RU2430416C1 - Radio signal recognition method - Google Patents

Abstract

FIELD: information technology.

SUBSTANCE: standard radio signals are first given, discretised, quantised and the sequence of quantised readings of standard radio signals undergoes frame wavelet transformation. A radio signal attribute vector is then generated from the wavelet coefficients of the standard radio signals. The recognised radio signal is then received and its attribute vector is then generated similarly to that of standard radio signals. The received radio signal is then identified by comparing its attributes with attributes of standard radio signals. Before generating the attribute vector from wavelet coefficients, maximum coefficients are separately picked up at the outputs of the second and third filters, onto which the remaining wavelet coefficients are respectively standardised separately for the second and third filters. The average power values of the wavelet coefficients are then calculated, which are selected as radio signal attribute vector elements. The received radio signal is identified through successive modulo subtraction of elements of its attribute vector from attribute vectors of each of the standard radio signals, and the recognised radio signal is the incident standard radio signal, the difference between attribute vectors of which is minimal.

EFFECT: high efficiency while maintaining the required probability of correct recognition.

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Description

The invention relates to pattern recognition, and in particular to methods for recognizing radio signals, in particular to methods for recognizing the form and modulation parameters of radio signals. The method can be used in the technical means of recognition of radio signals of phase manipulation.

A known method for the recognition of radio signals based on the singular decomposition of the pseudo-frequency-time distribution (pseudo-CWR) Wigner-Ville [N.M. Marinovic, G. Eichmann. An expansion of Wigner distribution and its applications. - Proc. IEEE ICASSP-85, 1985, pp. 1021-1024,], in which the energy distribution matrices (REs) of the reference radio signals are formed on the basis of the Wigner-Ville pseudo-FEC, spectral decomposition is performed, the parameters of the reference radio signals are formed, then the recognized radio signal is received, sampled and quantized, the distribution matrix is formed energy of the received radio signal, distinguish the signs of the received radio signal, compare them with the parameters of the reference radio signals and, based on the results of the comparison, identify the received radio signal.

The disadvantage of this method is the relatively low probability of correct recognition ¹ ( ^{1 The} probability of correct recognition is the relative frequency of making the right decision when assigning the received radio signal to one of the reference classes. The correct recognition event is the opposite (additional) to the error recognition event (P _dec = 1-P _osh ) - see - J.Tu, R. Gonzalez, Principles of Pattern Recognition, Translated from English - M .: Mir, 1978. - pp. 142-152) of radio signals of complex frequency-time structure, as well as p radio signals when exposed to noise and interference, which is due to the peculiarities of the Wigner-Ville pseudo-CVM used for the formation of RE matrices [Cohen L. Time-frequency distributions. Overview. // TIIER, 1989, v.77, No. 10. S.72-121]. The probability of correct recognition is reduced due to the appearance of interference background and false power peaks in the Wigner-Ville pseudo-FWM, which distort the real picture of the RE of the radio signal in frequency-time coordinates.

A known method of recognizing radio signals according to the patent of the Russian Federation No. 2261476, IPC ⁷ G06K 9/00 from 09/27/2005, in this method, the reference radio signals are pre-set. Then, the reference radio signals are discretized, quantized, and a continuous wavelet transform (VP) operation is performed on them in order to obtain an RE matrix. After that, for each matrix RE form the vector RE. Then, for all obtained RE vectors, the total covariance matrix is calculated. After that, spectral decomposition of the RE matrices of the reference radio signals is performed by calculating the eigenvectors and eigenvalues of the total covariance matrix. Then a truncated matrix of eigenvectors is formed by choosing the eigenvectors of the general covariance matrix corresponding to its maximum eigenvalues. When forming the parameters of the reference radio signals, a truncated matrix of eigenvectors is multiplied by the RE vectors of the reference radio signals, and the average values of the obtained products are used as the parameters of the standards. After that, a recognizable radio signal is received, it is sampled, quantized, and then the continuous VP operation of its quantized samples is performed. Then, an RE vector is formed from the RE matrix, and a truncated matrix of eigenvectors is multiplied by its RE vector to extract features of the received radio signal. The calculation results are taken as signs of recognition of the received radio signal, which are sequentially compared with the parameters of the previously obtained standards. The comparison results serve as the basis for deciding on the correlation of the recognized radio signal to one or another class.

