through an indirect method.
Further, step S3 includes the following sub-steps:
s31: squaring the power spectral density to obtain the squared power spectral density C2(n) the calculation formula is as follows:
C2(n)＝[C1(n)]2
where n is used to traverse each sample point, C, of the signal sample sequence1(n) is the power spectral density;
s32: performing zero center normalization processing of first absolute value extraction on the power spectral density after the square extraction to obtain an intermediate power spectral density C3(n) the calculation formula is as follows:
wherein N is used to traverse each sample point of the signal sample sequence, N is the total number of sample points of the signal sample sequence, C2(n) is the squared power spectral density;
s33: for intermediate power spectral density C3(n) performing zero-center normalization processing of the second absolute value to obtain the processed power spectral density C4(n) the calculation formula is as follows:
wherein N is used to traverse each sample point of the signal sample sequence, N is the total number of sample points of the signal sample sequence, C3(n) is the intermediate power spectral density.
The beneficial effects of the further scheme are as follows: in the present invention, the square of the amplitude of the resulting power spectral density is calculated in order to increase the effective amplitude gap and further attenuate the effect of noise; two zero-centering processes are for statistical convenience and the normalized power spectral density is the distribution with mean 0, standard deviation 1 and variance 1.
Further, in step S5, the standard deviation coefficient mdask is calculated as:
where n is used to traverse each sample point of the signal sample sequence, D [ C ]4(n)]For post-processing power spectral density C4(n) the variance of the (n),
for post-processing power spectral density C4(n) standard deviation, E [ C ]4(n)]For post-processing power spectral density C4Average of (n).
The beneficial effects of the further scheme are as follows: in the invention, because the power spectral densities of signals with different modulation patterns have different data levels, the relative concentration of the power spectral densities cannot be compared directly by using the standard deviation for measuring the concentration degree of the data, and the variation degree of the power spectral densities can be measured by using the standard deviation coefficient.
Further, step S6 includes the following sub-steps:
s61: obtaining a characteristic parameter distribution diagram of the signal to be detected under different in-band signal-to-noise ratios by using a characteristic extraction method of the standard deviation coefficient mdask;
s62: repeatedly verifying the signals to be tested at different code element rates, different carrier frequencies and different sampling frequencies based on the characteristic parameter distribution diagram;
s63: obtaining a comparison threshold value for effectively distinguishing the standard deviation coefficient mdask according to the repeated verification result;
s64: and accurately identifying the signal to be detected by utilizing the standard deviation coefficient mdask and the comparison threshold value, and identifying the ASK signal under the low signal-to-noise ratio.
The beneficial effects of the further scheme are as follows: in the invention, the standard deviation coefficient mdask is the characteristic parameter to be extracted, and the extracted characteristic parameter does not change greatly, so that the method has effectiveness and robustness.
Further, the signal types of the signal to be measured include 2ASK, 4ASK, 8ASK, BPSK, QPSK, 8PSK, OQPSK, UQPSK, 8QAM, 16QAM, 32QAM, 64QA, 128QA, 256QAM, 2FSK, 4FSK, MSK, and GMSK.
The beneficial effects of the further scheme are as follows: in the invention, the signals to be detected are various in types, and the ASK signals can be effectively identified by using the characteristic extraction method.
Further, in step S64, when the in-band snr is greater than-8 dB, the ASK-class signal is accurately identified by using the standard deviation coefficient mdask.
The beneficial effects of the further scheme are as follows: in the invention, the ASK signals can be accurately identified by using the characteristic parameter mdask without being influenced by communication parameters such as carrier frequency and the like.
Drawings
FIG. 1 is a flow chart of a feature extraction method;
fig. 2 is a flowchart of step S2;
fig. 3 is a flowchart of step S3;
fig. 4 is a flowchart of step S6;
FIG. 5 is a graph of the standard deviation coefficient distribution for different in-band signal-to-noise ratios.
Detailed Description
The embodiments of the present invention will be further described with reference to the accompanying drawings.
