CN115017935A - Radio frequency signal emission time detection method based on sliding window variance track - Google Patents
Radio frequency signal emission time detection method based on sliding window variance track Download PDFInfo
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Abstract
The invention relates to a radio frequency signal emission moment detection method based on a sliding window variance track, which belongs to the technical field of information safety and is used for acquiring a radio frequency signal; preprocessing the radio frequency signal to obtain a preprocessed radio frequency signal; obtaining a variance track corresponding to the preprocessed radio frequency signal by adopting a sliding window; calculating the posterior probability density of the variance track; the method has the advantages that the relation between the posterior probability density and the mean value of the radio frequency signals is judged, the transmitting time of the radio frequency signals is obtained, meanwhile, the variance track has good robustness to noise interference, the posterior probability density curve has strong pulse compression characteristics, the position of the starting point of the transient signal can be detected even in a severe environment with low signal-to-noise ratio, threshold value and non-parameter estimation do not need to be defined for hypothesis testing, and the method has strong adaptability and popularization.
Description
Technical Field
The invention relates to the technical field of information security, in particular to a radio frequency signal emission moment detection method based on a sliding window variance track.
Background
With the rapid development of the internet of things technology, the internet of everything has become an inevitable trend in the development of the times, and the wireless communication technology has become an essential part in our lives. However, because the openness of wireless communication technology makes it more vulnerable and threatening, network security issues are a major concern. Radio Frequency Fingerprint (RFF) is a unique feature of a device obtained by analyzing a received Radio Frequency signal, so as to realize identification and authentication of a target device. Once the radio frequency fingerprint identification technology is put forward, the radio frequency fingerprint identification technology has received wide attention at home and abroad. The existing radio frequency fingerprint technology is divided into a transient radio frequency fingerprint technology and a steady radio frequency fingerprint technology according to a target signal interval of extracted features. The problem of detecting the starting point of the transient signal plays an important role in correctly extracting the transient signal.
At present, the following 6 methods are commonly used for the transient detection problem: Short-Time Energy Detection (STED), Bayesian Step Change Detection (BSCD), Bayesian Ramp Change Detection (BRCD), Variance Fractal Dimension Threshold Detection (VFDTD), Phase Detection (PD), and Mean Change Point Detection (MCPD), where the MCPD is a starting Point for detecting transient signals based on Fractal features of sampled data. In this method, a Higuchi method is used to calculate the fractal dimension of a signal continuous segment, but a non-stationary signal like a transient is not a pure fractal, and its fractal is time-varying, so that a signal having a local fractal dimension is processed by multi-fractal, which is complicated in calculation, slow in operation speed, and poor in detection capability for a transient signal of small amplitude.
Disclosure of Invention
In order to solve the defects of high calculation complexity and low calculation speed of Bayes step change detection caused by calculating the local fractal dimension of the continuous part of the signal, the invention provides the technical scheme that: a radio frequency signal emission moment detection method based on a sliding window variance track comprises the following steps:
acquiring a radio frequency signal;
preprocessing the radio frequency signal to obtain a preprocessed radio frequency signal;
obtaining a variance track corresponding to the preprocessed radio frequency signal by adopting a sliding window;
calculating the posterior probability density of the variance track;
and judging the relation between the posterior probability density and the mean value of the radio frequency signals to obtain the transmitting time of the radio frequency signals.
Further, the method comprises the following steps: the pre-processing of the radio frequency signal comprises the following steps:
filtering the radio frequency signal through a band-pass filter;
and carrying out normalization processing on the filtered radio frequency signals.
Further: the method for obtaining the variance track corresponding to the preprocessed radio frequency signal by adopting the sliding window comprises the following steps:
dividing the preprocessed radio frequency signal by using a sliding window to obtain a corresponding truncated time signal sequence;
and calculating the amplitude variance of the corresponding truncated time signal sequence to obtain a scalar, and obtaining a corresponding variance track along with the movement of the starting point of the sliding window after taking the logarithm of the scalar.
Further: the posterior probability density of the variance track is calculated by adopting a posterior probability density formula as follows:
wherein: p ({ m } | V) is posterior probability density, and m is a discrete sample point index number corresponding to the maximum value of the posterior probability density; v (i) log (V) sd (i)),V sd (i) Is a scalar quantity, N denotes the length of the radio frequency signal and i denotes the index number of the discrete sample points.
