CN114978582A - Radio frequency fingerprint identification method and system based on iterative cosine spectrum transformation - Google Patents
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Abstract
The invention provides a radio frequency fingerprint identification method and a system based on iterative cosine spectrum transformation, wherein the method comprises the following steps: separating power envelope and carrier of a transmitter signal to be identified; carrying out first-order derivation on the power envelope, and acquiring a transient signal in a primary transient interval; establishing a floating interval at the start and stop positions of the transient signal based on the continuous length of the transient signal, establishing a fixed step length, setting a sliding window to slide in the floating interval, and when the calculated cost function value is smaller than that of the transient signal, adjusting the sliding step length until the position which enables the cost function value to be minimum is found, so as to obtain an optimal target interval; after performing fast Fourier transform and discrete cosine transform on signals in the optimal target interval in sequence, constructing a radio frequency fingerprint characteristic vector; and inputting the radio frequency fingerprint feature vector into a support vector machine to perform identity authentication of equipment. The authentication method can utilize hardware subtle differences among different wireless devices to carry out accurate identity authentication on the wireless devices.
Description
Technical Field
The invention relates to the technical field of wireless communication, in particular to a radio frequency fingerprint identification method and system based on iterative cosine spectrum transformation.
Background
With the popularization and development of mobile communication technology, wireless interconnection has deepened into the aspect of daily life, and especially with the popularization of the internet of things, huge changes are brought to the life and even the production mode of people. Although the difficulty of networking of mass equipment can be simplified by using the wireless information interaction mode of electromagnetic waves, the openness of the wireless channel also provides natural conditions for information eavesdropping and counterfeit attacks, and the wireless information interaction mode becomes the root of the wireless communication security problem. For the problem of device access authentication in a wireless network, password authentication, digital certificate authentication, access authentication based on a block chain, and the like are generally adopted. Essentially, the authentication modes are digital encryption based on modern encryption algorithms, and guarantee is provided for reliability of access authentication by using complexity of the encryption algorithms and privacy of initial distribution passwords. Along with the continuous improvement of the computing capability of various computers with new systems, the violent decoding of the encryption algorithm becomes easier and easier; in addition, the distribution mechanism of the initial key also has the hidden danger of privacy disclosure. Since the tolerance effect of the electronic components in the wireless device is the main reason for generating the rf fingerprint, the rf fingerprint is difficult to clone and has uniqueness, thereby becoming a new paradigm for wireless device identity authentication.
The existing radio frequency fingerprint technology is mainly a steady radio frequency fingerprint technology. The steady-state signal is a signal of a transmitter in a stable power state, and is mainly based on the aspects of a modulation mode, noise output difference, stray characteristic difference and the like of equipment, the time-frequency analysis is carried out on a received signal, and physical quantities with independent and stable characteristics are extracted to be used as radio frequency fingerprints to be classified so as to achieve the purpose of radiation source identification. The radio frequency fingerprint method based on the steady-state signal needs more prior knowledge based on the modulation mode, the code pattern design and the like of the target signal, and therefore the universality is poor.
Disclosure of Invention
According to the technical problem that the prior radio frequency fingerprint needs to be based on prior knowledge of a modulation mode, a code pattern design and the like of a target signal, so that the universality is poor, the radio frequency fingerprint identification method and the radio frequency fingerprint identification system based on the iterative cosine spectrum transformation are provided. The received signals are subjected to power envelope and carrier separation, the characteristics of the power envelope are deeply analyzed, fast Fourier transform and discrete cosine transform are used for converting the received signals into a higher-dimensional space, the energy statistical characteristics of the signals are extracted and constructed into characteristic vectors, the characteristic vectors are used as radio frequency fingerprint characteristics of equipment, then classification is carried out, and the equipment is identified according to the characteristics. The invention has wider application range and lower computation complexity, and can realize the identification and authentication of transmitter equipment in practical application.
The technical means adopted by the invention are as follows:
a radio frequency fingerprint identification method based on iterative cosine spectrum transformation comprises the following steps:
s1, acquiring a transmitter signal to be identified, and separating the power envelope and the carrier of the transmitter signal to be identified;
s2, after first-order derivation is carried out on the power envelope, a primary transient interval is obtained according to a preset threshold value, and a transient signal is obtained in the primary transient interval;
s3, establishing a floating interval at the start and stop positions of the transient signal based on the continuous length of the transient signal, establishing a fixed step length, setting a sliding window to slide in the floating interval, continuously calculating the value of the cost function in the sliding process, and adjusting the sliding step length until the position which enables the cost function value to be minimum is found out when the calculated cost function value is smaller than the cost function value of the transient signal, thereby obtaining an optimal target interval;
s4, after performing fast Fourier transform and discrete cosine transform on the signals in the optimal target interval in sequence, constructing radio frequency fingerprint characteristic vectors based on the transform result;
and S5, inputting the radio frequency fingerprint feature vector into a support vector machine to perform identity authentication of equipment.
