CN111814703B - HB-based signal joint feature extraction method under non-reconstruction condition - Google Patents
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Abstract
The invention belongs to the field of signal processing, provides an HB-based signal combined feature extraction method under a non-reconstruction condition aiming at the problem that a single feature has an identification result which is easy to drift, and is particularly used for processing the problem of radiation source signal classification and identification. The single feature is extracted as the identification feature, and sometimes the feature classification boundary is fuzzy, which may reduce the identification effect. The joint feature of high order cumulants combined with bispectrum is used to solve this problem. The high-order cumulant can well retain information such as amplitude, phase and the like, and the bispectrum is the lowest-order high-order spectrum and has the advantage of simple calculation. The radiation source signal is first generated, and the received signal is then compressively sampled by the MWC and its amplitude is normalized. And then calculating the three-six-order cumulant and the square eight-order cumulant, and extracting the characteristics of the high-order cumulant. And meanwhile, extracting the estimation characteristics of the bispectrum coefficients of the sampling matrix. And finally, transmitting the three different high-order cumulant features and the bispectrum coefficient estimation features into an SVM classifier for classification by using the three different high-order cumulant features and the bispectrum coefficient estimation features as joint features.
Description
Technical Field
The invention belongs to the field of signal processing, relates to a signal joint feature extraction method based on HB under a non-reconstruction condition, and is particularly used for processing the problem of classification and identification of radiation source signals.
Background
A particular radiation source identification distinguishes individual emitters from other emitters by their unique characteristics, thereby identifying different emitters. The radiation source identification technology is mainly used in military communication. It becomes increasingly important as new technologies emerge, such as cognitive radio and ad hoc networks.
Based on the operating mode of the emitter, emitter identification can be performed on transient or steady state signals by the emitter identification. Transient signals, also known as on/off signals, and the resulting emitter specificity can be used to extract features. To extract the feature of the transient signal, the main method is to extract the transient signal by detecting the start point and the end point of noise. However, transient signals are short in duration and difficult to capture. It is also susceptible to interference from complex channels and affects the recognition of the transmitter. The steady state signal is transmitted between the transient start and end of the overall signal. The detection and acquisition of steady state signals is simpler than transient state signals. However, since the steady-state feature is easily broken, extraction of the steady-state feature becomes difficult. For steady state signals, various feature extraction schemes were investigated. The most common methods are based on time-frequency analysis algorithms such as short-time fourier transforms, wavelets, etc. Besides these methods, like cumulant, bispectrum is also used in large quantities.
Although the traditional characteristic extraction method can effectively extract the characteristics of the radiation source, the single characteristic sometimes causes the drift of the identification result, and the correct identification rate is reduced. The invention provides a Joint Fingerprint feature (HB) extraction algorithm based on high-order cumulant and Bispectrum coefficient estimation. The method can solve the problem that the identification result is easy to drift in the single characteristic, and improves the identification stability of the radiation source.
Disclosure of Invention
The invention provides a multi-feature combined feature identification method aiming at the problem that the identification result is easy to drift in a single feature.
The technical scheme adopted by the invention is as follows:
the signal joint feature extraction based on HB under the non-reconstruction condition mainly comprises the following steps:
step 4, extracting estimation characteristics of the bispectrum coefficients;
and 5, classifying and identifying the radiation source signals by using a Support Vector Machine (SVM).
Has the advantages that: 1) compared with the VMD _ SF algorithm and the EMD _ EM algorithm, the method has better identification effect under the condition that three radiation sources exist; 2) the identification difficulty is increased along with the increase of the number of the radiation sources to be identified, but the identification rate can still reach 90% when the signal-to-noise ratio of 5 radiation sources reaches 10 dB.
Drawings
FIG. 1 is a block diagram of an MWC system;
FIG. 2 is a MWC spectrum shifting diagram;
FIG. 3 is a flow chart of a HB-based signal joint feature extraction algorithm under non-reconstruction conditions;
FIG. 4 is a TSOC characteristic profile of the present invention;
FIG. 5 is a SEOC characteristic profile of the present invention;
FIG. 6 is a bispectrum coefficient estimation feature profile of the present invention;
FIG. 7 is a HB joint signature distribution plot of the invention;
FIG. 8 is a graph comparing the recognition rate of the present invention in a Gaussian white noise channel with the VMD _ SF and EMD _ EM algorithms;
FIG. 9 is a graph comparing the recognition rate of the VMD _ SF and EMD _ EM algorithms in fading channels according to the present invention;
FIG. 10 is a graph comparing the recognition rate of the present invention with VMD _ SF and EMD _ EM algorithms for 4 radiation sources;
fig. 11 is a graph comparing the recognition rate of the present invention with VMD _ SF and EMD _ EM algorithms under 5 radiation sources.
