CN112733613A - Radiation source identification method based on Hilbert transform and Helbert coefficient characteristics - Google Patents
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Abstract
The invention relates to a radiation source identification method based on Hilbert transform and Hull coefficient characteristics, which solves the problems in the prior art and has the technical scheme that the method comprises the following steps of firstly, collecting radio frequency transient signal segments of communication radiation source individuals through a receiver; step two, after Hilbert conversion and Holder coefficient characteristic extraction, the extracted characteristic is used as a radio frequency fingerprint of a transmitter; and step three, the input classifier identifies the radio frequency fingerprint of the transmitter, so that modulation identification and individual identification of a communication radiation source and physical layer authentication of the equipment of the Internet of things are realized.
Description
Technical Field
The invention belongs to a radiation source identification method, and relates to a radiation source identification method based on Hilbert transform and Hull coefficient characteristics.
Background
Information security is the key to construct a reliable and robust internet of things. With the continuous emergence of information security problems brought by wireless communication networks, how to accurately identify and authenticate an internet of things object and prevent the problems of counterfeit user identity, equipment cloning and the like is a problem which is mainly solved by the application of the internet of things. The traditional authentication mechanism is realized in an application layer, a numerical result which is difficult to counterfeit by a third party is generated by utilizing a cryptographic algorithm, but the mechanism has risks of protocol security loopholes, secret key leakage and the like. The internet of things sensing layer terminal equipment has the characteristics of diversification, intellectualization, complexity and huge quantity, and although the traditional authentication mechanism can ensure information safety to a certain extent, the internet of things sensing layer terminal equipment is not suitable for processing large-scale networks and mass data brought by the large-scale networks, and the information safety requirements of the internet of things are difficult to meet. Therefore, the research of the individual identification method of the communication radiation source with low error rate, high efficiency and low cost is the key for ensuring the stable operation of the internet of things. Physical layer authentication is one of core technologies for guaranteeing wireless communication safety, and the basic principle is that space-time specificity of a receiving and sending channel and a transmission signal is combined to verify physical characteristics of two communication parties, so that identity authentication is realized in a physical layer. Compared with the authentication technology of an application layer, the method can effectively resist the imitation attack and has the advantages of high authentication speed, low complexity, good compatibility and no need of considering the execution of various protocols.
The research on the physical layer security authentication technology is still in a preliminary stage nowadays, rich physical layer resources are not fully utilized, and a large research space is still provided. The radio frequency fingerprint identification is a non-password authentication method based on equipment physical layer hardware, does not need to consume additional computing resources, does not need to embed additional hardware, and is a very potential technology for constructing a low-cost, simpler and safer identification and authentication system. Device Identification based on subtle features of radio frequency signals, originally stemming from Specific Emitter Identification (SEI), is the ability to associate the unique electromagnetic properties of radiation sources with individual radiation sources. In 2003, Hall et al, canada, proposed the concept of radio frequency "fingerprinting" (RF-Fingerprint) to extract a set of fine feature sets with diversity from the transmitter signal as the essential physical layer features of the device. As everyone has different fingerprints, each wireless device also has different radio frequency fingerprints, i.e. hardware differences, which are included in the communication signal, and the signal can be identified by extracting the fingerprint.
The transient signal-based fingerprinting technique is a process of radio frequency fingerprint extraction of a section of transient/transient signals transmitted at the instant of device turn-on/turn-off. The transient signal does not contain any data information, only reflects the hardware characteristics of a transmitter, has independence, and the radio frequency fingerprint is extracted from the transient signal at first, such as the duration, fractal dimension characteristics, spectral characteristics, time domain envelope, wavelet coefficient and the like of the transient signal.
From the current research situation of radio frequency fingerprint identification, extracting a radio frequency fingerprint with unique native attributes is still a very challenging task, the extracted radio frequency fingerprint is still limited by a large number of factors, and a large number of problems are still to be researched in the aspects of radio frequency fingerprint generation mechanism, feature extraction and feature selection, robustness of the radio frequency fingerprint, channel environment interference resistance and the like.
