CN112083448A - Interference signal classification and identification feature extraction method and system for satellite navigation system - Google Patents
Interference signal classification and identification feature extraction method and system for satellite navigation system Download PDFInfo
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Abstract
The invention belongs to the technical field of anti-interference of satellite navigation systems, and particularly relates to a satellite navigation system-oriented interference signal classification and identification feature extraction method and system. The method for extracting the interference signal classification and identification features facing the satellite navigation system can realize accurate identification of six interference types, solves the problem of excessive feature parameters of a decision tree classification method, solves the problem of low identification rate of a box dimension-based classification algorithm under the condition of low JNR (just noticeable ratio), and can improve the anti-interference performance of the satellite navigation system. The invention provides an interference signal classification and identification feature extraction system for a satellite navigation system, which extracts a frequency domain Sevcik fractal dimension D and a spectrum flatness F of an interference signalseThe two characteristic parameters are combined into a two-bit characteristic vector, and then classification and identification are carried out through the SVM, so that the timeliness and the accuracy of the identification algorithm are effectively improved.
Description
Technical Field
The invention belongs to the technical field of anti-interference of satellite navigation systems, and particularly relates to a satellite navigation system-oriented interference signal classification and identification feature extraction method and system.
Background
The satellite navigation system is influenced by complex electromagnetic interference in space, the satellite navigation system faces huge challenges in usability and reliability, navigation unreliability events caused by interference are frequent, at present, scholars at home and abroad propose a plurality of anti-interference means, but one means can deal with all interference types does not exist, so that the type of an interference signal needs to be analyzed, different anti-interference means are adopted according to different types of the interference signal, and necessary guarantee is provided for the smooth construction and normal operation of the satellite navigation system.
Interference type recognition simulation is carried out in a Frequency Hopping System by Meng, X.Y. and the like in a Conference of 2010First International Conference on permanent Computing, Signal Processing and Applications, and a text entitled "An Intelligent Anti-jamming Frequency Hopping System" extracts a plurality of characteristics in a time domain, a Frequency domain and a transformation domain to realize recognition of a plurality of interference types, but the accuracy of the interference recognition is reduced by adopting a decision tree classifier manually setting a threshold value.
One trip of the same year, he et al published a text entitled "interference identification algorithm based on complexity features" in the journal of military communication technology, which extracts two signal complexity features, namely L-Z complexity and fractal dimension, of an interference signal as characteristic parameters for interference signal type identification and classifies the two signal complexity features by a support vector machine. But the recognition performance of the algorithm for single tone interference is inferior to that of the decision tree algorithm.
Huanghao et al in 2014 published a article in journal of air force early warning academy of academic, entitled "interference identification method based on fractal box dimension and wavelet packet energy", which extracts the box dimension of a received signal, the wavelet packet energy and characteristic parameters used as a classifier, and designs a BT-SVM classifier for interference identification, but the box dimension is greatly influenced by noise, and the identification rate is not high when the signal-to-noise ratio is low.
Disclosure of Invention
The invention aims to provide a method for extracting fractal dimension D and spectral flatness F based on frequency domain SevcikseThe method for extracting the interference signal classification and identification features of the two-dimensional feature parameters facing the satellite navigation system.
The purpose of the invention is realized by the following technical scheme: the method comprises the following steps:
step 1: sampling a received signal y (t) to obtain a sampling sequence { y (N) }, N ═ 1., N of the received signal;
step 2: FFT conversion is carried out on the sampling sequence { y (n) } of the received signal, and the frequency spectrum { f (n) } of the sampling sequence of the received signal is obtained;
and step 3: normalizing the frequency spectrum (f (n)) of the received signal sampling sequence to obtain a normalized frequency spectrum (f) of the received signal sampling sequence*(n)}；
And 4, step 4: calculating a normalized spectrum f of a sample sequence of the received signal*(n) } frequency domain Sevcik fractal dimension D;
and 5: calculating a normalized spectrum f of a sample sequence of the received signal*Normalized spectral flatness F of (n) }se；
Wherein M is the width of a set sliding window;
step 6: outputting the characteristic vector T ═ D, F of the received signal y (T)se]And finishing the extraction of the classification identification characteristics of the received signals.
The invention also aims to provide a satellite navigation system-oriented interference signal classification and identification feature extraction system.
