CN111563227A - Fingerprint characteristic parameter extraction method for radiation source signal and radiation source identification - Google Patents
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Abstract
The application discloses a fingerprint characteristic parameter extraction method of a radiation source signal, which comprises the following steps: (1) carrying out ITD decomposition on the radio frequency digital signal of the radiation source to obtain a series of rotation components with gradually reduced instantaneous frequency, namely: any radio frequency digital signal data sn＝(s1,...,sn) Decomposition by ITD into:
wherein the content of the first and second substances,
refers to the i-th order baseline signal,
is the ith order rotational component;
is the residual component; here, the radio frequency digital signal data snIs the real part I of any complex digital signalnImaginary part QnOr amplitude En(ii) a (2) Calculating by utilizing the different-order rotation components in the step (1) to obtain corresponding energy distribution, box dimension, information dimension, instantaneous phase mean value, instantaneous phase standard deviation and envelope R characteristics; (3) directly comparing the (1) step signal data snReal part InImaginary part QnOr amplitude EnAnd calculating the corresponding box dimension and information dimension. By using the extracted fingerprint characteristic parameters and combining a machine learning algorithm, the judgment and identification of the radiation signal source can be accurately realized.
Description
Technical Field
The technical scheme of the application belongs to the technical field of signal analysis, signal processing and signal source distinguishing and identifying, and particularly aims at obtaining a series of fingerprint characteristic parameters of energy distribution, box dimension, information dimension, instantaneous phase mean value, instantaneous phase standard deviation and envelope R characteristic of different electromagnetic radiation signals after digital processing, and establishing a learning machine by utilizing a machine learning method to determine the type of a signal source according to the signal fingerprint characteristic parameters through autonomous comparison and learning, namely a fingerprint characteristic parameter extraction method and radiation source identification of a radiation source signal.
Background
In modern communication and data transmission technology, the realization of information transmission by electromagnetic field transmission has become a standard information transmission mode. In the transmission of electromagnetic field information, it is necessary to use a device or apparatus that transmits a different electromagnetic field radiation signal, i.e., a signal radiation source. The electromagnetic radiation source emits electromagnetic signals which necessarily carry fine characteristic information of hardware characteristics of equipment due to differences of circuits and devices of the electromagnetic radiation source, and the individual characteristic information of the radiation source is the characteristic which is different from other individuals and exists intrinsically. Therefore, the purpose of identifying the individual identity of the radiation source can be realized in principle by analyzing the fine characteristics of the radiation source, and the core of the method is that the characteristics of the received electromagnetic signals are measured, and the individual process of the radiation source generating the signals is determined according to the existing prior information. In order to realize the process, the characteristics of the fine characteristics of the radiation signals of the radiation source are firstly analyzed: (1) independence: the fine characteristic of the signal of the radiation source is irrelevant to the form of the emission signal, and when the waveform of the emission signal changes, the characteristic is still unchanged; (2) stability: the fine characteristics are stable and do not change significantly due to environmental changes such as temperature, vibration and the like; (3) the testability: means that the characteristic is measurable, and the measurement precision can meet the requirement of individual classification. The subtle features of the radiation source signal are more represented by irregular non-stationary, non-linear, non-gaussian signals, and the nature of the irregular non-stationary, non-linear, non-gaussian signals is difficult to be revealed more deeply by the traditional first-order and second-order analysis methods. The high-order statistical theory is used as an analysis tool of non-stationary signals and has great advantages for analyzing the fine characteristics of the radiation source. The high-order statistical features can be mainly divided into integral bispectrum, selective bispectrum, signal kurtosis and the like. The fine feature set of commonly used radiation sources is characterized by: a carrier frequency offset of the signal; modulation parameters of the signal (frequency modulation index of the FM signal, code rate of the FSK signal); the high order J characteristics of the signal envelope; hilbert spectral symmetry parameters based on EMD; box dimension, information dimension, LZC complexity of the signal envelope; kurtosis of the signal envelope; bispectral features of the signal; the n-th order high order spectral features of the signal. When the radiation source is distinguished and identified, firstly, the characteristic extraction is needed to be carried out on the radiation source fine signals, namely, the most effective characteristic subset is extracted from the original characteristic signal set according to a certain calculation rule, which is equivalent to the nonlinear transformation from the original measurement space to the characteristic space, the transformation processing from the high-dimensional measurement space to the low-dimensional characteristic space is realized, and the information with classification significance is obtained. After the extracted feature information is obtained, the source resolution is realized through classification decision by combining with the artificial intelligence of machine learning, which is the key of the final purpose. The classification decision is the last link of the radiation source identification task and is also a crucial link. The method has the function of establishing a decision rule according to the feature vectors obtained in the feature extraction link, and realizing the discrimination and classification of the tested objects. The main task of classifier design is to complete the nonlinear relation approximation of input-target pairs quickly according to limited training samples, the approximation is a training and learning process of a high-dimensional space, a certain generalization performance needs to be ensured in the approximation process, and the phenomena of over-learning and over-fitting cannot occur.
