RU2593276C1  Method of selecting moving targets  Google Patents
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 RU2593276C1 RU2593276C1 RU2015132506/07A RU2015132506A RU2593276C1 RU 2593276 C1 RU2593276 C1 RU 2593276C1 RU 2015132506/07 A RU2015132506/07 A RU 2015132506/07A RU 2015132506 A RU2015132506 A RU 2015132506A RU 2593276 C1 RU2593276 C1 RU 2593276C1
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 230000015572 biosynthetic process Effects 0.000 description 7
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 230000002123 temporal effects Effects 0.000 description 3
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Abstract
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The invention relates to radar, can be used in radars (radars) of shortrange decimeter range and is intended to highlight moving against a background of passive interference targets.
The primary temporary signal processing in pulsed radars of a survey type consists, as is known, of intra and interperiod filtering of the received mixture [1]. Intraperiodic processing usually consists in consistent (or close to consistent) filtering of a single burst pulse. In this case, a certain deviation from the matched filtering is aimed at reducing the level of the side lobes of the compressed pulse, as well as [2]  minimizing the average timetoarrival of the useful signal loss in terms of signaltonoise caused by time discretization of the process.
A much greater variety of approaches is found in the implementation of interperiod processing. It is due both to the nature of the tasks being solved and the available computing (hardware) resources, and is associated with the need to select useful and interfering signals by the Doppler frequency. Such selection involves both suppression (rejection) of passive interference and the accumulation of a useful signal with a possible measurement of the radial velocity of detected objects.
One of the approaches [3] to the synthesis of such interperiod processing is to divide it into two stages: preliminary coherent rejection of passive interference, followed by incoherent (or partially coherent) accumulation of useful signals. The structural diagram of a device that implements signal processing in this case has the form shown in figure 1, where RF is a notch filter of passive interference; HELL  amplitude detector; NN is an incoherent drive of a burst of pulses of a useful signal.
The notch filter in this scheme can be implemented, for example, on the basis of the principle of linear prediction of one of the pulses of a packet of passive noise from neighboring pulses, when the vector of filter coefficients is determined from the YuleWalker equation [1, p. 384], [4, p. 226]
where W is the weight vector of the Russian Federation; R is the correlation matrix of passive interference of size (L + 1) × (L + 1); L is the filter order selected from the condition of the required suppression; Q _{n} is a column vector whose nth component (corresponding to the predicted passive interference sample) is equal to one, and the rest to zero.
The use of an incoherent storage device in this case is caused, as a rule, by the fact that multichannel coherent storage is unacceptable due to high hardware costs.
The described method is, apparently, the most economical in terms of computation, since it contains only one processing channel. However, the disadvantages of this method are also obvious, associated with losses in the signal / intrinsic noise ratio compared to the optimal signal processing only against the background of intrinsic noise. (We emphasize that both here and in the future, the remainder of the passive interference after suppression is assumed to be negligibly small in comparison with the intrinsic noise, as a result of which the signal / intrinsic noise losses occur as a charge for suppressing interference.
In the case under consideration, these losses occur due to the interperiod correlation of the originally uncorrelated intrinsic noise introduced by the notch filter of passive interference, which, with subsequent incoherent accumulation, leads to losses of the order of (2–3) dB [5]. If we also take into account losses on incoherent accumulation (compared to coherent) that occur even with uncorrelated intrinsic noise (see, for example, [1]), then this interperiod processing algorithm may be unacceptable in many practical applications.
Often, in practice, the method of interperiodic time processing shown in FIG. 1 is also applied when the coherence interval of the received pulse train exceeds its duration, that is, the waveguide is completely coherent. The use of an incoherent drive in this case is due only to the fact that coherent interperiod filtering, being necessary for multichannel, is unacceptable due to high hardware (computational) costs. In this situation, of course, it is necessary to reckon with the losses in the detection of useful signals arising from the replacement of completely coherent processing with partially coherent [5].
The closest approach to the synthesis of such interperiod processing, i.e. The prototype consists in the fact that under the condition of a sufficiently high degree of coherence of the entire received burst of pulses, twostage interperiod temporary signal processing is provided with its splitting into coherent rejection of passive interference and accumulation of the useful signal, implemented in a completely coherent manner using a parallel set of coherent drives, each of which corresponds to a useful signal with a certain Doppler frequency. As a result, the processing flowchart has the form shown in FIG. 2, where RF is a notch filter of interference, KN is a coherent drive, K is the number of coherent drives. The notch filter implementation approach is based on a linear prediction vector (1).