Further, after the transmitter signal to be identified is acquired in S1, the transmitter signal to be identified is further subjected to filtering processing, so as to filter stray components that are unintentionally acquired during the acquisition process.
Further, establishing a floating interval at the start and stop positions of the transient signal based on the duration of the transient signal in S3 includes:
a floating range with the size of w/8 is set at the start position and the stop position of the primary transient interval, so that a floating interval is constructed, wherein w represents the continuous length of the transient signal.
Further, the cost function described in S3 is obtained according to the following calculation:
wherein c, d represents the position of the currently detected transient start and stop point, s (c) represents the amplitude of the start point, s (d) represents the amplitude of the end point, s 1 ,s 2 Amplitude value corresponding to ideal transient start-stop point position, C c,d And (u) is a logarithmic power cosine coefficient of the currently detected transient signal.
Further, constructing the radio frequency fingerprint feature vector based on the transformation result in S4 includes: and acquiring a maximum value, a minimum value and a first extreme value of a first-order difference component of the transformation result, and a second-order central moment and a first-order origin moment of logarithmic envelope Fourier transformation to construct a radio frequency fingerprint feature vector.
Further, the step S5 of inputting the radio frequency fingerprint feature vector to a support vector machine for identity authentication of the device includes:
and (3) reducing the dimension of the radio frequency fingerprint feature vector by using principal component analysis, randomly dividing the features subjected to dimension reduction into a training set and a testing set according to a preset proportion, inputting the training set and the testing set into a Support Vector Machine (SVM) for classification, and comparing a label result output by the trained model with an actual label to obtain the classification accuracy.
The invention also discloses a radio frequency fingerprint identification system based on iterative cosine spectrum transformation, which comprises the following components:
the power envelope acquiring unit is used for acquiring a transmitter signal to be identified and separating the power envelope and the carrier of the transmitter signal to be identified;
a primary transient interval obtaining unit, configured to obtain a primary transient interval according to a preset threshold after performing a first derivation on the power envelope, and obtain a transient signal in the primary transient interval;
the acquisition unit is used for establishing a floating interval at the start position and the stop position of the transient signal based on the continuous length of the transient signal, establishing a fixed step length to set a sliding window to slide in the floating interval, continuously calculating the value of the cost function in the sliding process, and adjusting the sliding step length until the position which enables the minimum value of the cost function is found when the calculated value of the cost function is smaller than the value of the cost function of the transient signal, so that the optimal target interval is acquired;
the radio frequency fingerprint characteristic vector construction unit is used for constructing a radio frequency fingerprint characteristic vector based on a transformation result after sequentially performing fast Fourier transformation and discrete cosine transformation on the signals in the optimal target interval;
and the identity authentication unit is used for inputting the radio frequency fingerprint feature vector into a support vector machine to perform identity authentication of equipment.
Compared with the prior art, the invention has the following advantages:
by the method, the transient power envelope characteristic is deeply analyzed, a three-phase down-conversion structure is designed to realize the classification of turn-on power envelope and carrier, the optimal target signal interval can be selected for radio frequency fingerprint extraction and identification no matter what modulation mode the received signal is, and other prior knowledge is not needed. The method can be used for effectively identifying different devices through experiments, and the identification accuracy is 98%.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a flowchart of a radio frequency fingerprint identification method based on iterative cosine spectrum transformation according to the present invention.
Fig. 2 is a flow of performing the radio frequency fingerprint identification according to the embodiment.
Fig. 3 is a three-dimensional visualization effect of the extracted features in the embodiment.
FIG. 4 shows the recognition accuracy achieved by the embodiment.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, the present invention provides a radio frequency fingerprint identification method based on iterative cosine spectrum transformation, comprising the following steps:
s1, obtaining a transmitter signal to be identified, and separating the power envelope and the carrier of the transmitter signal to be identified.
Further, when the transmitter signal to be identified is acquired in this step, filtering is performed to filter out stray components that are unintentionally acquired during the acquisition process.
And S2, after first-order derivation is carried out on the power envelope, acquiring a primary transient interval according to a preset threshold, and acquiring a transient signal in the primary transient interval.