The existing radiation signal identification technology has the defect of inaccuracy and completeness in the selection of signal fine characteristic information, so that the identification precision and accuracy are low; aiming at the problems, according to the technical scheme, firstly, parameters of characteristic fine information are obtained through mathematical principle analysis, a calculation method of the parameters is provided, and on the basis, machine learning algorithm is combined to obtain more accurate judgment and identification of the radiation signal source.
Disclosure of Invention
The invention aims to further improve the identification accuracy of the radiation source, extract fine characteristic information (radiation source fingerprint information) contained in a radiation signal of the radiation source and realize the identification of a signal source by combining the information with a machine learning algorithm. The scheme for realizing the aim of the invention comprises a fingerprint information extraction method and a signal source identification process, and the specific technical scheme is as follows:
the fingerprint characteristic parameter extraction method of the radiation source signal comprises the following steps of energy distribution, box dimension, information dimension, instantaneous phase mean value, instantaneous phase standard deviation and envelope R characteristic:
(1) carrying out ITD decomposition on the radio frequency digital signal of the radiation source to obtain a series of rotation components with gradually reduced instantaneous frequency, namely: any radio frequency digital signal data sn＝(s1,...,sn) Decomposition by ITD into:
wherein the content of the first and second substances,
refers to the i-th order baseline signal,
is the ith order rotational component;
is the residual component; here, the radio frequency digital signal data snIs the real part I of any complex digital signalnImaginary part QnOr amplitude En；
(2) Calculating to obtain energy distribution, box dimension, information dimension, instantaneous phase mean value, instantaneous phase standard deviation and envelope R characteristics corresponding to different rotation components by using the rotation components of different orders obtained in the step (1);
(3) directly aiming at (1) step signal data snReal part of (I)nImaginary part QnOr amplitude EnAnd calculating the corresponding box dimension and information dimension.
According to the technical scheme, energy distribution, box dimension, information dimension, instantaneous phase mean value, instantaneous phase standard deviation and envelope R characteristics of different signal data are selected as characteristic parameters in the fingerprint characteristics of the signal transmitted by the signal source, the parameters not only aim at real part and imaginary part data of an original time domain signal, but also can obtain a series of rotating component parts with gradually reduced instantaneous frequency through ITD decomposition of the data, the completeness of fingerprint characteristic parameters is guaranteed, and the parameter data quantity of subsequent judgment is improved.
Radio frequency digital signal r of the radiation sourcen＝[r(1),r(2),...r(n)]Expressed in complex numbers: r isn＝In+jQnAmplitude of which
The corresponding energy distribution, box dimension, information dimension, instantaneous phase mean, instantaneous phase standard deviation and envelope R characteristics can also be obtained for the real part, imaginary part and amplitude of these signals.
The energy distribution corresponding to the rotation component in the step (2) is characterized by energy entropy:
herein, the
Representing the sum of energies, entropy of energies I, of the rotational componentsEERepresenting the energy distribution of the respective order rotation components of the signal by ITD decomposition, IEESmaller, represents a more concentrated energy distribution.
