CN113657138A - Radiation source individual identification method based on equipotential planet chart - Google Patents
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Abstract
A radiation source individual identification method based on an equipotential planet chart comprises the following steps: s1: acquiring signal data of a plurality of radiation source individuals with the same type; s2: carrying out data preprocessing on the signal data to obtain a signal constellation diagram; s3: performing point density calculation on data points in the signal constellation diagram, and coloring the signal constellation diagram according to a point density calculation result to obtain an equipotential sphere diagram; s4: performing feature extraction and classification on the equipotential planet map by using a convolutional neural network; s5: and outputting the recognition result. The method converts IQ two-path signal data from a signal domain to an image domain based on an equipotential planet image, automatically and effectively extracts deep features of radiation source individuals carried by the signals through a convolutional neural network, corresponds each equipotential planet image to the signal data, and corresponds the signal data to the radiation source individuals, thereby more effectively identifying and classifying the radiation source individuals.
Description
Technical Field
The invention relates to a radiation source individual identification method based on an equipotential planet chart, and belongs to the technical field of radiation source signal identification.
Background
Specific Emitter Identification (SEI) refers to a technique of performing characteristic measurement on a received electromagnetic signal and determining an individual radiation source generating the signal according to existing a priori information. The method has extremely important military significance for correctly identifying the detected radiation source in a complex and changeable electromagnetic environment. Meanwhile, with the advent of the age of 5G and the internet of things, the security of wireless networks is also facing a huge challenge. The individual identification technology of the radiation source can be applied to projects such as wireless access authentication, electromagnetic environment supervision and the like, and the safety of a wireless network can be effectively improved. In the civil field, the radiation source individual identification technology is one of key technologies in the fields of cognitive radio, radio positioning, communication equipment fault detection and identification and the like, and has very important function.
With the development of science and technology, the electromagnetic signal environment of the signal is increasingly complex, the density of a radiation source is multiplied, and the pattern of the electromagnetic signal is complex and changeable, which all bring unprecedented challenges to the radiation source identification. The general radiation source identification method is difficult to realize accurate, stable and reliable radiation source identification in a complex electromagnetic environment. Therefore, research and development of new radiation source identification methods and identification devices are urgently needed.
Disclosure of Invention
The invention aims to solve the technical problem that the prior art is not enough, and provides a radiation source individual identification method based on an equipotential planet map.
The technical problem to be solved by the invention is realized by the following technical scheme:
the invention provides a radiation source individual identification method based on an equipotential planet chart, which comprises the following steps of:
s1: acquiring signal data of a plurality of radiation source individuals with the same type;
s2: carrying out data preprocessing on the signal data to obtain a signal constellation diagram;
s3: performing point density calculation on data points in the signal constellation diagram, and coloring the signal constellation diagram according to a point density calculation result to obtain an equipotential sphere diagram;
s4: performing feature extraction and classification on the equipotential planet map by using a convolutional neural network;
s5: and outputting the recognition result.
Preferably, the signal data includes I-path signal data and Q-path signal data.
Preferably, the data preprocessing is performed on the signal data to obtain a signal constellation diagram, and specifically:
and taking the I-path signal data in the signal data of each sampling point of the plurality of radiation source individuals as the abscissa of a signal constellation diagram, and taking the Q-path signal data in the signal data of each sampling point as the ordinate of the signal constellation diagram, thereby mapping the signal data of the plurality of radiation source individuals into the signal constellation diagram.
Preferably, a window function is used for the point density calculation.
Preferably, the window function is a density window function, and the normalized point density ρ at the sampling point is:
where ρ (i) represents the normalized point density of the ith sample point, h (i) represents the abscissa of the signal constellation diagram at which the ith sample point is obtained, v (i) represents the ordinate of the signal constellation diagram at which the ith sample point is obtained, r represents half of the side length of the square region in which the normalized point density is calculated, N represents the total number of sample points in the signal constellation diagram, f (x) satisfies the following expression:
preferably, the r ═ sqrt ((range (x)/30) ^2+ (range (y)/30) ^2),
wherein x represents the I path signal data, y represents the Q path signal data, range function represents the difference between the maximum value and the minimum value in the solving vector, and sqrt represents the square root function.