The purpose of the invention is realized by the following technical scheme: the device comprises a signal receiving module, a sampling module, an FFT (fast Fourier transform) module, a normalization module, a frequency domain Sevcik fractal dimension calculation module, a frequency spectrum flatness calculation module and a mapping module; the signal receiving module transmits the received signal y (t) to the sampling module; the sampling module samples the received signal y (t) to obtain a sampling sequence of the received signal (y (N)), (N is 1., and N is the sampling number), and the sampling module transmits the sampling sequence of the received signal (y (N)) } to the FFT conversion module; the FFT conversion module carries out FFT conversion on the sampling sequence { y (n) } of the received signal to obtain a frequency spectrum { f (n) } of the sampling sequence of the received signal, and the FFT conversion module transmits the frequency spectrum { f (n) } of the sampling sequence of the received signal to the normalization module; the normalization module normalizes the frequency spectrum { f (n) } of the received signal sampling sequence to obtain the normalized frequency spectrum { f of the received signal sampling sequence*(n), the normalization module normalizes the normalized spectrum of the received signal sample sequence { f*(n) transmitting the frequency domain Sevcik fractal dimension calculation module and the frequency spectrum flatness calculation module; the frequency domain Sevcik fractal dimension calculation module extracts the normalized frequency spectrum { f) of the received signal sampling sequence*(n) transmitting the frequency domain Sevcik fractal dimension D as a first characteristic parameter to a mapping module; the calculation of the spectral flatnessModule extracts the normalized spectrum { f) of a sample sequence of a received signal*Normalized spectral flatness F of (n) }seAs a second characteristic parameter to the mapping module; the mapping module maps a first characteristic parameter D and a second characteristic parameter FseComponent feature vector T ═ D, Fse]And outputting to finish the classification and identification feature extraction of the received signals.
The present invention may further comprise:
the frequency domain Sevcik fractal dimension calculation module extracts the normalized frequency spectrum { f) of the received signal sampling sequence*(n) the specific process of the frequency domain Sevcik fractal dimension D is as follows:
step 3.1: calculating a normalized spectrum f of a sample sequence of the received signal*(n) distance L between every two sampling pointsi；
Step 3.2: calculating the distance L between all sampling pointsiSumming to obtain the length L of the waveform;
step 3.3: calculating a normalized spectrum f of a sample sequence of the received signal*(n) } frequency domain Sevcik fractal dimension D;
the spectrum flatness calculation module extracts the normalized spectrum { f of the receiving signal sample sequence*Normalized spectral flatness F of (n) }seThe specific process comprises the following steps:
step 4.1: normalized spectrum f of a sample sequence of a received signal*(n) is input to a sliding window function processing module which computes an average spectrum of the received signal sample sequence
Wherein M is the width of a set sliding window;
step 4.2: normalized spectrum f of a sample sequence of a received signal*(n) and average spectrum
Taking the difference to obtain the steep part in the normalized frequency spectrum of the received signal sample sequence
Step 4.3: calculating the standard deviation of the steep part in the normalized frequency spectrum of the received signal sample sequence to obtain the normalized frequency spectrum { f of the received signal sample sequence*Normalized spectral flatness F of (n) }se；
The invention has the beneficial effects that:
the method for extracting the interference signal classification and identification features oriented to the satellite navigation system can accurately identify six interference types including narrow-band noise interference, broadband noise interference, single-tone interference, multi-tone interference, pulse interference and linear frequency sweep interference, solves the problem of excessive feature parameters of a decision tree classification method, and simultaneously solves the problem of excessive feature parameters of the decision tree classification methodThe problem of low recognition rate of a box dimension-based classification algorithm under the condition of low JNR is solved, and the anti-interference performance of a satellite navigation system can be improved. The invention provides a satellite navigation system-oriented interference signal classification recognition feature extraction system aiming at the problems that a plurality of features need to be extracted by a decision tree recognition algorithm and the recognition rate of a box-dimension feature-based recognition algorithm in an SVM recognition algorithm is not high under the condition of low JNR (just noticeable noise), wherein the system extracts the frequency domain Sevcik fractal dimension D and the frequency spectrum flatness F of an interference signalseThe two characteristic parameters are combined into a two-bit characteristic vector, and then classification and identification are carried out through the SVM, so that the timeliness and the accuracy of the identification algorithm are effectively improved.
Drawings
Fig. 1 is a system block diagram of an interference signal classification and identification feature extraction system for a satellite navigation system according to the present invention.
Fig. 2 is a flow chart of the implementation of extracting the fractal dimension of the interference signal frequency domain Sevcik in the present invention.
Fig. 3 is a flow chart of the implementation of extracting the spectrum flatness of the interference signal according to the present invention.