The method for calculating the instantaneous phase mean value and the instantaneous phase standard deviation corresponding to the rotation component in the step (2) comprises the following steps: (S1) extracting the rotation component signal segment between the zero-crossing points, the instantaneous phase is calculated as:
where A is1＞0，A2> 0 represents the maximum amplitude in the positive and negative half-waves of the signal segment, respectively, [ t [1,t2,…,t5]Being a point of time on a signal segment, t1，t5At the beginning and end of a segment, t2，t4At the time point of maximum positive and negative signal amplitude, t3For time points at which the signal amplitude is zero, xtSignal amplitude at time t; (S2) calculating the mean and variance of the instantaneous phase theta to obtain the instantaneous phase mean and the instantaneous phase standard deviation. The calculation method can accurately obtain the phase information of different areas through the phase differential calculation of a plurality of signal segments, and can obtain the instantaneous mean value and the instantaneous standard deviation of the phase through the average and variance calculation, so that the calculation precision is greatly improved.
The box dimension calculation method of different signal data in the step (2) and the step (3) comprises the following steps:
d (Delta) and d (2 Delta) are defined,
box dimension
Where s (n) is the real part I of the rotation component signal or signal datanImaginary part QnOr amplitude EnFloor (·) denotes the lower rounding operation, and Δ represents the sampling interval.
The information dimension calculation method of different signal data in the step (2) and the step (3) comprises the following steps: time domain continuous signal snConversion to the frequency domain fsThen to the frequency domain signal fsSampling to obtain ionScattered sequence { fs(i) T, where T is the length of the pre-processed signal sequence,
definition of
To calculate the information dimension
S herenIs the real part I of the rotating component signal or signal data of the time domainnImaginary part QnOr amplitude En。
The envelope R characteristic calculation method in the step (2) comprises
I.e. it is the ratio of the variance D of the envelope of the signal s to the square of the envelope mean E, where s is any of the different rotational component signals, if the signal is represented as s (t) ═ a (t) · cos (wt.) + (t), (t) is noise, and a (t) is the amplitude of the useful signal:
where E is the signal amplitude calculation.
In the method, the traditional method for extracting the characteristics from the real signals is utilized, the characteristics of the complex signals obtained by the orthogonal demodulation receiver are extracted to obtain more abundant information for individual identification of the radiation source, and the fine signals of the digital communication signals can be extracted with high precision due to good demodulation performance of ITD orthogonal demodulation.
By combining the extracted fingerprint characteristic parameters with a machine learning algorithm, the application also provides a method for identifying the radiation source, and the specific technical scheme is as follows: (1) constructing a radiation source signal fingerprint characteristic parameter classifier, and inputting known radiation source signal fingerprint characteristic parameters for classifier training; (2) and (3) receiving the radiation source signal by using the classifier completed in the step (1) to perform signal fingerprint characteristic parameter comparison and identification, and determining the radiation source. The radiation source signal fingerprint characteristic parameter classifier is constructed as a Kernel Extreme Learning Machine (KELM).
The kernel-extreme learning machine (KELM) projects the network input into a high-dimensional space by introducing a kernel function to replace random mapping in the ELM, so that in some occasions, a better classification effect is achieved compared with the traditional ELM, and the result is more stable. In the identification method, after a classifier is obtained by giving a fully trained sample for training, the radiation source individual to which the new signal received by the receiver belongs can be judged by analyzing the new signal.
Drawings
FIG. 1 is a schematic diagram of a real part of a signal to obtain a rotation component through ITD decomposition and to obtain a fingerprint characteristic parameter through direct calculation;
FIG. 2 is a schematic diagram of obtaining rotation components through ITD decomposition of the imaginary signal part and obtaining fingerprint characteristic parameters through direct calculation;
FIG. 3 is a schematic diagram of obtaining rotation components through ITD decomposition after calculating amplitudes of real and imaginary parts of signals and obtaining fingerprint characteristic parameters through direct calculation;
fig. 4 is a schematic diagram of instantaneous phase mean and instantaneous phase standard deviation calculation.