Further, the determining the relationship between the posterior probability density and the mean value of the radio frequency signal to obtain the transmission time of the radio frequency signal includes:
when the maximum value of the posterior probability density is larger than the mean value of the radio frequency signals, the discrete sample point index number corresponding to the maximum value of the posterior probability density is the initial moment of the transient signals; otherwise, the radio frequency signal is pure noise.
A radio frequency signal emission moment detection device based on a sliding window variance track comprises:
an acquisition module: for acquiring a radio frequency signal;
the preprocessing module is used for preprocessing the radio frequency signal to obtain a preprocessed radio frequency signal;
a calculation module I: the method comprises the steps of obtaining a variance track corresponding to a preprocessed radio frequency signal by adopting a sliding window;
a calculating module II: a posterior probability density for calculating the variance trajectory;
and the judging module is used for judging the relation between the posterior probability density and the mean value of the radio frequency signals to obtain the transmitting time of the radio frequency signals.
The invention provides a radio frequency signal emission moment detection method based on a sliding window variance track, which has the following advantages: by the method, the initial point position of the transient signal is determined, the detection precision of a signal target area is ensured while the complexity of the Bayes step change detection method is simplified, the speed of the Bayes step change detection method is further improved, and the initial point of the transient signal is accurately detected. Meanwhile, the variance track has better robustness on noise interference, a posterior probability density curve has stronger pulse compression characteristic, the position of the starting point of the transient signal can be detected even in a severe environment with low signal-to-noise ratio, a threshold value and nonparametric estimation do not need to be defined for hypothesis testing, and the method has stronger adaptability and popularization.
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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 used in the embodiments or the description of 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 flow chart of a method of the present invention;
FIG. 2(a) is a plot of the variance trace of the present invention at high signal-to-noise ratio and (b) is a plot of the posterior probability density of the present invention at high signal-to-noise ratio;
FIG. 3(a) is a plot of the variance trace of the present invention at low signal-to-noise ratio and (b) is a plot of the posterior probability density of the present invention at low signal-to-noise ratio;
FIG. 4(a) is a plot of the variance trace of Bayesian step change detection at high SNR, and (b) is a plot of the posterior probability density of the present invention at high SNR;
FIG. 5(a) is a plot of the variance trace of Bayesian step change detection at low signal-to-noise ratio, and (b) is a plot of the posterior probability density of the present invention at low signal-to-noise ratio.
Detailed Description
It should be noted that, in the case of conflict, the embodiments and features of the embodiments of the present invention may be combined with each other, and the present invention will be described in detail with reference to the accompanying drawings and embodiments.
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, 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. The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the invention, its application, or uses. 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.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention. As used herein, the singular forms "a", "an", and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
The relative arrangement of the components and steps, the numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present invention unless specifically stated otherwise. Meanwhile, it should be understood that the sizes of the respective portions shown in the drawings are not drawn in an actual proportional relationship for the convenience of description. Techniques, methods, and apparatus known to those of ordinary skill in the relevant art may not be discussed in detail but are intended to be part of the specification where appropriate. Any specific values in all examples shown and discussed herein are to be construed as exemplary only and not as limiting. Thus, other examples of the exemplary embodiments may have different values. It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, further discussion thereof is not required in subsequent figures.
In the description of the present invention, it is to be understood that the orientation or positional relationship indicated by the directional terms such as "front, rear, upper, lower, left, right", "lateral, vertical, horizontal" and "top, bottom", etc., are generally based on the orientation or positional relationship shown in the drawings, and are used for convenience of description and simplicity of description only, and in the absence of any contrary indication, these directional terms are not intended to indicate and imply that the device or element so referred to must have a particular orientation or be constructed and operated in a particular orientation, and therefore should not be considered as limiting the scope of the present invention: the terms "inner and outer" refer to the inner and outer relative to the profile of the respective component itself.
