CN112350787A - Radio signal abnormity detection method - Google Patents
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Abstract
The invention discloses a radio signal abnormity detection method, which comprises the following steps: s1, collecting two sections of radio signal data which normally work, and calculating a first relative wavelet time entropy; s2, obtaining a time entropy threshold value according to the curve fluctuation range of the first relative wavelet time entropy; s3, collecting the radio signal data which normally works and the radio signal data to be detected, and calculating a second relative wavelet time entropy; s4, counting the number of sample points of the radio signal data with the second relative wavelet time entropy larger than the time entropy threshold value, and calculating the abnormal signal ratio; s5, judging whether the abnormal signal ratio is larger than the abnormal signal ratio threshold value, if so, determining that the radio signal data to be detected is abnormal, otherwise, skipping to the step S3; the invention solves the problems that the prior art is mostly classified based on a supervised method and the abnormal signal type is defined by adopting an artificial standard.
Description
Technical Field
The invention relates to the technical field of radio, in particular to a radio signal abnormity detection method.
Background
With the rapid development of electromagnetic technology and wireless communication technology, the form of radio signals shows a diversified trend, the demand of human beings on radio spectrum resources is more and more intense, the radio spectrum is not inexhaustible, and the contradiction between the increasing demand and the limited spectrum resources increases the difficulty for the supervision of the electromagnetic spectrum and the safety guarantee of the electromagnetic space. In recent years, personal use conditions of amateur radio stations, unmanned aerial vehicles and wireless communication equipment are more and more common, and due to the lack of knowledge on electromagnetic space safety, cases of illegal invasion to other wireless communication frequency bands occur, even civil aviation radio receives interference, and safety accidents occur.
The existing frequency spectrum anomaly detection methods are mainly divided into two categories: one method is to use spectrum analysis to determine whether the spectrum state is abnormal by analyzing the variation of the spectrum characteristic parameters, such as: [1] majie, belladonna, zhouxiang, smithspring, abnormal electromagnetic signal monitoring model based on immune network [ J ] computer application research, 2011; the other method is to use a supervised machine learning algorithm to perform two classifications to judge whether the frequency spectrum is abnormal or not, such as: support vector machines, naive bayes classification, etc.
On one hand, in an actual radio propagation frequency band, radio signals are in a normal working state in most of time, the probability of occurrence of abnormity is relatively low, and due to the fact that a radio system is complex, frequency spectrum signals at a detection end of the system are abnormal due to various reasons such as internal faults of the system, external interference signals and the like, the sample acquisition difficulty is high, the supervised detection method is difficult to fully master experience knowledge, and therefore the detection precision is influenced; on the other hand, the algorithm is based on specific tasks and scenes, and the type of the abnormal signal is defined by human standards, so that the algorithm has limitations.
Disclosure of Invention
Aiming at the defects in the prior art, the radio signal abnormality detection method provided by the invention solves the problems that the prior art is mostly classified based on a supervised method and the type of an abnormal signal is defined by an artificial standard.
In order to achieve the purpose of the invention, the invention adopts the technical scheme that: a radio signal abnormality detection method comprising the steps of:
s1, collecting two sections of radio signal data which normally work, and calculating a first relative wavelet time entropy;
s2, obtaining a time entropy threshold value according to the curve fluctuation range of the first relative wavelet time entropy;
s3, collecting the radio signal data which normally works and the radio signal data to be detected, and calculating a second relative wavelet time entropy;
s4, counting the number of sample points of the radio signal data with the second relative wavelet time entropy larger than the time entropy threshold value, and calculating the abnormal signal ratio;
and S5, judging whether the abnormal signal ratio is larger than the abnormal signal ratio threshold value, if so, judging that the radio signal data to be detected is abnormal, otherwise, jumping to the step S3.
Further, step S1 includes the following substeps:
s11, collecting two sections of radio signal data which normally work, and respectively carrying out N-layer discrete wavelet transform processing on the two sections of radio signal data which normally work to obtain a first N-layer wavelet coefficient and a second N-layer wavelet coefficient;
s12, determining the size of a sliding window according to the first N-layer wavelet coefficient and the second N-layer wavelet coefficient, and calculating the relative wavelet energy of the first time evolution and the relative wavelet energy of the second time evolution;
and S13, calculating the first relative wavelet time entropy according to the relative wavelet energy of the first time evolution and the relative wavelet energy of the second time evolution.
Further, the calculation formula of the first relative wavelet temporal entropy in step S13 is:
wherein the content of the first and second substances,
is a first relative wavelet temporal entropy, NjIs the number of wavelet coefficients contained in the ith window of the jth layer, N is the layer number of the wavelet coefficients, W is the size of the sliding window, C'j(k) Is the kth wavelet coefficient of the jth layer in the first N layers of wavelet coefficients, C ″j(k) Is the kth wavelet coefficient of the jth layer in the second N layers of wavelet coefficients.