Fig. 4 is a graph of frequency domain Sevcik fractal dimension of six interference signals along with change of JNR.
Fig. 5 is a graph of the spectral flatness of the broadband noise interference and linear swept frequency interference signals as a function of JNR.
Fig. 6 is a distribution diagram of two-dimensional feature vectors of six kinds of interference signals.
Detailed Description
The invention is further described below with reference to the accompanying drawings.
Example 1:
the invention provides a fractal dimension D and a spectrum flatness F based on frequency domain SevcikseThe method extracts the two-dimensional characteristic parameters and the interference signal classification and identification characteristic of the satellite navigation system, and the method classifies the fractal dimension D of the frequency domain Sevcik and the flatness F of the frequency spectrumseForming two-dimensional characteristic parameter vector T ═ D, Fse]Then, classification recognition is carried out by adopting an SVM classifier.
A method for extracting interference signal classification and identification features for a satellite navigation system is characterized by comprising the following steps:
step 1: sampling a received signal y (t) to obtain a sampling sequence { y (N) }, N ═ 1., N of the received signal;
step 2: FFT conversion is carried out on the sampling sequence { y (n) } of the received signal, and the frequency spectrum { f (n) } of the sampling sequence of the received signal is obtained;
and step 3: normalizing the frequency spectrum (f (n)) of the received signal sampling sequence to obtain a normalized frequency spectrum (f) of the received signal sampling sequence*(n)}；
And 4, step 4: calculating a normalized spectrum f of a sample sequence of the received signal*(n) } frequency domain Sevcik fractal dimension D;
and 5: calculating a normalized spectrum f of a sample sequence of the received signal*Normalized spectral flatness F of (n) }se；
Wherein M is the width of a set sliding window;
step 6: outputting the characteristic vector T ═ D, F of the received signal y (T)se]And finishing the extraction of the classification identification characteristics of the received signals.
The method for extracting the interference signal classification and identification features facing the satellite navigation system can accurately identify six interference types including narrow-band noise interference, broadband noise interference, single-tone interference, multi-tone interference, pulse interference and linear frequency sweep interference, solves the problem of excessive feature parameters of a decision tree classification method, improves the problem of low identification rate of a box dimension-based classification algorithm under the condition of low JNR (just noticeable noise ratio), and can improve the anti-interference performance of the satellite navigation system.
Example 2:
aiming at the problems that a plurality of features need to be extracted by a decision tree recognition algorithm and the recognition rate of a box-dimension feature-based recognition algorithm in an SVM recognition algorithm is not high under the condition of low JNR (just noticeable noise), the invention provides a satellite navigation system-oriented interference signal classification recognition feature extraction system, which extracts the frequency domain Sevcik fractal dimension D and the frequency spectrum flatness F of an interference signalseThe two characteristic parameters are combined into a two-bit characteristic vector, and then classification and identification are carried out through the SVM, so that the timeliness and the accuracy of the identification algorithm are effectively improved.
An interference signal classification and identification feature extraction system for a satellite navigation system comprises an FFT (fast Fourier transform) converter 1, a selector 2, a subtracter 3, a divider 4, a frequency domain Sevcik fractal dimension calculation module 5, a frequency spectrum flatness calculation module 6 and a mapping module 7. The invention adopts two-dimensional characteristic vectors to describe interference signals, respectively extracts frequency domain Sevcik fractal dimension of the interference signals as a first characteristic parameter and frequency spectrum flatness as a second characteristic parameter to form a characteristic vector T ═ D, Fse]And then, recognizing the type of the interference signal by adopting an SVM classifier. The invention simplifies the defect that a decision tree recognition algorithm needs to extract a plurality of features, and the same timeThe method solves the problem that the recognition rate of the recognition algorithm based on the box-dimension feature in the SVM recognition algorithm is not high under the condition of low JNR.
Fig. 1 is a block diagram of an interference signal classification and identification feature extraction system for a satellite navigation system according to the present invention. The meanings of the symbols in fig. 1 are as follows:
y (t): a signal received by a receiver;
{ y (N) } (N ═ 1,2, …, N): y (t) the digital signal after sampling by the sampler;
{ f (N) } (N ═ 1,2, … N): receiving signal frequency spectrum after FFT;
fmax: receiving a signal spectrum maximum;
fmin: receiving a signal spectral minimum;
{f*(N) } (N ═ 1,2, … N): a normalized received signal spectrum;
Fse: spectral flatness;
d: frequency domain Sevcik fractal dimension;
t: a two-dimensional feature vector.