Specifically, the transient rough detection is firstly carried out through the step, the first derivative of the power envelope is obtained, and the threshold value is set to obtain the transient interval detected in the first step.
And then intercepting the power envelope in turn-on logarithmic form in the interval, performing fast Fourier transform to convert the power envelope into a frequency domain, and continuously performing discrete cosine transform on the basis of the frequency domain.
S3, establishing a floating interval at the start and stop positions of the transient signal based on the continuous length of the transient signal, establishing a fixed step length to set a sliding window to slide in the floating interval, continuously calculating the value of the cost function in the sliding process, and adjusting the sliding step length until the position which enables the cost function value to be minimum is found out when the calculated cost function value is smaller than the cost function value of the transient signal, thereby obtaining the optimal target interval.
And S4, sequentially carrying out fast Fourier transform and discrete cosine transform on the signals in the optimal target interval, and constructing the radio frequency fingerprint characteristic vector based on the transform result.
The method comprises two steps of S3 and S4, and is mainly used for transient detection optimization based on a dynamic sliding window sequence search algorithm, extracting features and constructing feature vectors, and specifically comprises the following steps:
firstly, by designing a dynamic sliding window and a cost function as follows:
where c, d represent the position of the current transient start and stop point, s 1 ,s 2 An amplitude value corresponding to the position of the ideal transient start-stop point, s (C) represents the amplitude of the start point, s (d) represents the amplitude of the end point, C c,d (u) indicates that the target signal is in [ c, d ]]Log cosine power spectrum within the interval. Determining a more accurate transient signal interval position, and taking a corresponding signal interval when the cost function is the minimum value as an optimal target signal interval;
and then, carrying out fast Fourier transform and discrete cosine transform processing by using the optimal target signal interval, and extracting the seven-dimensional statistics of the maximum value, the minimum value, the first extreme value of the first-order difference component, the second central moment of the first-order difference component, the first origin moment of logarithmic envelope Fourier transform and the medium-high dimensional energy of the logarithmic power cosine spectrum to construct the radio frequency fingerprint feature vector.
And S5, inputting the radio frequency fingerprint feature vector into a support vector machine to perform identity authentication of equipment.
Specifically, the step uses principal component analysis to perform dimensionality reduction on the obtained feature vector, randomly divides the dimensionality-reduced features into a training set and a testing set according to a preset proportion, inputs the training set and the testing set into a Support Vector Machine (SVM) for classification, and compares a label result output by a trained model with an actual label to obtain classification accuracy.
The scheme and effect of the present invention are further illustrated by specific application examples.
As shown in fig. 1, the radio frequency fingerprint identification method based on iterative cosine spectral transform provided in this embodiment includes:
1) the transmitter signal to be identified is acquired and the signal is subjected to power envelope and carrier separation.
In this embodiment, bluetooth wireless signals of 7 different devices are selected as target signals and numbered 1 to 7, and each device collects 150 pieces of data. Since bluetooth signals operate in the ISM2400 frequency band, the sampling frequency needs to be at least 4.8Gsps according to the nyquist sampling theorem. The sample rate of the selected data is 5Gsps and the signal duration is about 10 mus in this embodiment. The real signals are directly captured by a high sampling rate oscilloscope and a low resolution analog-to-digital converter, and in the signal acquisition process, a band-pass filter is added in a preprocessing part to filter out spurious signals due to the fact that the oscilloscope generates some unnecessary spurious signals. Modeling the receiving end signal as:
s IF (t)＝A env (t)·m IF (t)
wherein A is env (t) is a power envelope model of the received signal, specifically expressed as:
m IF (t) is a carrier modulated signal, t represents time, t 0 Denotes the instant of onset of the transient, t 1 Indicating the end of the transient. Construction of s by designing three-phase down-conversion structures IF And (t) completely eliminating the carrier component containing the modulation information through logarithmic operation, realizing the separation of the power envelope, and simultaneously performing the energy normalization processing of the power envelope to obtain the representation of turn-on normalized logarithmic power envelope as follows:
wherein A is s (t) is the carrier modulated signal amplitude.
2) Carrying out logarithmic power cosine transformation on the target signal;
in the step, firstly, rough transient detection is carried out, a first derivative of the power envelope is obtained, the position of a midpoint of a transient part is obtained according to the symmetry of the derivative of the transient envelope, and then a transient interval detected in the first step is obtained. The starting point of the transient interval is the starting moment of the transmitter, and the end point is the moment when the transmitting power reaches the rated power.