As shown in fig. 1, the present invention provides a feature extraction method for identifying ASK-like signals under low signal-to-noise ratio, comprising the following steps:
s1, reading in a signal sampling sequence of the signal to be detected;
s2, obtaining the power spectrum density of the signal sampling sequence by carrying out Fourier transform on the autocorrelation function of the signal sampling sequence;
s3, processing the power spectral density to obtain the processed power spectral density;
s4, calculating the standard deviation and the mean value of the processed power spectral density;
s5, obtaining a standard deviation coefficient mdask of the signal to be detected according to the standard deviation and the mean value of the processed power spectral density, and completing feature extraction of the signal to be detected;
and S6, identifying the signal to be detected by using the standard deviation coefficient mdask to finish identifying the ASK signal under the condition of low signal-to-noise ratio.
In the embodiment of the present invention, as shown in fig. 2, step S2 includes the following sub-steps:
s21: calculating an autocorrelation function R (k) of the signal sample sequence S (n);
S22：obtaining a power spectral density C of a sequence of signal samples by Fourier transforming an autocorrelation function R (k)1(n) the calculation formula is as follows:
C1(n)＝FFT[R(k)]
R(k)＝E[s(n)s(n+k)]
wherein n is used to traverse each sampling point of the signal sampling sequence s (n), k is the number of points delayed by the signal sequence itself when performing the autocorrelation operation, FFT is the fourier transform, and E is the averaging.
In the invention, the processing mode is selected because when the signal sampling sequence and the signal sampling sequence are subjected to autocorrelation, the signal sampling sequence can be used for measuring the similarity of values of the signal sampling sequence at different moments, and noise usually does not have periodicity, so that noise interference in a communication environment is effectively eliminated; meanwhile, Fourier transform is carried out on the autocorrelation function, and the estimation of the power spectral density of the signal sampling sequence can be obtained through an indirect method.
In the embodiment of the present invention, as shown in fig. 3, step S3 includes the following sub-steps:
s31: squaring the power spectral density to obtain the squared power spectral density C2(n) the calculation formula is as follows:
C2(n)＝[C1(n)]2
where n is used to traverse each sample point, C, of the signal sample sequence1(n) is the power spectral density;
s32: performing zero center normalization processing of first absolute value extraction on the power spectral density after the square extraction to obtain an intermediate power spectral density C3(n) the calculation formula is as follows:
wherein N is used to traverse each sample point of the signal sample sequence, N is the total number of sample points of the signal sample sequence, C2(n) is the squared power spectral density;
s33: for intermediate power spectral density C3(n) taking absolute value for the second timeZero-center normalization of the values to obtain a processed power spectral density C4(n) the calculation formula is as follows:
wherein N is used to traverse each sample point of the signal sample sequence, N is the total number of sample points of the signal sample sequence, C3(n) is the intermediate power spectral density.
In the present invention, the square of the amplitude of the resulting power spectral density is calculated in order to increase the effective amplitude gap and further attenuate the effect of noise; two zero-centering processes are for statistical convenience and the normalized power spectral density is the distribution with mean 0, standard deviation 1 and variance 1.
In the embodiment of the present invention, as shown in fig. 1, in step S5, the calculation formula of the standard deviation coefficient mdask is:
where n is used to traverse each sample point of the signal sample sequence, D [ C ]4(n)]For post-processing power spectral density C4(n) the variance of the (n),
for post-processing power spectral density C4(n) standard deviation, E [ C ]4(n)]For post-processing power spectral density C4Average of (n).
In the invention, because the power spectral densities of signals with different modulation patterns have different data levels, the relative concentration of the power spectral densities cannot be compared directly by using the standard deviation for measuring the concentration degree of the data, and the variation degree of the power spectral densities can be measured by using the standard deviation coefficient.
In the embodiment of the present invention, as shown in fig. 4, step S6 includes the following sub-steps:
s61: obtaining a characteristic parameter distribution diagram of the signal to be detected under different in-band signal-to-noise ratios by using a characteristic extraction method of the standard deviation coefficient mdask;
s62: repeatedly verifying the signals to be tested at different code element rates, different carrier frequencies and different sampling frequencies based on the characteristic parameter distribution diagram;
s63: obtaining a comparison threshold value for effectively distinguishing the standard deviation coefficient mdask according to the repeated verification result;
s64: and accurately identifying the signal to be detected by utilizing the standard deviation coefficient mdask and the comparison threshold value, and identifying the ASK signal under the low signal-to-noise ratio.