Preferably, the coloring the signal constellation according to the result of the point density calculation specifically includes:
and selecting different colors, and coloring the data points with different normalized point density values or different normalized point density ranges in the signal constellation diagram.
Preferably, the normalized dot density from high to low is expressed sequentially using the colors in a yellow to green to blue gradient color bar.
Preferably, the convolutional neural network is AlexNet.
In summary, the IQ two-path signal data are converted from the signal domain to the map domain based on the equipotential star map, the deep features of the radiation source individuals carried by the signals are automatically and effectively extracted through the convolutional neural network, each equipotential star map corresponds to the signal data, and the signal data corresponds to the radiation source individuals, so that the radiation source individuals are more effectively identified and classified.
The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.
Drawings
FIG. 1 is a block diagram of the individual identification method of a radiation source based on an equipotential sphere diagram according to the present invention;
fig. 2 is a comparison diagram of the radiation source individual identification method based on the equipotential sphere diagram and the traditional feature extraction method.
Detailed Description
FIG. 1 is a block diagram of the individual identification method of a radiation source based on an equipotential sphere diagram. As shown in fig. 1, the present invention provides an equipotential sphere diagram-based individual radiation source identification method, which includes the following steps:
s1: acquiring signal data of a plurality of radiation source individuals with the same type;
s2: carrying out data preprocessing on the signal data to obtain a signal constellation diagram;
s3: performing point density calculation on data points in the signal constellation diagram, and coloring the signal constellation diagram according to a point density calculation result to obtain an equipotential sphere diagram;
s4: performing feature extraction and classification on the equipotential planet map by using a convolutional neural network;
s5: and outputting the recognition result.
In S1, the signal data includes I-path signal data and Q-path signal data.
In S2, the data preprocessing is performed on the signal data to obtain a signal constellation, specifically, I-path signal data in the signal data of each sampling point of the multiple radiation source individuals is used as an abscissa of the signal constellation, and Q-path signal data in the signal data of each sampling point is used as an ordinate of the signal constellation, so that the signal data of the multiple radiation source individuals is mapped to the signal constellation. It should be added that the above examples are only illustrative, the present invention does not limit the specific way of obtaining the signal constellation through data preprocessing, and those skilled in the art can select a suitable existing method to process the signal data into the signal constellation according to practical situations.
In the signal constellation, different regions have different sample point densities. In the present invention, the point density calculation described in S3 is exemplarily performed using a window function.
Specifically, in the present invention, the window function is preferably a density window function, and when the density window function slides on the signal constellation diagram, the density window function counts the number of points in different region windows, and divides the number of the sampling points of the whole signal constellation diagram by the number of the points to obtain a normalized point density ρ at the sampling points, that is, the normalized point density ρ is obtained
Where ρ (i) represents the normalized point density of the ith sample point, h (i) represents the abscissa of the signal constellation diagram at which the ith sample point is obtained, v (i) represents the ordinate of the signal constellation diagram at which the ith sample point is obtained, r represents one half of the side length of the square region in which the normalized point density is calculated, N represents the total number of sample points in the signal constellation diagram, and f (x) satisfies the following expression:
preferably, r ═ sqrt ((range (x)/30) ^2+ (range (y)/30) ^2), where x represents the I-path signal data, y represents the Q-path signal data, the range function represents the difference between the maximum value and the minimum value in the solution vector, and sqrt represents the square root function.
By the above method, the point density at each data point in the signal constellation diagram can be obtained, and of course, the invention is not limited to the calculation method of the point density, and those skilled in the art can select other well-known point density calculation methods.
In S3, the coloring the signal constellation according to the result of the point density calculation specifically includes: and selecting different colors, and coloring the data points with different normalized point density values or different normalized point density ranges in the signal constellation diagram. The present invention does not limit the specific rule of coloring, for example, coloring the signal constellation diagram by a preset color bar containing the corresponding relationship between the dot density and the color. Preferably, the normalized dot density from high to low is expressed sequentially using the colors in a yellow to green to blue gradient color bar.
Through the coloring process in S3, an image having RGB information can be formed, that is, an equi-star map can be obtained. The traditional signal constellation diagram has defects when used for extracting signal characteristics, for example, in the actual data acquisition process, the internal noise of a device can seriously pollute radio frequency signals, so that the constellation diagrams of different acquired radio frequency signals have the same graph.