The working process of the interference signal classification and identification feature extraction system facing the satellite navigation system comprises the following steps:
firstly, a received signal y (t) is sampled by a sampler to obtain a sampling sequence of the received signal { y (N) } (N ═ 1, …, N), then FFT conversion is performed by an FFT converter 1 to obtain a spectrum of the received signal { f (N)) } (N ═ 1,2, … N), and then the spectrum of { f (N)) } (N ═ 1,2, … N) is normalized by a selector 2, a subtracter 3 and a divider 4 to obtain a normalized spectrum { f ═ f (N ═ 1,2, … N)*(N) } (N is 1,2, … N), then extracting the frequency domain Sevcik fractal dimension of the interference signal as a first characteristic parameter in a frequency domain Sevcik fractal dimension calculation module 5, extracting the normalized frequency spectrum flatness of the interference signal as a second characteristic parameter in a frequency spectrum flatness calculation module 6, and finally forming a characteristic vector T [ D, F ] by the first characteristic parameter 1 and the second characteristic parameter through a mapping module 7se]。
Example 3:
fig. 2 is a flow chart showing the implementation of the frequency domain Sevcik fractal dimension of the six interference signals extracted respectively according to the present invention, and the fractal dimension is composed of a differentiator, a squarer, an adder, a divider, a square root transformer, and a logarithmic transformer. The meanings of the symbols in fig. 2 are as follows:
Li: receiving the distance between any two points in the signal frequency spectrum sequence;
l: the length of the waveform;
d, frequency domain Sevcik fractal dimension of the received signal.
The invention relates to an interference signal classification and identification feature extraction system for a satellite navigation system, which extracts a frequency domain Sevcik fractal dimension of a received signal as a first feature parameter, wherein the extraction process of the feature is as follows:
1) let the waveform signal consist of a series of points (x)i,yi) Composition, length is N, i is more than or equal to 1 and less than or equal to N, and distance L between two sampling points is calculatediThe expression is
For communication signals xi+1-xi1/(N-1), then
Thus, the distance L between two sampling points is calculated by a differentiator, squarer, adder and square root calculatoriThe expression is
2)LiThe length of the waveform being calculated by an adder, i.e.
3) L is obtained by a logarithm converter to obtain ln (L), the L is summed with the logarithm ln (2) of the constant 2 by an adder, then the sum is subjected to quotient with the logarithm which is 2 times of the sequence number, and then the sum is subjected to constant 1, so that the Sevcik fractal dimension D is obtained
Fig. 4 shows the variation of the frequency domain Sevcik fractal dimension of six interference signals with JNR, and it can be known from fig. 4 that the single tone interference, the multi-tone interference and the pulse interference gradually decrease with the increase of JNR, the fractal dimension of the broadband noise interference floats around 1.8 and basically does not change with the increase of JNR, and when JNR is less than-2 dB, the fractal dimension of the linear sweep interference approaches the fractal dimension of the broadband noise interference according to the frequency domain Svcik, and the accurate identification of the two cannot be realized.
Example 4:
fig. 3 is a flow chart of the implementation of extracting the flatness of the interference signal spectrum according to the present invention, which is composed of a sliding window function processing module, a mean calculator, a subtracter, and a standard deviation calculator, and the meaning of each symbol in fig. 3 is as follows:
Fse: spectral flatness.
The invention relates to an interference signal classification and identification feature extraction system for a satellite navigation system, which extracts the spectral flatness of a received signal as a second feature parameter, wherein the extraction process of the feature is as follows:
1) normalized frequency spectrum { f*(N) } (N is 1,2, … N) is input into the sliding window function processing module, and the average frequency spectrum is calculated by the mean calculator
Namely, it is
The width M of the sliding window is usually taken
2) Normalized spectrum { f) is realized by using a subtracter*(N) } (N is 1,2, … N) and
differencing to obtain a steep portion in the normalized spectrum
Namely, it is
3) Calculated by means of a standard deviation calculator
The standard deviation can obtain the normalized frequency spectrum flatness FseNamely:
FIG. 5 shows the spectral flatness F of the broadband noise interference and linear swept frequency interference signalseAs JNR varies, it can be seen from the figure that as JNR increases, the F of the wide-band noise interference increasesseBasically unchanged, floating around 1 and linear sweep frequency interference FseThe JNR gradually decreases along with the increase of the JNR, so that the two types of signals can be accurately classified.