Spatially relative terms, such as "above … …," "above … …," "above … …," "above," and the like, may be used herein for ease of description to describe one device or feature's spatial relationship to another device or feature as illustrated in the figures. It will be understood that the spatially relative terms are intended to encompass different orientations of the device in use or operation in addition to the orientation depicted in the figures. For example, if a device in the figures is turned over, devices described as "above" or "on" other devices or configurations would then be oriented "below" or "under" the other devices or configurations. Thus, the exemplary term "above … …" can include both an orientation of "above … …" and "below … …". The device may be otherwise variously oriented (rotated 90 degrees or at other orientations) and the spatially relative descriptors used herein interpreted accordingly.
It should be noted that the terms "first", "second", and the like are used to define the components, and are only used for convenience of distinguishing the corresponding components, and the terms have no special meanings unless otherwise stated, and therefore, the scope of the present invention should not be construed as being limited.
FIG. 1 is a flow chart of the method of the present invention;
a radio frequency signal emission moment detection method based on a sliding window variance track comprises the following steps:
s1, acquiring radio frequency signals; bluetooth wireless signals can be selected as detection targets, and real signals are directly captured through a high sampling rate oscilloscope and a low resolution analog-to-digital converter;
s2, preprocessing the radio frequency signal to obtain a preprocessed radio frequency signal;
s3, obtaining a variance track corresponding to the preprocessed radio frequency signal by adopting a sliding window;
s4, calculating the posterior probability density of the variance track;
and S5, judging the relation between the posterior probability density and the mean value of the radio frequency signal to obtain the transmitting time of the radio frequency signal.
Steps S1, S2, S3, S4, S5 are sequentially performed;
further: the pre-processing of the radio frequency signal comprises the following steps:
in the signal acquisition process, because the oscilloscope generates some unnecessary stray signals, the radio frequency signal is filtered through the band-pass filter to filter the unnecessary stray signals in the signal
And carrying out normalization processing on the filtered radio frequency signals, so that the extracted features can represent the generalized features of the transmitting device.
Further: the method for obtaining the variance track corresponding to the preprocessed radio frequency signal by adopting the sliding window comprises the following steps:
dividing discrete RF signal s (N) with total length N by using sliding window with length L, when starting point of sliding window is moved to i, obtaining correspondent truncated signal defined as s d (i) In that respect Because the modulation signal is a carrier wave periodic oscillation, the probability of various amplitude values is equal in an amplitude range, namely, equal probability distribution is presented, and the statistical characteristic of the modulation signal is obviously different from the statistical characteristic of noise before the emission moment. Therefore, the temperature of the molten metal is controlled,
calculating the amplitude variance of the corresponding truncated time signal sequence to obtain a scalar V sd (i)，
Since the actual signal strength is usually weak, to amplify the variance value for the subsequent posterior probability density calculation, the scalar is logarithmized by V (i) -log (V) sd (i) With the start point of the sliding window moving, the corresponding variance track is obtained.
Further: by using the Bayesian Step Change Detection (BSCD) algorithm, the posterior probability density of the calculated variance locus adopts the formula of posterior probability density (conditional probability) as follows:
wherein: p ({ m } | V)) For the posterior probability density, m is the discrete sample point index number corresponding to the maximum value of the posterior probability density, i.e. the starting time of the transient signal, but in practical application, when the length N of the target signal is relatively large, the power exponent in the above formula
It will be too large, resulting in overflow of the value obtained by the numerical calculation, so that the power in the formula can be fixed to a reasonably fixed value.
Since the actual signal strength is usually weak, the scalar V is used to amplify the variance value for the subsequent calculation of the posterior probability density sd (i) Taking the logarithm to obtain:
V(i)＝log(V sd (i)) (3)
wherein, V sd (i) And m is a scalar, in the variance track V, the index number of the discrete sample point corresponding to the transient starting moment, N represents the length of the radio frequency signal, and i represents the index number of the discrete sample point.
Further, the method comprises the following steps: judging the relationship between the posterior probability density and the mean value of the radio frequency signals to obtain the transmission time of the radio frequency signals comprises the following steps:
when the maximum value of the posterior probability density is larger than the mean value of the radio frequency signals, the discrete sample point index number corresponding to the maximum value of the posterior probability density is the initial time (emission time) of the transient signal;
when the maximum value of the posterior probability density is less than or equal to the mean value of the radio frequency signals, the radio frequency signals are pure noise.