The disadvantage of the considered method is the relatively low efficiency (speed ² (Speed ² is the time it takes for the system to transition from a certain initial state to the desired final state; one of the system quality estimates is see the Dictionary on Cybernetics. Kiev: Ukr. Sov. Encyclopedia, 1979 , 623 pp. // P.89.)) Of the recognition process itself, due to the need to perform continuous VP operations, perform spectral decomposition of RE matrices and form a truncated matrix of eigenvectors that are associated with a significant IOM computing operations.

The closest analogue in technical essence to the claimed one is the method of recognition of radio signals according to RF patent No. 2356064, IPC ⁷ G06K 9/00 of 05/20/2009. In the closest analogue, reference radio signals are pre-set. Then, for each reference radio signal, its RE matrix is formed. For this purpose, reference radio signals are sampled, quantized, and then perform frame ³ operation ( ³ Wavelet frames are a wavelet transform that uses a multiple of two scaling (in frequency) and continuous shifts (in time). - see V. Dyakonov Wavelets. From theory to practice. - M .: SOLON-R, - 2002. 448 p. P.106.) VP by filtering their quantized readings by means of filters, the bandwidth of which each time doubles with increasing filter serial number. After that, the wavelet coefficients (VK) obtained from the output of each filter are normalized, ranked and excluded insignificant VK. The VC set is selected as insignificant, starting from the lowest, the total energy of which is 10-30% of the total energy of the entire VC set at the output of each filter, respectively. Then, from the remaining VCs, an RE matrix is formed, with the rows of the RE matrix of each reference radio signal being the VC obtained at the output of the filters. And from the RE matrices of the reference radio signals, their feature vectors are formed by line-wise concatenation of all the VC generated RE matrices. After that, a recognizable radio signal is received, from the quantized samples of which an RE matrix and a feature vector are formed in the same way as for reference radio signals. The received radio signal is identified by subtracting modulo its feature vector from the feature vectors of each of the reference radio signals. The recognized radio signal is considered incident with the reference radio signal, the difference of feature vectors with which is minimal.

The disadvantage of the prototype method is the relatively low efficiency (speed), due to the need to select insignificant VCs when forming a feature vector and the relatively low probability of correct recognition when exposed to noise and interference, due to the uneven distribution of their power across the VCs of the generated RE matrix [V. Dyakonov Wavelets. From theory to practice. - M .: SOLON-R, - 2002.448 s.].

The purpose of the claimed technical solution is to develop a method for recognizing radio signals, providing increased recognition efficiency while maintaining the required probability of correct recognition, by using average power of normalized VC obtained at the output of the second and third filters as element vectors of signs.

This goal is achieved by the fact that in the method of recognition of radio signals pre-set L≥2 reference radio signals. For each l-th reference radio signal, where l = l, ..., L, an RE M _l matrix is formed, for which the radio signal is sampled, quantized, and then the frame IP operation of the sequence of its quantized reports is performed using K≥2 filters. Moreover, the passband ΔF _{k of the} k-th filter, where k = 1, ..., K, is selected from the condition ΔF _k = 2 ^(k-1) ΔF, where ΔF is the bandwidth of the first filter. Then, from the VC of the l-th reference radio signal received in each k-th frequency band Δ Ф _k , a feature vector of the l-th reference radio signal is formed. After that, a recognizable radio signal is received and its feature vector is generated in the same way as for the l-th reference radio signal, after which the received radio signal is identified by comparing its features with the features of the reference radio signals. Moreover, before the formation of the feature vector from the number of VK separately at the outputs of the second and third filters, the maximum ones are allocated, which normalize the remaining VK separately for the second and third filters. Then calculate the average power of the wavelet coefficients obtained at the outputs of the second and third filters, which are selected as elements of the vector of signs of the radio signal. The received radio signal is identified by sequentially subtracting modulo elements of its feature vector from the feature vectors of each of the L reference radio signals. The recognized radio signal is considered incident with the reference radio signal, the difference of feature vectors with which is minimal.

Thanks to the new set of essential features in the claimed method, the number of computational operations is reduced during the formation of feature vectors by eliminating the selection of non-significant VK, as well as the choice of average VK power values at the outputs of only the second and third filters as features of the vector of signs. This reduces the number of procedures performed during the recognition of radio signals and thereby provides higher efficiency.