Carrying out logarithmic power cosine transformation on the signals in the roughly detected transient interval to obtain a logarithmic power cosine coefficient:
wherein:
F ramp,1 (k)+F ramp,2 (k) is a pair off ramp (t) the result of performing a fast fourier transform on the discrete form of (t). It should be noted that, since the present invention only processes transient signals, the amplitude of the transient envelope is also zero by default at the time t is 0, and therefore, the envelope in the logarithmic form can be expanded into a power series form. Wherein the denominator part is log (A) in discrete form s (t)), the molecular portion is a power series expansion corresponding to a discrete form logarithmic power envelope. N is the number of FFT points, u represents the order of the logarithmic cosine power coefficient, a s,q Coefficients being the expansion terms of a logarithmic form of the power series of the carrier, a env,p The coefficients of the expansion terms of the logarithmic envelope power series are such that n represents the number of sample points of the discrete signal.
Observed to obtain, F ramp,1 (k) Is a constant term, with only F ramp,2 (k) Is varied, it can be deduced that the values of the log power cosine spectrum c (u) are uniquely related to the coefficients of the power series expansion of the power envelope only.
3) Transient detection optimization based on a dynamic sliding window sequence search algorithm, extracting features and constructing feature vectors, comprising the following steps:
a. designing a cost function:
where c, d denote the position of the currently detected transient start and stop points, s 1 ,s 2 The amplitude value corresponding to the position of the ideal transient start-stop point. Firstly, the approximate position p and q of the transient signal obtained in the step 2) obtain the continuous sample length w of the transient part, which is p-q. Left and right floating intervals of w/8 are set at the position p and the position q, respectively, and within the floating interval, the step length epsilon is firstly used 1 Setting a sliding window (5 sample points), calculating the value of a cost function once each sliding, and changing the sliding step length into epsilon when the value of the cost function of the current position is less than J (p, q) 2 (1 sample point), finding the result which can minimize the cost function value, namely the optimal target interval.
b. And (3) carrying out fast Fourier transform and discrete cosine transform processing in the step 2) by using the optimal target signal interval, taking a logarithmic power cosine spectrum, and then obtaining statistics such as a maximum value, a minimum value, a first extreme value of a first-order difference component, a second-order central moment, a first-order origin moment of logarithmic envelope Fourier transform and the like from the logarithmic power cosine spectrum to construct a radio frequency fingerprint feature vector.
4) And inputting the feature vector into a support vector machine to perform identity authentication of the equipment.
Specifically, 150 samples per device were run as 8: 2, dividing the ratio into a training set and a testing set, and performing dimensionality reduction on the obtained seven-dimensional vector by using principal component analysis, wherein the processing flow of the principal component analysis is as follows:
a. for the feature set X ∈ R n*m The sample features in (1) are de-averaged. For each feature x of the sample i Using the value x of the current feature i The mean of the features in the sample set is subtracted, i.e.:
b. calculating the covariance matrix XX of the samples T 。
c. And carrying out eigenvalue decomposition on the covariance matrix to obtain an eigenvalue and an eigenvector of the covariance matrix.
d. And selecting the eigenvector corresponding to the largest first k eigenvalues to be converted into P.
And e.Y is the k-dimensional feature matrix obtained after dimensionality reduction.
And the feature vector after dimensionality reduction is used as input to enter a Support Vector Machine (SVM) to carry out model learning and training by using a linear kernel function, and the classification accuracy of the obtained model is 97.2% and 98.5% on a training set and a test set respectively.
The invention also discloses a radio frequency fingerprint identification system based on iterative cosine spectrum transformation, which comprises the following components:
the power envelope acquiring unit is used for acquiring a transmitter signal to be identified and separating the power envelope and the carrier of the transmitter signal to be identified;
a primary transient interval obtaining unit, configured to obtain a primary transient interval according to a preset threshold after performing a first derivation on the power envelope, and obtain a transient signal in the primary transient interval;
the acquisition unit is used for establishing a floating interval at the start position and the stop position of the transient signal based on the continuous length of the transient signal, establishing a fixed step length to set a sliding window to slide in the floating interval, continuously calculating the value of the cost function in the sliding process, and adjusting the sliding step length until the position which enables the minimum value of the cost function is found when the calculated value of the cost function is smaller than the value of the cost function of the transient signal, so that the optimal target interval is acquired;
the radio frequency fingerprint characteristic vector construction unit is used for constructing a radio frequency fingerprint characteristic vector based on a transformation result after sequentially carrying out fast Fourier transformation and discrete cosine transformation on signals in the optimal target interval;
and the identity authentication unit is used for inputting the radio frequency fingerprint feature vector into a support vector machine to perform identity authentication of equipment.