Disclosure of Invention
The invention solves the problems that the prior art still has a very challenging task of extracting the radio frequency fingerprint with unique native attributes from the current research situation of the radio frequency fingerprint identification, the extracted radio frequency fingerprint is still limited by a plurality of factors, and a plurality of problems are to be researched in the aspects of radio frequency fingerprint generation mechanism, feature extraction and feature selection, robustness of the radio frequency fingerprint, channel environment interference resistance and the like, and provides a radiation source identification method based on Hilbert transform and Hulder coefficient features.
The technical scheme adopted by the invention for solving the technical problems is as follows: a radiation source identification method based on Hilbert transform and Hull coefficient characteristics comprises the following steps,
step one, collecting radio frequency transient signal segments of an individual communication radiation source through a receiver;
secondly, acquiring the frequency component of the radio frequency transient signal, transforming and extracting the coefficient characteristic of the radio frequency transient signal to be used as the radio frequency fingerprint of the transmitter;
and step three, the input classifier identifies the radio frequency fingerprint of the transmitter, so that modulation identification and individual identification of a communication radiation source and physical layer authentication of the equipment of the Internet of things are realized.
The invention provides a communication radiation source individual identification method based on Hilbert transform and Holder coefficient feature extraction, aiming at the problem that the identification rate of the existing fingerprint identification technology based on transient signals is low for extremely similar communication radiation source individuals (especially wireless devices of the same manufacturer, the same model and the same batch), wherein the method comprises the steps of firstly collecting radio frequency transient signal segments of the communication radiation source individuals through a receiver, then extracting the instantaneous envelopes of the transient signal segments through Hilbert transform, and then extracting the two-dimensional features of the instantaneous envelopes by using Holder coefficient features to be used as radio frequency fingerprints of a transmitter; and finally, inputting a grey correlation classifier to identify the radio frequency fingerprint of the transmitter, so that modulation identification and individual identification of a communication radiation source, physical layer authentication of equipment of the Internet of things and the like can be realized.
Preferably, the transformation for obtaining the frequency component of the radio frequency transient signal is a Hilbert transformation. The Hilbert transform, is used to phase-retard all frequency components of a signal by 90 degrees.
Preferably, the frequency component of the radio frequency transient signal obtained by the Hilbert transform is extracted by a Holder coefficient feature extraction method. The method comprises the steps of defining a Holder coefficient in a Holder coefficient-based radar radiation source individual identification technology, then providing a Holder coefficient feature extraction algorithm based on pulse rising/falling edges, and finally verifying the stability and the effectiveness of a Holder coefficient feature set.
Preferably, the radio frequency signal collected by the receiver is a transient or transient signal transmitted at the instant the wireless device is turned on or off.
Preferably, in step three, the extracted two-dimensional Holder coefficient features are identified by a gray correlation classifier.
Preferably, in the first step, at the moment when the wireless device is turned on or off, a transient or transient signal segment is collected, and the transient envelope of the transient signal segment is extracted through Hilbert transform, and then the Holder coefficient feature is used to perform two-dimensional feature extraction on the transient envelope.
Preferably, in the training process of the gray correlation classifier, an oscilloscope is selected as acquisition equipment, 10 interphone transient signals are acquired in a laboratory LOS, a wave filter is used for acquisition, 50 groups of data are acquired for each interphone, the sampling frequency is 40MHz, 159901 points are acquired for each group of data, 200 samples are randomly selected from 500 samples in total for training, and the rest 300 samples are used for identification testing, wherein for each interphone, 20 training samples are used, and 30 testing samples are used.
The substantial effects of the invention are as follows: the method can effectively improve the identification accuracy of the radio frequency fingerprint identification technology based on the transient signal to the communication radiation source individuals, in particular to the wireless equipment of the same manufacturer, the same model and the same batch.
Description of the drawings:
FIG. 1 is a schematic overall flow chart of the present invention.
Detailed Description
The technical solution of the present invention will be further specifically described below by way of specific examples.
Example 1:
a radiation source identification method based on Hilbert transform and Hull coefficient characteristics is shown in figure 1 and comprises the following steps,
step one, collecting radio frequency transient signal segments of an individual communication radiation source through a receiver;
step two, after Hilbert conversion and Holder coefficient characteristic extraction, the extracted characteristic is used as a radio frequency fingerprint of a transmitter;
and step three, the input classifier identifies the radio frequency fingerprint of the transmitter, so that modulation identification and individual identification of a communication radiation source and physical layer authentication of the equipment of the Internet of things are realized.