Detailed Description
The present invention will be further described in detail with reference to the drawings and examples, as shown in fig. 1,2 and 3. Any one of the digital signals rn＝[r(1),r(2),...r(n)]Can be represented in complex form: r isn＝In+jQnIn which In＝[I(1),I(2),...,I(n)]Represented as a co-directional output signal; and Qn＝[Q(1),Q(2),...,Q(n)]Representing quadrature output signals (in-and-out with respect to the received signal). I isn、QnAnd its envelope signal EnFeatures can be extracted by respective rotational components. Wherein the envelope signal En＝[E(1),E(2),...,E(n)]The calculation formula of (2) is as follows:
due to the envelope signal EnR is discardednThus in feature extraction, EnAnd phase information of signal no longer extracted by each order rotation componentAnd (4) information. Before extracting the fingerprint characteristic parameters, firstly, the signal is subjected to inherent time scale decomposition (ITD) and any radio frequency digital signal data sn＝(s1,...,sn) Decomposition by ITD into:
wherein the content of the first and second substances,
refers to the i-th order baseline signal,
is the ith order rotational component;
is the residual component; here, the radio frequency digital signal data snIs the real part I of any complex digital signalnImaginary part QnOr amplitude En(ii) a And then calculating the different-order rotation components obtained by ITD decomposition to obtain the energy distribution, box dimension, information dimension, instantaneous phase mean, instantaneous phase standard deviation and envelope R characteristics corresponding to the components. The same calculation method is used for directly comparing signal data snReal part of (I)nImaginary part QnOr amplitude EnAnd calculating the corresponding box dimension and information dimension. The specific calculation method of the parameters comprises the following steps: (1) calculation of energy distribution, characterized by energy entropy:
herein, the
Representing the sum of energies, entropy of energies I, of the rotational componentsEERepresenting the energy distribution of the respective order rotation components of the signal by ITD decomposition, IEEThe smaller the size, the more concentrated the energy distribution; (2) as shown in fig. 4, (S1) extracting the rotation component signal segment between the zero-crossing points, the instantaneous phase mean and the instantaneous phase standard deviation, and the instantaneous phase calculation formula is:
where A is1＞0，A2> 0 represents the maximum amplitude in the positive and negative half-waves of the signal segment, respectively, [ t [1,t2,…,t5]Being a point of time on a signal segment, t1，t5At the beginning and end of a segment, t2，t4At the time point of maximum positive and negative signal amplitude, t3For time points at which the signal amplitude is zero, xtSignal amplitude at time t; (S2) calculating the mean value and the variance of the instantaneous phase theta to obtain the instantaneous phase mean value and the instantaneous phase standard deviation; (3) the box dimension, as a parameter of the fractal dimension, the size and the change of which can reflect the irregularity and the complexity of the signal, is mainly used for describing the geometrical scale condition of a graph or a structure, and the specific calculation method comprises the following steps: d (Delta) and d (2 Delta) are defined,
box dimension
Where s (n) is the real part I of the rotation component signal or signal datanImaginary part QnOr amplitude EnFloor (·) denotes the lower rounding operation, Δ represents the sampling interval; (4) the information dimension, which is also a parameter of the fractal dimension, reflects the density degree of spatial distribution, and the influence of carrier frequency change can be eliminated by extracting the information dimension in a frequency domain, and the calculation method comprises the following steps: time domain continuous signal snConversion to the frequency domain fsThen to the frequency domain signal fsSampling is carried out to obtain a discrete sequence { fs(i) T, where T is the length of the pre-processed signal sequence,
definition of
To calculate the information dimension
S herenIs the real part I of the rotating component signal or signal data of the time domainnImaginary part QnOr amplitude En(ii) a (5) In the waveform feature extraction of the signal, parameters such as a rising edge, a falling edge, a top falling, a pulse width and the like are generally extracted, but the parameters have large variation in an unsteady signal and cannot be extracted as a fingerprint feature of the signal, and the waveform feature of the signal can be extracted by extracting a high-order statistic feature of the envelope. The method has the advantages that the stray output of different radiation source individuals is different, high-order statistic characteristics are different due to the fact that the stray output is reflected on a signal envelope, common signal envelope high-order statistic refers to a J value and an R value, and the envelope R value which is relatively simple to calculate is used as the extracted fingerprint characteristics. The calculation method comprises the following steps:
i.e. it is the ratio of the variance D of the envelope of the signal s to the square of the envelope mean E, where s is any of the different rotational component signals, if the signal is represented as s (t) ═ a (t) · cos (wt.) + (t), (t) is noise, and a (t) is the amplitude of the useful signal:
where E is the signal amplitude. The envelope R value can better reflect the envelope characteristic of the radiation source signal and reduce the influence of additive noise, so that the envelope R value has a certain inhibiting effect on the additive noise. For different radiation source signals, due to different spurious output noises, different extra modulations are added to each radiation source signal even under the same working mode, the extra modulations affect the envelope R value, and the envelope R value can realize the identification and classification of different radiation source signals.