Fig. 6 shows the distribution of two-dimensional feature vectors T of six interference signals, i.e., 4dB JNR, where the method can accurately identify six interferences as shown in the figure.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (4)
1. A method for extracting interference signal classification and identification features for a satellite navigation system is characterized by comprising the following steps:
step 1: sampling a received signal y (t) to obtain a sampling sequence { y (N) }, N ═ 1., N of the received signal;
step 2: FFT conversion is carried out on the sampling sequence { y (n) } of the received signal, and the frequency spectrum { f (n) } of the sampling sequence of the received signal is obtained;
and step 3: normalizing the frequency spectrum (f (n)) of the received signal sampling sequence to obtain a normalized frequency spectrum (f) of the received signal sampling sequence*(n)}；
And 4, step 4: calculating a normalized spectrum f of a sample sequence of the received signal*(n) } frequency domain Sevcik fractal dimension D;
and 5: calculating a normalized spectrum f of a sample sequence of the received signal*Normalized spectral flatness F of (n) }se；
Wherein M is the width of a set sliding window;
step 6: outputting the characteristic vector T ═ D, F of the received signal y (T)se]And finishing the extraction of the classification identification characteristics of the received signals.
2. The utility model provides a satellite navigation system-oriented interference signal classification recognition feature extraction system which characterized in that: the device comprises a signal receiving module, a sampling module, an FFT (fast Fourier transform) module, a normalization module, a frequency domain Sevcik fractal dimension calculation module, a frequency spectrum flatness calculation module and a mapping module; the signal receiving module transmits the received signal y (t) to the sampling module; the sampling module samples the received signal y (t) to obtain a sampling sequence of the received signal (y (N)), (N is 1., and N is the sampling number), and the sampling module transmits the sampling sequence of the received signal (y (N)) } to the FFT conversion module; the FFT conversion module carries out FFT conversion on the sampling sequence { y (n) } of the received signal to obtain a frequency spectrum { f (n) } of the sampling sequence of the received signal, and the FFT conversion module transmits the frequency spectrum { f (n) } of the sampling sequence of the received signal to the normalization module; the normalization module normalizes the frequency spectrum { f (n) } of the received signal sampling sequence to obtain the normalized frequency spectrum { f of the received signal sampling sequence*(n), the normalization module normalizes the normalized spectrum of the received signal sample sequence { f*(n) transmitting the frequency domain Sevcik fractal dimension calculation module and the frequency spectrum flatness calculation module; the frequency domain Sevcik fractal dimension calculation module extracts the normalized frequency spectrum { f) of the received signal sampling sequence*(n) transmitting the frequency domain Sevcik fractal dimension D as a first characteristic parameter to a mapping module; the above-mentionedThe spectral flatness calculation module extracts a normalized spectrum { f) of the received signal sample sequence*Normalized spectral flatness F of (n) }seAs a second characteristic parameter to the mapping module; the mapping module maps a first characteristic parameter D and a second characteristic parameter FseComponent feature vector T ═ D, Fse]And outputting to finish the classification and identification feature extraction of the received signals.
3. The system according to claim 2, wherein the system comprises: the frequency domain Sevcik fractal dimension calculation module extracts the normalized frequency spectrum { f) of the received signal sampling sequence*(n) the specific process of the frequency domain Sevcik fractal dimension D is as follows:
step 3.1: calculating a normalized spectrum f of a sample sequence of the received signal*(n) distance L between every two sampling pointsi；
Step 3.2: calculating the distance L between all sampling pointsiSumming to obtain the length L of the waveform;
step 3.3: calculating a normalized spectrum f of a sample sequence of the received signal*(n) } frequency domain Sevcik fractal dimension D;
4. the system for classifying and identifying the interference signals of the satellite navigation system according to claim 2 or 3, wherein: the spectrum flatness calculating module is used for calculating the flatness of the spectrumTaking the normalized spectrum { f) of a sample sequence of a received signal*Normalized spectral flatness F of (n) }seThe specific process comprises the following steps:
step 4.1: normalized spectrum f of a sample sequence of a received signal*(n) is input to a sliding window function processing module which computes an average spectrum of the received signal sample sequence
Wherein M is the width of a set sliding window;
step 4.2: normalized spectrum f of a sample sequence of a received signal*(n) and average spectrum
Taking the difference to obtain the steep part in the normalized frequency spectrum of the received signal sample sequence
Step 4.3: calculating the standard deviation of the steep part in the normalized frequency spectrum of the received signal sample sequence to obtain the normalized frequency spectrum { f of the received signal sample sequence*Normalized spectral flatness F of (n) }se；
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