Further, step S3 includes the following substeps:
s31, collecting the radio signal data which normally work and the radio signal data to be detected, and respectively carrying out N-layer discrete wavelet transform processing on the radio signal data which normally work and the radio signal data to be detected to obtain a third N-layer wavelet coefficient and a fourth N-layer wavelet coefficient;
s32, determining the size of a sliding window according to the third N-layer wavelet coefficient and the fourth N-layer wavelet coefficient, and calculating the relative wavelet energy of the third time evolution and the relative wavelet energy of the fourth time evolution;
and S33, calculating the second relative wavelet time entropy according to the relative wavelet energy of the third time evolution and the relative wavelet energy of the fourth time evolution.
Further, the calculation formula of the second relative wavelet temporal entropy in step S33 is:
wherein the content of the first and second substances,
is the second relative wavelet temporal entropy, NjIs the number of wavelet coefficients contained in the ith window of the jth layer, N is the layer number of the wavelet coefficients, W is the size of the sliding window, D'j(k) Is the kth wavelet coefficient of the jth layer in the wavelet coefficients of the third N layers, D ″j(k) Is the kth wavelet coefficient of the jth layer in the wavelet coefficients of the third N layers.
The beneficial effects of the above further scheme are: the spectrum abnormal state detection is carried out through the relative wavelet time entropy, an unsupervised method is established for classification, the time entropy threshold is obtained by calculating two sections of radio signals in normal working, and the method has a good detection effect when the abnormal state with malicious interference and the abnormal state with an unauthorized target occur.
Further, the calculation formula of the abnormal signal ratio in step S4 is:
p＝L/M
wherein p is the abnormal signal ratio, L is the number of sample points of the radio signal data of which the second relative wavelet time entropy is greater than the time entropy threshold, and M is the total number of sample points of the radio signal data which normally works or the radio signal data to be detected.
Further, the abnormal signal ratio threshold value is 50% in step S5.
In conclusion, the beneficial effects of the invention are as follows: the time entropy threshold value of the radio signal in normal work is determined through the relative wavelet time entropy, when the signal to be detected is abnormal, the relative wavelet time entropy of the signal to be detected and the normal signal is inevitably fluctuated in a large range and exceeds the time entropy threshold value, and at the moment, whether the radio signal to be detected is abnormal or not can be judged as long as the value of the abnormal signal ratio is obtained through statistics.
Drawings
Fig. 1 is a flowchart of a radio signal abnormality detection method.
Detailed Description
The following description of the embodiments of the present invention is provided to facilitate the understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and it will be apparent to those skilled in the art that various changes may be made without departing from the spirit and scope of the invention as defined and defined in the appended claims, and all matters produced by the invention using the inventive concept are protected.
As shown in fig. 1, a radio signal abnormality detection method includes the steps of:
s1, collecting two sections of radio signal data which normally work, and calculating a first relative wavelet time entropy;
step S1 includes the following substeps:
s11, collecting two sections of radio signal data which normally work, and respectively carrying out N-layer discrete wavelet transform processing on the two sections of radio signal data which normally work to obtain a first N-layer wavelet coefficient and a second N-layer wavelet coefficient;
s12, determining the size of a sliding window according to the first N-layer wavelet coefficient and the second N-layer wavelet coefficient, and calculating the relative wavelet energy of the first time evolution and the relative wavelet energy of the second time evolution;
and S13, calculating the first relative wavelet time entropy according to the relative wavelet energy of the first time evolution and the relative wavelet energy of the second time evolution.
The calculation formula of the first relative wavelet temporal entropy in step S13 is:
wherein the content of the first and second substances,
is a first relative wavelet temporal entropy, NjIs the number of wavelet coefficients contained in the ith window of the jth layer, N is the layer number of the wavelet coefficients, W is the size of the sliding window, C'j(k) Is the kth wavelet coefficient of the jth layer in the first N layers of wavelet coefficients, C ″j(k) The kth wavelet coefficient of the jth layer in the second N layers of wavelet coefficients;
for the relative wavelet energy of the first time evolution,
relative wavelet energy for a second time evolution;
s2, obtaining a time entropy threshold value according to the curve fluctuation range of the first relative wavelet time entropy;
s3, collecting the radio signal data which normally works and the radio signal data to be detected, and calculating a second relative wavelet time entropy;
step S3 includes the following substeps:
s31, collecting the radio signal data which normally work and the radio signal data to be detected, and respectively carrying out N-layer discrete wavelet transform processing on the radio signal data which normally work and the radio signal data to be detected to obtain a third N-layer wavelet coefficient and a fourth N-layer wavelet coefficient;
s32, determining the size of a sliding window according to the third N-layer wavelet coefficient and the fourth N-layer wavelet coefficient, and calculating the relative wavelet energy of the third time evolution and the relative wavelet energy of the fourth time evolution;
and S33, calculating the second relative wavelet time entropy according to the relative wavelet energy of the third time evolution and the relative wavelet energy of the fourth time evolution.