After the process is carried out, the fine fingerprint characteristic parameters of the radiation source contained in the signal are extracted, and the parameters are used for radiation source identification: (1) constructing a radiation source signal fingerprint characteristic parameter classifier, and inputting known radiation source signal fingerprint characteristic parameters for classifier training; (2) and (3) receiving the radiation source signal by using the classifier completed in the step (1) to perform signal fingerprint characteristic parameter comparison and identification, and determining the radiation source. The radiation source signal fingerprint characteristic parameter classifier is constructed as a Kernel Extreme Learning Machine (KELM). The kernel-extreme learning machine (KELM) projects the network input into a high-dimensional space by introducing a kernel function to replace random mapping in the ELM, so that in some occasions, a better classification effect is achieved compared with the traditional ELM, and the result is more stable. In the identification method, after a classifier is obtained by giving a fully trained sample for training, the radiation source individual to which the new signal received by the receiver belongs can be judged by analyzing the new signal.
The technical scheme of the application adopts a layered feature extraction method for individual identification of the radiation source. A series of rotating components with gradually reduced instantaneous frequency are obtained by carrying out ITD decomposition on a received radiation source radio frequency signal, then combination characteristics are respectively extracted from an original signal and each rotating component, and the signal is depicted in a multi-level, multi-dimensional and multi-angle mode. The selected characteristics comprise fractal characteristics, statistical characteristics, energy distribution characteristics and the like so as to retain the fine fingerprint characteristics of the signal, thereby realizing individual identification of the radiation source.
The embodiments of the present invention are merely illustrative and not restrictive, and those skilled in the art can modify the embodiments without inventive contribution as required after reading the present specification, but the present invention is protected by patent law within the scope of the appended claims.
Claims (9)
1. The fingerprint characteristic parameter extraction method of the radiation source signal comprises the following steps of energy distribution, box dimension, information dimension, instantaneous phase mean value, instantaneous phase standard deviation and envelope R characteristic:
(1) carrying out ITD decomposition on the radio frequency digital signal of the radiation source to obtain a series of rotation components with gradually reduced instantaneous frequency, namely: any radio frequency digital signal data sn＝(s1,...,sn) Decomposition by ITD into:
wherein the content of the first and second substances,
refers to the i-th order baseline signal,
is the ith order rotational component;
is the residual component; here, the radio frequency digital signal data snIs the real part I of any complex digital signalnImaginary part QnOr amplitude En；
(2) Calculating to obtain energy distribution, box dimension, information dimension, instantaneous phase mean value, instantaneous phase standard deviation and envelope R characteristics corresponding to different rotation components by using the rotation components of different orders obtained in the step (1);
(3) directly aiming at the (1) step signal data snReal part of (I)nImaginary part QnOr amplitude EnAnd calculating the corresponding box dimension and information dimension.
2. The method of claim 1, wherein the radio frequency digital signal r of the radiation source isn＝[r(1),r(2),...r(n)]Expressed in complex numbers: r isn＝In+jQnAmplitude of which
3. The method for extracting fingerprint characteristic parameters of radiation source signals according to claim 1, wherein the energy distribution corresponding to the rotation component in the step (2) is characterized by energy entropy:
herein, the
Representing the sum of energies, entropy of energies I, of the rotational componentsEERepresenting the energy distribution of the respective order rotation components of the signal by ITD decomposition, IEESmaller, represents a more concentrated energy distribution.