In the invention, the standard deviation coefficient mdask is the characteristic parameter to be extracted, and the extracted characteristic parameter does not change greatly, so that the method has effectiveness and robustness. Meanwhile, the resulting comparison threshold was 19.
In the embodiment of the present invention, as shown in fig. 1, the signal types included in the signal to be measured include 2ASK, 4ASK, 8ASK, BPSK, QPSK, 8PSK, OQPSK, UQPSK, 8QAM, 16QAM, 32QAM, 64QA, 128QA, 256QAM, 2FSK, 4FSK, MSK, and GMSK.
In the invention, the signals to be detected are various in types, and the ASK signals can be effectively identified by using the characteristic extraction method.
In the embodiment of the present invention, as shown in fig. 4, in step S64, when the in-band snr is greater than-8 dB, the ASK-class signal is accurately identified by using the standard deviation coefficient mdask.
In the invention, the ASK signals can be accurately identified by using the characteristic parameter mdask without being influenced by communication parameters such as carrier frequency and the like.
The method of the present invention is further described below in conjunction with the actual profile of the features. When the signal types included in the signal to be measured include 2ASK, 4ASK, 8ASK, BPSK, QPSK, 8PSK, OQPSK, UQPSK, 8QAM, 16QAM, 32QAM, 64QA, 128QA, 256QAM, 2FSK, 4FSK, MSK, and GMSK, the distribution of the standard deviation coefficients of the signal to be measured under different in-band signal to noise ratios can be obtained as shown in fig. 5. By repeatedly verifying signals of different symbol rates, different carrier frequencies and different sampling frequencies, a comparison threshold value 19 for effectively distinguishing the extracted characteristic parameter mdask can be obtained. When the in-band signal-to-noise ratio is larger than-8 dB, the ASK signals can be accurately identified by using the characteristic parameter mdask. And the eigenvalue distribution graph obtained under the condition that the symbol rate and the sampling frequency are the same but the carrier frequencies are different is completely consistent with that of fig. 5; when the sampling rate changes, the signal characteristic values of other modulation patterns except for the ASK signal can change, but still keep distributed below the comparison threshold. This fully demonstrates the effectiveness and robustness of the extracted feature parameter mdask.
The working principle and the process of the invention are as follows: the characteristic extraction method adopted by the invention firstly reads in the received signal sampling sequence, then obtains the power spectrum density, and performs zero center normalization processing of two absolute values after squaring the power spectrum density, thereby obtaining the standard deviation coefficient, namely the characteristic parameter. And finally, accurately identifying the ASK signals according to the characteristic parameters.
The invention has the beneficial effects that: the method provided by the invention can not only complete the task of effectively identifying the ASK signals under the condition of low signal-to-noise ratio, but also directly process the intermediate frequency signals without being influenced by communication parameters such as carrier frequency and the like, and does not need accurate parameter estimation. Meanwhile, the characteristic parameters extracted by the method cannot be greatly changed, the method has effectiveness and robustness, and the problems of influence of load frequency and the like and poor noise adaptability of the existing method are solved.
It will be appreciated by those of ordinary skill in the art that the embodiments described herein are intended to assist the reader in understanding the principles of the invention and are to be construed as being without limitation to such specifically recited embodiments and examples. Those skilled in the art can make various other specific changes and combinations based on the teachings of the present invention without departing from the spirit of the invention, and these changes and combinations are within the scope of the invention.