In addition, the normalization of the VC at the outputs of the second and third filters allows to reduce the negative impact of additive noise, which ensures the stability of the generated feature vectors and, therefore, increases the likelihood of correct recognition when exposed to additive noise.

The claimed method is illustrated by drawings, which show:

figure 1. The principle of calculating the RE matrix of a radio signal based on a frame VP;

figure 2. Reference eight-position phase-shifted radio signal S ₁ (t);

figure 3. The matrix RE M ₁ reference radio signal S ₁ (t);

figure 4. The feature vector m ₁ (k) of the reference radio signal S ₁ (t), consisting of 8 elements, with a signal-to-noise ratio (SNR is the ratio of the signal power P _s to the noise power R _w ) 30 dB;

figure 5. The feature vector m ₁ (k) of the reference radio signal S ₁ (t), consisting of 8 elements, with an SNR of 20 dB;

Fig.6. The feature vector m ₁ (k) of the reference radio signal S ₁ (t), consisting of 8 elements, with an SNR of 10 dB;

Fig.7. A generalized graph of the dependence on the SNR of the probability of correct recognition when normalizing elements of the feature vector and without standardization.

The implementation of the claimed method is explained as follows.

Pre-set L reference radio signals, the number and types of which cover the possible number and types of real radio signals to be recognized.

Then, a set of procedures is performed to form a feature vector of each l-th reference radio signal, where l = 1, ..., L. For this, each reference radio signal is sampled and quantized. The sampling and quantization procedures for analog signals are known and described, for example [V.Grigoriev. Signal transmission in foreign information technology systems. - SPb .: YOU. 1998, p. 83-85], and the quantized samples of the reference sequences of radio signals are formed in accordance with the requirements of the calculation of statistical estimates [Mathematical Encyclopedic Dictionary. M .: Sov. Encyclopedia, 1988. 847 p .; G.Corn, T.Corn. Math reference. Per. from English - M .: Nauka, 1977, p. 638-643]. The sample length N (the value of discrete reports of radio signals n = 1, ..., N) is selected within 128 ... 16384, depending on the requirements for the probability of correct recognition and processing time (the sample length must be a multiple of 2 ⁱ , where i is an integer) . The larger N, the higher the probability of correct recognition, but the processing time increases.

Then a set of matrices RE {M ₁ ... M _L } is formed, for which a frame IP operation is performed on the quantized samples of reference radio signals. The operation of the frame VP consists in filtering the samples of the quantized radio signal using a combination of K≥2 filters (figure 1). The total number K of filters in this case is determined taking into account the conditions:

where ΔF is the width of the spectrum of the radio signal; ΔF ₁ - bandwidth of the first filter [V. Dyakonov Wavelets. From theory to practice. - M .: SOLON-R, - 2002. 448 p., S.117-121]. In turn, the bandwidth ΔF _{k of the} k-th filter, where k = 1, ..., K, is selected from the condition

.

.

Such a choice of filter bandwidths provides a complete overlap in the frequency spectrum of the radio signal with a wavelet filter system and at the same time avoids the description redundancy inherent in a continuous VP [V. Dyakonov Wavelets. From theory to practice. - M .: SOLON-R, - 2002. 448 p., S.104-107].

The amplitude-frequency characteristics of the filters (Fig. 1) used to perform the frame IP operation correspond to the passband of the basic wavelet functions. The dimension of the generated matrix RE M is (K × N), where K is the number of filters, N is the number of temporary discrete reports of the radio signal.

Then, from the formed RE matrices, VCs obtained only at the outputs of the second and third filters are selected. After that, the VC modules of each l-th reference radio signal from the outputs of the second and third filters, presented in the form of vectors, are separately normalized with respect to their maximum values. The operation of normalizing vectors is known and is realized by multiplying each of its elements by a number equal to the reciprocal of the maximum value of this vector [G. Korn, T. Korn. Math reference. - M.: Science, - 1977. 831 S.].

Then, for each l-th reference radio signal, average power values of the normalized VC obtained at the outputs of the second and third filters are calculated. The operation of finding average values is known and described in [G. Korn, T. Korn. Math reference. - M.: Science, - 1977. 831 S.].

From the average power values of the normalized VCs, at the outputs of the second and third filters, feature vectors are formed for each l-th reference radio signal.