4. The method for extracting fingerprint characteristic parameters of radiation source signals according to claim 1, wherein the method for calculating the instantaneous phase mean and the instantaneous phase standard deviation corresponding to the rotation component in the step (2) comprises: (S1) extracting the rotation component signal segment between the zero-crossing points, the instantaneous phase is calculated as:
where A is1＞0，A2> 0 represents the maximum amplitude in the positive and negative half-waves of the signal segment, respectively, [ t [1,t2,…,t5]Being a point of time on a signal segment, t1，t5At the beginning and end of a segment, t2，t4At the time point of maximum positive and negative signal amplitude, t3For time points at which the signal amplitude is zero, xtSignal amplitude at time t; (S2) calculating the mean and variance of the instantaneous phase theta to obtain the instantaneous phase mean and the instantaneous phase standard deviation.
5. The method for extracting fingerprint characteristic parameters of radiation source signals according to claim 1, wherein the box-dimension calculation method of different signal data in the step (2) and the step (3) is: d (Delta) and d (2 Delta) are defined,
box dimension
Where s (n) is the real part I of the rotation component signal or signal datanImaginary part QnOr amplitude EnFloor (·) denotes the lower rounding operation, and Δ represents the sampling interval.
6. The method for extracting fingerprint characteristic parameters of radiation source signals according to claim 1, wherein the information dimension calculation method of different signal data in the step (2) and the step (3) is: time domain continuous signal snConversion to the frequency domain fsThen to the frequency domain signal fsSampling is carried out to obtain a discrete sequence { fs(i) T, where T is the length of the pre-processed signal sequence,
definition of
To calculate the information dimension
S herenIs the real part I of the rotating component signal or signal data of the time domainnImaginary part QnOr amplitude En。
7. The method for extracting fingerprint characteristic parameters of radiation source signal according to claim 1, wherein the envelope R characteristic calculation method in step (2) is
I.e. it is the ratio of the variance D of the envelope of the signal s to the square of the envelope mean E, where s is any of the different rotational component signals, if the signal is represented as s (t) ═ a (t) · cos (wt.) + (t), (t) is noise, and a (t) is the amplitude of the useful signal:
where E is the signal amplitude calculation.
8. A method for identifying a radiation source using a fingerprint characteristic of a radiation source signal, the method comprising the steps of: (1) constructing a radiation source signal fingerprint characteristic parameter classifier, and inputting known radiation source signal fingerprint characteristic parameters for classifier training; (2) and (3) receiving the radiation source signal by using the classifier completed in the step (1) to perform signal fingerprint characteristic parameter comparison and identification, and determining the radiation source.
9. The method of claim 8, wherein the classifier of fingerprint characteristic parameters of radiation source signal is a Kernel Extreme Learning Machine (KELM).
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| CN113219415A (en) * | 2021-04-30 | 2021-08-06 | 北京理工大学 | Interference source individual identification method based on envelope fingerprint characteristics |
| CN113205076A (en) * | 2021-05-31 | 2021-08-03 | 西南交通大学 | Method and system for extracting behavior characteristics of wireless frame based on MEMD decomposition |
| CN113343868A (en) * | 2021-06-15 | 2021-09-03 | 四川九洲电器集团有限责任公司 | Radiation source individual identification method and device, terminal and storage medium |
| CN114531328A (en) * | 2022-04-24 | 2022-05-24 | 四川轻化工大学 | Modulation format identification method based on signal envelope flatness |
| CN114531328B (en) * | 2022-04-24 | 2022-07-01 | 四川轻化工大学 | Modulation format identification method based on signal envelope flatness |
| CN114866162A (en) * | 2022-07-11 | 2022-08-05 | 中国人民解放军国防科技大学 | Signal data enhancement method and system and identification method and system of communication radiation source |
| CN114866162B (en) * | 2022-07-11 | 2023-09-26 | 中国人民解放军国防科技大学 | Signal data enhancement method and system and communication radiation source identification method and system |
| CN115169406A (en) * | 2022-07-15 | 2022-10-11 | 中国人民解放军国防科技大学 | Instantaneous phase fingerprint feature enhancement method based on empirical mode decomposition |
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