A radio frequency signal emission moment detection device based on a sliding window variance track comprises:
an acquisition module: for acquiring a radio frequency signal;
the preprocessing module is used for preprocessing the radio frequency signal to obtain a preprocessed radio frequency signal;
a calculation module I: the variance track corresponding to the preprocessed radio frequency signal is obtained by adopting a sliding window;
a calculating module II: a posterior probability density for calculating the variance trajectory;
and the judging module is used for judging the relation between the posterior probability density and the mean value of the radio frequency signals to obtain the transmitting time of the radio frequency signals.
FIG. 2(a) is a plot of the variance trace of the present invention at high signal-to-noise ratio and (b) is a plot of the posterior probability density of the present invention at high signal-to-noise ratio;
FIG. 3(a) is a plot of the variance trace of the present invention at low signal-to-noise ratio and (b) is a plot of the posterior probability density of the present invention at low signal-to-noise ratio;
FIG. 4(a) is a plot of the variance trace of Bayesian step change detection at high SNR, and (b) is a plot of the posterior probability density of the present invention at high SNR;
FIG. 5(a) is a plot of the variance trace of Bayesian step change detection at low signal-to-noise ratio, and (b) is a plot of the posterior probability density of the present invention at low signal-to-noise ratio.
From the above results, it can be seen that both the variance trace and the fractal trace have better robustness to noise interference, and the positions of the starting points of the transient signals calculated by the two methods are approximately the same, and the positions of the starting points of the transient signals can still be measured in the environment with low signal-to-noise ratio. However, the method for detecting the radio frequency signal emission time based on the sliding window variance track is low in complexity, fast in operation and high in popularization and applicability.
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 (6)
1. A radio frequency signal emission moment detection method based on a sliding window variance track is characterized in that: the method comprises the following steps:
acquiring a radio frequency signal;
preprocessing the radio frequency signal to obtain a preprocessed radio frequency signal;
obtaining a variance track corresponding to the preprocessed radio frequency signal by adopting a sliding window;
calculating the posterior probability density of the variance track;
and judging the relation between the posterior probability density and the mean value of the radio frequency signals to obtain the transmitting time of the radio frequency signals.
2. The method of claim 1, wherein the method comprises: the pre-processing of the radio frequency signal comprises the following steps:
filtering the radio frequency signal through a band-pass filter;
and carrying out normalization processing on the filtered radio frequency signals.
3. The method of claim 1, wherein the method comprises: the method for obtaining the variance track corresponding to the preprocessed radio frequency signal by adopting the sliding window comprises the following steps:
dividing the preprocessed radio frequency signal by using a sliding window to obtain a corresponding truncated time signal sequence;
and calculating the amplitude variance of the corresponding truncated time signal sequence to obtain a scalar, and taking logarithm of the scalar to obtain a corresponding variance track along with the movement of the starting point of the sliding window.
4. The method for detecting the transmission time of a radio frequency signal based on a sliding window variance track according to claim 1, wherein the sliding window variance track comprises: the posterior probability density of the variance track is calculated by adopting a posterior probability density formula as follows:
wherein: p ({ m } | V) is the posterior probability density, and m is the discrete sample corresponding to the maximum value of the posterior probability densityPoint index number; v (i) log (V) sd (i)),V sd (i) Is a scalar quantity, N denotes the length of the radio frequency signal and i denotes the index number of the discrete sample points.
5. The method of claim 1, wherein the method comprises: judging the relationship between the posterior probability density and the mean value of the radio frequency signals to obtain the transmission time of the radio frequency signals comprises the following steps:
when the maximum value of the posterior probability density is larger than the mean value of the radio frequency signals, the discrete sample point index number corresponding to the maximum value of the posterior probability density is the initial moment of the transient signals; otherwise, the radio frequency signal is pure noise.
6. A radio frequency signal emission moment detection device based on a sliding window variance track is characterized in that: the method comprises the following steps:
an acquisition module: for acquiring a radio frequency signal;
the preprocessing module is used for preprocessing the radio frequency signal to obtain a preprocessed radio frequency signal;
a calculating module I: the method comprises the steps of obtaining a variance track corresponding to a preprocessed radio frequency signal by adopting a sliding window;
a calculating module II: a posterior probability density for calculating the variance trajectory;
and the judging module is used for judging the relation between the posterior probability density and the mean value of the radio frequency signals to obtain the transmitting time of the radio frequency signals.
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