, выполняют над ним все описанные действия, которые выполнялись над эталонными радиосигналами и формируют вектор признаков принятого радиосигнала

аналогично, как и для эталонных радиосигналов.After performing operations on the formation of vectors of features of the standards receive a recognizable radio signal

, perform on it all the described actions that were performed on the reference radio signals and form a vector of signs of the received radio signal

similarly as for reference radio signals.

с вектором признаков каждого из эталонных радиосигналов {m₁(k)…m_L(k)}. Идентификация может быть реализована с использованием различных приемов. Например, путем вычитания по модулю из вектора признаков принятого радиосигнала векторов признаков каждого из L эталонных радиосигналов

. Процедуры принятия решения являются известными и описаны, например, в [Я.Фомин, Г.Тарловский. Статистическая теория распознавания образов. - М.: Радио и связь, 1986, стр.30-46; Ю.Сато Обработка сигналов. Первое знакомство. / пер. с яп. / Под ред Ёсифуми Амэмия. - М.: Издательский дом «Додека-XXI», 2002. - 176 с. С. 41-54]. Распознаваемый радиосигнал считают инцидентным одному их L эталонных радиосигналов, с использованием одного из решающих правил, например, когда разница между векторами признаков минимальна.Recognized radio signal is identified by comparing its feature vector.

with the feature vector of each of the reference radio signals {m ₁ (k) ... m _L (k)}. Identification can be implemented using various techniques. For example, by subtracting modulo from the feature vector of the received radio signal the feature vectors of each of the L reference radio signals

. Decision-making procedures are well known and are described, for example, in [Y. Fomin, G. Tarlovsky. Statistical theory of pattern recognition. - M .: Radio and communications, 1986, pp. 30-46; Yu.Sato Signal processing. First meeting. / per. with yap. Edited by Yoshifumi Amemiya. - M.: Dodeka-XXI Publishing House, 2002. - 176 p. S. 41-54]. A recognized radio signal is considered to be incident to one of their L reference radio signals, using one of the decisive rules, for example, when the difference between the feature vectors is minimal.

The choice of the characteristic vector elements for the phase-shift radio signals of the average VK values from the output of the second and third filters is due to the fact that the main energy of this class of radio signals in the absence of noise is concentrated mainly on the output of the first, second and third filters. So figure 2 presents a fragment of an eight-position phase-manipulated radio signal FM8 S ₁ (t), and figure 3 presents its matrix RE M ₁ . Figure 4 shows the feature vector m ₁ (k), the elements of which are averaged values from the output of eight filters.

Studies have shown that the additive effect of noise leads to a significant change in the value of the first element of the feature vector and all subsequent elements, starting from the fourth (Figure 4-6). The most resistant to noise are the elements of the feature vector representing the average values of the VC from the outputs of the second and third filters.

The normalization of the VC from the outputs of the second and third filters provided additional resistance to the elements of the feature vectors to noise, which increased the likelihood of correct recognition of phase-shift radio signals. The experimental results confirming the effectiveness of the normalization procedure are presented in Fig.7.

Claims (1)

A method for recognizing radio signals, namely, that L≥2 reference radio signals are predefined, is formed for each l-th reference radio signal, where l = 1, ..., L, the energy distribution matrix M ₁ , for which it is sampled, quantized, and then performed the operation of the frame wavelet transform of the sequence of its quantized reports using K≥2 filters, for which the passband ΔФ _{k of the} k-th filter, where k = 1, ..., K, is selected from the condition ΔФ _k = 2 ^(k-1) ΔФ, where ΔФ is the bandwidth of the first filter, then from the wavelet coefficients ENTOV l-th reference radio signal received in each k-th band of frequencies ΔF _k are formed feature vector l-th reference radio signal, and then take a recognizable radio signal and generating its feature vector is similar as for the l-th reference radio, and identifies the received radio signal by comparing its features with the features of the reference radio signals, characterized in that before the formation of the feature vector from the number of wavelet coefficients separately at the outputs of the second and third filters, the maximum which, respectively, normalize the remaining wavelet coefficients separately for the second and third filters, then calculate the average values of the power of the wavelet coefficients obtained at the outputs of the second and third filters, which are selected as elements of the radio signal attribute vector, and the received radio signal is identified by sequentially subtracting the modulus of the elements its feature vector from the feature vectors of each of the L reference radio signals, and the recognized radio signal is considered incident to the reference iosignalu difference of feature vectors which is minimal.

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