In S4, the present invention does not limit the type of the convolutional neural network as long as it can implement the picture recognition classification. Preferably, the convolutional neural network AlexNet is used for automatically extracting features and classifying the same. Since AlexNet is a public network, the specific process in S4 is not described herein again.
Fig. 2 is a comparison diagram of the radiation source individual identification method based on the equipotential sphere diagram and the traditional feature extraction method. In fig. 2, the method of using a convolutional neural network AlexNet in combination with an equi-potential star map is compared with the conventional Welch method, Yule-Walker method and Burg method for extracting power spectrum features from signal data. The data set used in this comparative experiment is introduced as follows: eight power amplifier output data of the same model and the same batch are collected by a baseband signal receiver to be used as a research object, the operating center frequency of the amplifier module is 433MHz, each individual uses 100 samples to research, and each sample uses 20000 sample points. When the power spectrum feature is extracted, 2048 FFT points are used, PCA is used for dimensionality reduction after a power spectrum is obtained, and finally a K nearest neighbor classifier is used for classification and identification.
As can be seen from FIG. 2, the identification accuracy of the individual identification method of the radiation source of the invention using the convolutional neural network and the equipotential sphere diagram is superior to that of the conventional method.
In summary, the IQ two-path signal data are converted from the signal domain to the map domain based on the equipotential star map, the deep features of the radiation source individuals carried by the signals are automatically and effectively extracted through the convolutional neural network, each equipotential star map corresponds to the signal data, and the signal data corresponds to the radiation source individuals, so that the radiation source individuals are more effectively identified and classified.
Claims (9)
1. A radiation source individual identification method based on an equipotential planet chart is characterized by comprising the following steps:
s1: acquiring signal data of a plurality of radiation source individuals with the same type;
s2: carrying out data preprocessing on the signal data to obtain a signal constellation diagram;
s3: performing point density calculation on data points in the signal constellation diagram, and coloring the signal constellation diagram according to a point density calculation result to obtain an equipotential sphere diagram;
s4: performing feature extraction and classification on the equipotential planet map by using a convolutional neural network;
s5: and outputting the recognition result.
2. The method for identifying individuals as a radiation source based on an equipotential sphere according to claim 1, wherein the signal data includes I-path signal data and Q-path signal data.
3. The method for identifying individuals as a radiation source based on an equi-potential star map as claimed in claim 2, wherein the data preprocessing is performed on the signal data to obtain a signal constellation map specifically as follows:
and taking the I-path signal data in the signal data of each sampling point of the plurality of radiation source individuals as the abscissa of a signal constellation diagram, and taking the Q-path signal data in the signal data of each sampling point as the ordinate of the signal constellation diagram, thereby mapping the signal data of the plurality of radiation source individuals into the signal constellation diagram.
4. The method for identifying individuals as a source of radiation based on an equi-potential star map as claimed in claim 3 wherein said point density calculation is performed using a windowing function.
5. The method for identifying individuals as a radiation source based on an equipotential planet map according to claim 3, wherein the window function is a density window function, and the normalized point density p at the sampling points is:
where ρ (i) represents the normalized point density of the ith sample point, h (i) represents the abscissa of the signal constellation diagram at which the ith sample point is obtained, v (i) represents the ordinate of the signal constellation diagram at which the ith sample point is obtained, r represents half of the side length of the square region in which the normalized point density is calculated, N represents the total number of sample points in the signal constellation diagram, f (x) satisfies the following expression:
6. the method of claim 5, wherein r ═ sqrt ((range (x)/30) ^2+ (range (y)/30) ^2),
wherein x represents I path signal data, y represents Q path signal data, range function represents the difference between the maximum value and the minimum value in the solving vector, and sqrt represents the root opening number.
7. The method for identifying an individual radiation source based on an equipotential sphere of claim 1, wherein the coloring of the signal constellation according to the result of the point density calculation specifically comprises:
and selecting different colors, and coloring the data points with different normalized point density values or different normalized point density ranges in the signal constellation diagram.
8. The method for identifying individuals as a radiation source based on an equipotential sphere according to claim 7, wherein the normalized dot density from high to low is expressed sequentially by using colors in a color bar with a gradual change from yellow to green to blue.
9. The method for identifying individuals as sources of radiation based on an equinox global map as claimed in claim 1 wherein said convolutional neural network is AlexNet.
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