For the embodiments of the present invention, the description is simple because it corresponds to the above embodiments, and for the related similarities, please refer to the description in the above embodiments, and the detailed description is omitted here.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (7)
1. A radio frequency fingerprint identification method based on iterative cosine spectrum transformation is characterized by comprising the following steps:
s1, acquiring a transmitter signal to be identified, and separating the power envelope and the carrier of the transmitter signal to be identified;
s2, after first-order derivation is carried out on the power envelope, acquiring a primary transient interval according to a preset threshold, and acquiring a transient signal in the primary transient interval;
s3, establishing a floating interval at the start and stop positions of the transient signal based on the continuous length of the transient signal, establishing a fixed step length to set a sliding window to slide in the floating interval, continuously calculating the value of the cost function in the sliding process, and adjusting the sliding step length until the position which enables the cost function value to be minimum is found out when the calculated cost function value is smaller than the cost function value of the transient signal, so as to obtain an optimal target interval;
s4, after performing fast Fourier transform and discrete cosine transform on the signals in the optimal target interval in sequence, constructing radio frequency fingerprint characteristic vectors based on the transform result;
and S5, inputting the radio frequency fingerprint feature vector into a support vector machine to perform identity authentication of equipment.
2. The method according to claim 1, wherein after the transmitter signal to be identified is acquired in S1, the transmitter signal to be identified is filtered to filter out spurious components that are unintentionally acquired during the acquisition process.
3. The method according to claim 1, wherein the step S3 of establishing a floating interval at the start and stop positions of the transient signal based on the duration of the transient signal comprises:
a floating range with the size of w/8 is set at the start position and the stop position of the primary transient interval, so that a floating interval is constructed, wherein w represents the continuous length of the transient signal.
4. The method according to claim 1, wherein the cost function in S3 is obtained according to the following calculation:
wherein c, d represents the position of the currently detected transient start and stop point, s (c) represents the amplitude of the start point, s (d) represents the amplitude of the end point, s 1 ,s 2 Amplitude value corresponding to ideal transient start-stop point position, C c,d And (u) is a logarithmic power cosine spectrum of the currently detected transient signal.
5. The method according to claim 1, wherein constructing the radio frequency fingerprint feature vector based on the transform result in S4 includes: and acquiring a maximum value, a minimum value and a first extreme value of a first-order difference component of the transformation result, and a second-order central moment and a first-order origin moment of logarithmic envelope Fourier transformation to construct a radio frequency fingerprint feature vector.
6. The method according to claim 1, wherein the step of inputting the rf fingerprint feature vector to a support vector machine for identity authentication of a device in S5 comprises:
and (3) reducing the dimension of the radio frequency fingerprint feature vector by using principal component analysis, randomly dividing the features subjected to dimension reduction into a training set and a testing set according to a preset proportion, inputting the training set and the testing set into a Support Vector Machine (SVM) for classification, and comparing a label result output by the trained model with an actual label to obtain the classification accuracy.
7. A radio frequency fingerprint identification system based on iterative cosine spectrum transformation is characterized by comprising the following components:
the power envelope acquiring unit is used for acquiring a transmitter signal to be identified and separating the power envelope and the carrier of the transmitter signal to be identified;
a primary transient interval obtaining unit, configured to obtain a primary transient interval according to a preset threshold after performing a first derivation on the power envelope, and obtain a transient signal in the primary transient interval;
the acquisition unit is used for establishing a floating interval at the start position and the stop position of the transient signal based on the continuous length of the transient signal, establishing a fixed step length to set a sliding window to slide in the floating interval, continuously calculating the value of the cost function in the sliding process, and adjusting the sliding step length until the position which enables the minimum value of the cost function is found when the calculated value of the cost function is smaller than the value of the cost function of the transient signal, so that the optimal target interval is acquired;
the radio frequency fingerprint characteristic vector construction unit is used for constructing a radio frequency fingerprint characteristic vector based on a transformation result after sequentially carrying out fast Fourier transformation and discrete cosine transformation on signals in the optimal target interval;
and the identity authentication unit is used for inputting the radio frequency fingerprint feature vector into a support vector machine to perform identity authentication of equipment.
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| CN109598216A (en) * | 2018-11-23 | 2019-04-09 | 南方电网科学研究院有限责任公司 | A kind of radio-frequency fingerprint feature extracting method based on convolution |
| CN114282803A (en) * | 2021-12-23 | 2022-04-05 | 北京东土拓明科技有限公司 | Emergency disposal method and device, computing equipment and computer readable storage medium |
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