Claims (7)
1. A feature extraction method for identifying ASK-like signals at low signal-to-noise ratio, comprising the steps of:
s1: reading in a signal sampling sequence of a signal to be detected;
s2: obtaining the power spectral density of the signal sampling sequence by carrying out Fourier transform on the autocorrelation function of the signal sampling sequence;
s3: processing the power spectral density to obtain the processed power spectral density;
s4: calculating the standard deviation and the mean value of the processed power spectral density;
s5: obtaining a standard deviation coefficient mdask of the signal to be detected according to the standard deviation and the mean value of the processed power spectral density, and completing the feature extraction of the signal to be detected;
s6: and identifying the signal to be detected by using the standard deviation coefficient mdask to finish identifying the ASK signal under the low signal-to-noise ratio.
2. The feature extraction method for identifying ASK-like signals under low signal-to-noise ratio as claimed in claim 1, wherein said step S2 comprises the sub-steps of:
s21: calculating an autocorrelation function R (k) of the signal sample sequence S (n);
s22: obtaining a power spectral density C of a sequence of signal samples by Fourier transforming an autocorrelation function R (k)1(n) the calculation formula is as follows:
C1(n)＝FFT[R(k)]
R(k)＝E[s(n)s(n+k)]
wherein the content of the first and second substances,nfor traversing each sample point of the signal sample sequence S (n), k is the number of points delayed by the signal sequence itself when performing the autocorrelation operation, FFT [. multidot. ]]For Fourier transformation, E [. alpha. ]]To average.
3. The feature extraction method for identifying ASK-like signals under low signal-to-noise ratio as claimed in claim 1, wherein said step S3 comprises the sub-steps of:
s31: squaring the power spectral density to obtain the squared power spectral density C2(n) the calculation formula is as follows:
C2(n)＝[C1(n)]2
where n is used to traverse each sample point, C, of the signal sample sequence1(n) is the power spectral density;
s32: performing zero center normalization processing of first absolute value extraction on the power spectral density after the square extraction to obtain an intermediate power spectral density C3(n) the calculation formula is as follows:
wherein N is used to traverse each sample point of the signal sample sequence, N is the total number of sample points of the signal sample sequence, C2(n) is the squared power spectral density;
s33: for intermediate power spectral density C3(n) performing zero-center normalization processing of the second absolute value to obtain the processed power spectral density C4(n) the calculation formula is as follows:
wherein N is used to traverse each sample point of the signal sample sequence, N is the total number of sample points of the signal sample sequence, C3(n) is the intermediate power spectral density.
4. The feature extraction method for identifying ASK-like signals under low signal-to-noise ratio as claimed in claim 1, wherein in said step S5, the calculation formula of the standard deviation coefficient mdask is:
where n is used to traverse each sample point of the signal sample sequence, D [ C ]4(n)]For post-processing power spectral density C4(n) the variance of the (n),
for post-processing power spectral density C4(n) standard deviation, E [ C ]4(n)]For post-processing power spectral density C4Mean value of (n), σ [ C ]4(n)]And calculating the standard deviation of the processed power spectral density.
5. The feature extraction method for identifying ASK-like signals under low signal-to-noise ratio as claimed in claim 1, wherein said step S6 comprises the sub-steps of:
s61: obtaining a characteristic parameter distribution diagram of the signal to be detected under different in-band signal-to-noise ratios by using a characteristic extraction method of the standard deviation coefficient mdask;
s62: repeatedly verifying the signals to be tested at different code element rates, different carrier frequencies and different sampling frequencies based on the characteristic parameter distribution diagram;
s63: obtaining a comparison threshold value for effectively distinguishing the standard deviation coefficient mdask according to the repeated verification result;
s64: and accurately identifying the signal to be detected by utilizing the standard deviation coefficient mdask and the comparison threshold value, and identifying the ASK signal under the low signal-to-noise ratio.
6. The method of claim 1, wherein the signal to be detected includes 2ASK, 4ASK, 8ASK, BPSK, QPSK, 8PSK, OQPSK, UQPSK, 8QAM, 16QAM, 32QAM, 64QA, 128QA, 256QAM, 2FSK, 4FSK, MSK, and GMSK.
7. The feature extraction method for identifying ASK-class signals under low snr according to claim 5, wherein in the step S64, when the in-band snr is greater than-8 dB, the ASK-class signals are accurately identified by using the standard deviation coefficient mdask.
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