The calculation formula of the second relative wavelet temporal entropy in step S33 is:
wherein the content of the first and second substances,
is the second relative wavelet temporal entropy, NjThe number of wavelet coefficients contained in the ith window of the jth layer, N the number of layers of wavelet coefficients, and W the size of the sliding window (according to the number of sampling points of the radio signal, the larger W is in the range of 2048 to 20480, the more the detection effect isPreferably, but W cannot be too large, and the significance of researching the evolution of relative wavelet time entropy along with time domain is lost if the value is too large), D'j(k) Is the kth wavelet coefficient of the jth layer in the wavelet coefficients of the third N layers, D ″j(k) For the jth wavelet coefficient of the jth layer of the third N layers of wavelet coefficients,
for the relative wavelet energy of the third time evolution,
relative wavelet energy for a fourth time evolution;
s4, counting the number of sample points of the radio signal data with the second relative wavelet time entropy larger than the time entropy threshold value, and calculating the abnormal signal ratio;
the calculation formula of the abnormal signal ratio in step S4 is:
p＝L/M
wherein p is the abnormal signal ratio, L is the number of sample points of the radio signal data of which the second relative wavelet time entropy is greater than the time entropy threshold, and M is the total number of sample points of the radio signal data which normally works or the radio signal data to be detected.
And S5, judging whether the abnormal signal ratio is larger than the abnormal signal ratio threshold value, if so, judging that the radio signal data to be detected is abnormal, otherwise, jumping to the step S3.
The abnormal signal ratio threshold value in step S5 is 50%.
Claims (7)
1. A radio signal abnormality detection method characterized by comprising the steps of:
s1, collecting two sections of radio signal data which normally work, and calculating a first relative wavelet time entropy;
s2, obtaining a time entropy threshold value according to the curve fluctuation range of the first relative wavelet time entropy;
s3, collecting the radio signal data which normally works and the radio signal data to be detected, and calculating a second relative wavelet time entropy;
s4, counting the number of sample points of the radio signal data with the second relative wavelet time entropy larger than the time entropy threshold value, and calculating the abnormal signal ratio;
and S5, judging whether the abnormal signal ratio is larger than the abnormal signal ratio threshold value, if so, judging that the radio signal data to be detected is abnormal, otherwise, jumping to the step S3.
2. The radio signal abnormality detection method according to claim 1, characterized in that said step S1 includes the sub-steps of:
s11, collecting two sections of radio signal data which normally work, and respectively carrying out N-layer discrete wavelet transform processing on the two sections of radio signal data which normally work to obtain a first N-layer wavelet coefficient and a second N-layer wavelet coefficient;
s12, determining the size of a sliding window according to the first N-layer wavelet coefficient and the second N-layer wavelet coefficient, and calculating the relative wavelet energy of the first time evolution and the relative wavelet energy of the second time evolution;
and S13, calculating the first relative wavelet time entropy according to the relative wavelet energy of the first time evolution and the relative wavelet energy of the second time evolution.
3. The radio signal abnormality detection method according to claim 2, characterized in that the calculation formula of the first relative wavelet temporal entropy in said step S13 is:
wherein the content of the first and second substances,
is a first relative wavelet temporal entropy, NjIs the number of wavelet coefficients contained in the ith window of the jth layer, N is the layer number of the wavelet coefficients, W is the size of the sliding window, C'j(k) Is the kth wavelet coefficient of the jth layer in the first N layers of wavelet coefficients, C ″j(k) As a second N-layer wavelet systemThe kth wavelet coefficient of the jth layer in the number.
4. The radio signal abnormality detection method according to claim 1, characterized in that said step S3 includes the sub-steps of:
s31, collecting the radio signal data which normally work and the radio signal data to be detected, and respectively carrying out N-layer discrete wavelet transform processing on the radio signal data which normally work and the radio signal data to be detected to obtain a third N-layer wavelet coefficient and a fourth N-layer wavelet coefficient;
s32, determining the size of a sliding window according to the third N-layer wavelet coefficient and the fourth N-layer wavelet coefficient, and calculating the relative wavelet energy of the third time evolution and the relative wavelet energy of the fourth time evolution;
and S33, calculating the second relative wavelet time entropy according to the relative wavelet energy of the third time evolution and the relative wavelet energy of the fourth time evolution.
5. The method for detecting abnormality in radio signal according to claim 4, wherein the calculation formula of the second relative wavelet temporal entropy in said step S33 is:
wherein the content of the first and second substances,
is the second relative wavelet temporal entropy, NjIs the number of wavelet coefficients contained in the ith window of the jth layer, N is the layer number of the wavelet coefficients, W is the size of the sliding window, D'j(k) Is the kth wavelet coefficient of the jth layer in the wavelet coefficients of the third N layers, D ″j(k) Is the kth wavelet coefficient of the jth layer in the wavelet coefficients of the third N layers.
6. The radio signal abnormality detection method according to claim 1, characterized in that the calculation formula of the abnormal signal ratio in said step S4 is:
p＝L/M
wherein p is the abnormal signal ratio, L is the number of sample points of the radio signal data of which the second relative wavelet time entropy is greater than the time entropy threshold, and M is the total number of sample points of the radio signal data which normally works or the radio signal data to be detected.
7. The radio signal abnormality detection method according to claim 1, characterized in that the abnormal signal ratio threshold value in said step S5 is 50%.
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