The radio frequency signal collected by the receiver is a transient or transient signal transmitted at the moment when the wireless device is turned on or off.
In the first step, at the moment when the wireless device is turned on or off, a section of transient or transient signal is collected, the transient envelope of the transient signal section is extracted through Hilbert transformation, and then two-dimensional feature extraction is performed on the transient envelope by using Holder coefficient features.
In step three, the extracted two-dimensional Holder coefficient features are identified by a gray correlation classifier. More specifically, in the training process of the gray correlation classifier, an agilent oscilloscope is selected as acquisition equipment, 10 transient signals of the motorola interphones are acquired in a laboratory LOS, the agilent oscilloscope is used for acquisition, 50 groups of data are acquired by each interphone, the sampling frequency is 40MHz, 159901 points are acquired by each group of data, 200 samples are randomly selected from 500 total samples for training, the remaining 300 samples are used for identification and testing, wherein for each interphone, 20 training samples are provided, and 30 testing samples are provided.
The embodiment provides a communication radiation source individual identification method based on Hilbert transform and Holder coefficient feature extraction, aiming at the problem that the identification rate of the existing fingerprint identification technology based on transient signals is low for very similar communication radiation source individuals, particularly for wireless devices of the same manufacturer, the same model and the same batch, and the method comprises the steps of firstly collecting radio frequency transient signal segments of the communication radiation source individuals through a receiver, then extracting the instantaneous envelopes of the transient signal segments through the Hilbert transform, and then extracting two-dimensional features of the instantaneous envelopes through the Holder coefficient features to be used as radio frequency fingerprints of a transmitter; and finally, inputting a grey correlation classifier to identify the radio frequency fingerprint of the transmitter, so that modulation identification and individual identification of a communication radiation source, physical layer authentication of equipment of the Internet of things and the like can be realized.
The above-described embodiment is only a preferred embodiment of the present invention, and is not intended to limit the present invention in any way, and other variations and modifications may be made without departing from the spirit and scope of the invention as set forth in the appended claims.
Claims (7)
1. A radiation source identification method based on Hilbert transform and Hull coefficient characteristics is characterized by comprising the following steps of: comprises the following steps of (a) carrying out,
step one, collecting radio frequency transient signal segments of an individual communication radiation source through a receiver;
secondly, acquiring the frequency component of the radio frequency transient signal, transforming and extracting the coefficient characteristic of the radio frequency transient signal to be used as the radio frequency fingerprint of the transmitter;
and step three, the input classifier identifies the radio frequency fingerprint of the transmitter, so that modulation identification and individual identification of a communication radiation source and physical layer authentication of the equipment of the Internet of things are realized.
2. The method of claim 1, wherein the method comprises: the Hilbert transform is adopted for obtaining the frequency components of the radio frequency transient signals and performing transformation.
3. The method of claim 2, wherein the method comprises: and extracting the frequency components of the radio frequency transient signals obtained by Hilbert transformation by adopting a Holder coefficient characteristic extraction method.
4. The method of claim 3, wherein the method comprises: the radio frequency signal collected by the receiver is a transient or transient signal transmitted at the moment when the wireless device is turned on or off.
5. The method of claim 1, wherein the method comprises: in the first step, at the moment when the wireless device is turned on or off, a section of transient or transient signal is collected, the transient envelope of the transient signal section is extracted through Hilbert transformation, and then two-dimensional feature extraction is performed on the transient envelope by using Holder coefficient features.
6. The method of claim 5, wherein the method comprises: in step three, the extracted two-dimensional Holder coefficient features are identified by a gray correlation classifier.
7. The method of claim 6, wherein the method comprises: in the training process of the gray correlation classifier, an oscilloscope is selected as acquisition equipment, 10 interphone transient signals are acquired in a laboratory LOS, the oscilloscope is used for acquiring, 50 groups of data are acquired by each interphone, the sampling frequency is 40MHz, 159901 points are acquired by each group of data, 200 samples are randomly selected from 500 total samples for training, the rest 300 samples are used for identification and testing, wherein for each interphone, 20 training samples are used, and 30 testing samples are used.
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