Detailed Description
1. Generating a radiation source signal
A transmitter comprises a plurality of nonlinear devices, and the established system model mainly considers the mechanism that the nonlinearity of a power amplifier generates a radiation source fingerprint. Establishing Taylor series model, order
For the order of the Taylor polynomial, for the transmitter
Can be expressed as
Wherein
Wherein
Is from a radiation source
The channel fading coefficients to the receiver are,
is additive noise. Signal obtained by substituting formula (1) for formula (3)
Is composed of
Receiving signals at a receiving end
From
To identify different radiation sources.
2. Radiation source signal preprocessing
In the signal preprocessing section, we use a Modulation Wideband Converter (MWC), and the schematic block diagram of its sampling system is shown in fig. 1. Input signal
The system is divided into m paths of input MWC sampling systems, each undersampled channel consists of a pseudo-random frequency mixing channel, a low-pass filtering (LPF) channel and a low-speed ADC channel, and the output result is a compressed sampling sequence of an original signal. Fig. 2 is a spectrum moving diagram of each channel of the MWC, after spectrum cutting, the whole frequency band is divided into L spectrums, and after moving and mixing of each sub-band, the global information of the signal in the channel is included.
Receiving a signal
After MWC compression sampling, one is obtained
Dimension matrix, is recorded as
Wherein
Is that
And (5) maintaining column vectors. Avoiding the influence caused by intensity sensitivity and amplitude sensitivity when extracting the characteristics, realizing the translation compensation of the sample data by adopting an envelope alignment method, and carrying out amplitude normalization processing according to the following formula
Bispectrum is a feature widely used in higher order statistical analysis, the two-dimensional discrete Fourier transform of the third-order cumulant of a signal: (DFT, Discrete Fourier transform) is bispectrum. For a determined discrete-time signal
Its third order cumulant is
Wherein
Is that
Conjugation of (1).
3. Extracting high-order cumulant features
Extracting different high-Order Cumulants (HOC) characteristics, including three-six Order Cumulant (TSOC, Tri-six-Order Cumulant) and Square Eight Order Cumulant (SEOC, Square elevation-Order Cumulant). The CSD of the received signal is obtained via MWC compressive sampling. For the
The time-varying moment of the signal is defined as
Wherein (8) therein
Referred to as signals
The product of the hysteresis of (a) is,
is that
The conjugate of (a) to (b),
is the total number of conjugates.
Index set
Is named as
,
Is the number of elements in a partition, and is used by the index set belonging to the partition
And (4) showing. According to a Moment-cumulant (MC) conversion formula,
is/are as follows
The cumulative amount of order is expressed as
Therefore, we can obtain the following relationship between the moment and the accumulated quantity
TSOC, SEOC and TSOC characteristics can be respectively extracted from the parameters
Can be expressed as
SEOC characteristics
Can be expressed as
The TSOC and SEOC are extracted as two high-order cumulant features as in the above formula.
FIGS. 4 and 5 show a comparison of the TSOC characteristic and the SEOC characteristic of three radiation source units with different Taylor coefficients, wherein the Taylor coefficient of the radiation source 1 is
The Taylor coefficient of the radiation source 2 is
The Taylor coefficient of the radiation source 3 is
The baseband modulation modes are all 8 QAM. Fig. 4 is a TSOC characteristic distribution diagram in which the horizontal axis represents the number of signal points of the radiation source and the vertical axis represents the TSOC characteristic value. It can be seen that the three radiation source signals can be roughly classified and identified, but the boundary is confused, and if the boundary is interfered by the outside, the identification rate is reduced. The TSOC single characteristic is used as a classification judgment basis to be unfavorable for the classification of the radiation source signal. Fig. 5 is an SEOC characteristic distribution diagram in which the horizontal axis represents the number of signal points of the radiation source and the vertical axis represents the SEOC characteristic value. In the figure, the mean value of the SEOC characteristic values of the radiation source signal 1 is 11.4, the mean value of the SEOC characteristic values of the radiation source signal 2 is 10.9, and the mean value of the SEOC characteristic values of the radiation source signal 3 is 12.0. The SEOC characteristic values of the three signals are more confusing, but still feasible as one of the joint characteristics,if the feature classification identification is used alone, the result is seriously distorted.
4. Extracting bispectrum coefficient estimation features
Equation (7) has given the discrete-time signal
Third order cumulant of
Calculation formula, therefore
Double spectrum of
Is composed of
The bispectrum is to represent one frequency by two other frequencies, and the invention adopts a direct estimation method to carry out
And performing double-spectrum estimation. Sampling the data
Is divided into
Segments, each segment comprising
The data between two adjacent ends can have overlap, and are recorded as convenient description
. DFT coefficient calculation for each segment of data
Wherein
，
. Is provided with
Calculating triple correlation of each DFT coefficient for sampling rate to obtain correlation sequence
Wherein
Is shown as
Triple correlation of segment data
，
. The bispectrum coefficient of the sample data is estimated as
Wherein
。
Fig. 6 shows the distribution of the estimated characteristic of the bispectrum coefficient, in which the horizontal axis represents the number of signal points of the radiation source and the vertical axis represents the estimated characteristic value of the bispectrum coefficient. We can see that the radiation source signal 1 can be effectively distinguished from the other two signals, but still avoid the boundary confusion of the radiation source signal 2 and the radiation source signal 3. Fig. 7 is a HB joint feature distribution diagram, which shows three-dimensional features, and three coordinate axes respectively show the TSOC feature, the SEOC feature, and the bispectrum coefficient estimation feature. It can be clearly seen that when three features are combined into a combined three-dimensional feature, the different radiation source individuals can be completely distinguished.
5. Classification and identification of radiation source signals by using Support Vector Machine (SVM)
The support vector machine is a supervised learning classifier for the two-classification problem. Training set
，
Is a training vector that is a function of,
is a class of tags. We consider the simplest case where positive and negative label data can be represented by a simple hyperplane
So as to separate the components of the mixture,
wherein,
is the normal vector of the hyperplane;
is the perpendicular distance from the origin to the hyperplane. Of support vector machinesThe purpose is to optimize the hyperplane to its edges
To a maximum of
Representing the closest distance of the positive point to the hyperplane,
representing the closest distance of the negative point to the hyperplane). The hyperplane thus found serves as a separation boundary for different classes of data. For the case where the number of classes is greater than 2, in general, the multi-classification problem can be handled by reducing it to a few bi-classification problems.
And (3) identifying the joint features given by the figure 7 by using the SVM to obtain an identification result. Fig. 8 is a graph comparing the recognition rate of the present invention and VMD _ SF and EMD _ EM algorithms through an additive white gaussian noise channel in the case of class 3 radiation source signals. It can be seen from the figure that the recognition accuracy of the invention is higher at each SNR than the other two algorithms, and at SNR = -5db, the correct recognition rate of the invention is 90%, the recognition rate is 20% higher than VMD _ SF, and 21% higher than EMD _ EM. Under low SNR, the recognition effect of the recognition algorithm based on HB in the radiation source recognition field is better than that of the other two algorithms.
Fig. 9 is a comparison graph of the recognition rates of the fading channel of the present invention and VMD _ SF and EMD _ EM algorithms under the condition of 3 types of radiation source signals, and compared with fig. 8, the recognition rates of the three algorithms in the fading channel are all decreased, the recognition rate of the present invention can reach more than 90% after SNR >8db, and the recognition rates of the VMD _ SF and EMD _ EM algorithms are 81% and 73% respectively when SNR =20db, so that it can be seen that the present invention can still successfully recognize radiation source individuals under the fading channel.
Fig. 10 is a graph of the identification performance of the present invention and VMD _ SF and EMD _ EM algorithms through an additive white gaussian noise channel in the case of class 4 radiation source signals. Fig. 11 shows a graph of the recognition performance through an additive white gaussian noise channel in the case of a class 5 radiation source signal. It can be seen from the figure that the recognition rates of all three algorithms are reduced as the number of radiation sources increases, but the recognition rate of the present invention is always the highest compared to the other two algorithms.
Claims (3)
1. A signal joint feature extraction method based on HB under the non-reconstruction condition is characterized by comprising the following steps:
step 1, generating a radiation source signal;
step 2, preprocessing the radiation source signal;
step 3, extracting high-order cumulant features, wherein the formula for extracting the high-order cumulant is
And obtaining the relation between the moment and the cumulant through the formula as
Next, the three-Sixth Order Cumulant (TSOC, Tri-six-Order Cumulant) and the Square Eight Order Cumulant (SEOC, Square elevation-Order Cumulant) are extracted as characteristics, wherein
Step 4, extracting estimation characteristics of bispectrum coefficients, firstly sampling data
Segmentation, as
The DFT coefficients for each piece of data are then found,
then, according to the triple correlation of each DFT coefficient, a correlation sequence is obtained,
finally, the estimation characteristics of the bispectrum coefficient are obtained,
and 5, using a Support Vector Machine (SVM) and adopting a combined characteristic consisting of three-six-order cumulant, square eight-order cumulant and double-spectrum coefficient as input to classify and identify the radiation source signals.
2. The method according to claim 1, wherein the HB-based signal joint feature extraction method under the non-reconstruction condition is characterized in that: step 1, modulating QAM signals in the process of generating signals
Performing Taylor nonlinear processing, and adding fading coefficient
And noise
Generating a source signal, is
And is and
。
3. the method according to claim 1, wherein the HB-based signal joint feature extraction method under the non-reconstruction condition is characterized in that: receiving signals in step 2
After MWC compression sampling, one is obtained 
Dimension matrix, denoted as
And is made of
Will be
The amplitude is obtained after the normalization processing of the amplitude is carried out,
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