Another approach to the implementation of interperiod processing is the coherent accumulation of a useful signal using the discrete Fourier transform of the entire packet of received pulses after preliminary applying some weight function to it (DolphChebyshev, KaiserBessel, etc. [4]). This makes it possible to obtain a low level of side lobes of the frequency response of the interperiod filter everywhere outside its main peak and, therefore, it is also effective to suppress passive interference.
However, the use of a weight window leads to a mismatch between the Doppler interperiod filter and the useful signal and, therefore, generates signaltonoise noise losses, which, for example, when the side lobe level is minus 60 dB, are about 2 dB [6]. In addition, the use of the weight window leads to the expansion of the main peak of the frequency response compared to the matched filter, which, taking into account losses in the signaltonoise ratio, reduces both the accuracy of measuring the radial velocity of detected objects and the resolution along this coordinate. These shortcomings are, obviously, a fee for maintaining a low level of the side lobes of the transfer characteristic of the interperiod filter in the entire specified range of variation of the Doppler frequency of radar objects.
As you know, a low level of the side lobes of the frequency response is usually needed only in the frequency band occupied by strong passive noise, while outside this interval their level, sufficient to solve many practical problems, can be much higher, for example, minus (10 ÷ 20) dB . Such a frequency response in the claimed method is obtained using linear filtering in accordance with the weight vector [1]
where R is the correlation matrix of the sum of the vectors of passive interference and intrinsic noise,
is the vector of the useful signal,  Doppler frequency of the useful signal.Filtering (2) is optimal by the criterion of the maximum signal / (passive interference + intrinsic noise) ratio for any statistical distribution of interfering signals, and optimal in the Bayesian sense when their distribution is Gaussian [1].
The use of vector (2) in the synthesis of interperiod time processing in practice involves knowledge of a specific type of matrix R, which is far from always known due to a priori uncertainty regarding the shape of the power spectrum of passive interference (or, which is the same thing, regarding the shape of its correlation function) . On the other hand, in the implementation of the radar, a requirement is usually set for the deep rejection of passive interference in a certain range of changes in their Doppler frequencies, regardless of the shape of the power spectrum of the suppressed echo signals.
In this regard, a quasioptimal approach to the synthesis of interperiod temporal processing, based on the approximation of a passive interference signal by its projection onto a finitedimensional subspace, is noteworthy. The basis of this subspace is a set of discrete (in time) complex sinusoids, the frequencies of which overlap with a certain step the specified range of the Doppler frequency of passive interference.
Achievable technical result is to increase the efficiency of radar noise immunity in the presence of passive interference by minimizing losses when a useful signal is detected.
The specified technical result is achieved by the fact that in the known method of interperiod processing, based on preliminary coherent rejection of the passive interference and the subsequent accumulation of the useful signal using a multichannel system of coherent interperiod filters, each of which is matched to a certain Doppler frequency of the signal, the signal of the passive interference is approximated by its projection to the finitedimensional subspace and the derivation of the weight vector by the following formula:
where E is the identity matrix;
P = S (S ^{H} S) ^{1} S ^{H}  matrix projector on the subspace of interference;
S  matrix composed of column vectors
whose Doppler frequencies with a certain step they cover the frequency range of passive interference in order to provide a given suppression coefficient;is the vector of the useful signal,  Doppler frequency of the useful signal,
and also produce subsequent coherent accumulation of the useful signal, after which the useful signal is extracted from the received signal using a predetermined threshold on the threshold device.
The RF can be implemented in the structural form shown in figure 3. The RF contains a block 1 for generating input data, which is a vector corresponding to the received azimuthal packet, block 2 for generating a vector of the useful signal, which has an interperiod temporal structure characterized by a vector of a discrete sinusoid, block 3 control the width of the notch zone, which consists in the formation of a matrix composed of column vectors of signals whose Doppler frequencies overlap the frequency range with a certain step t of passive interference, block 4 of the formation of the matrix projector on the subspace of interference, the action of which on any linear combination of vectors of the useful signal leads to its zero, block 5 of the formation of the weight vector of optimal processing, which is formed by multiplying the inverse correlation matrix and the vector of the useful signal, block 6 calculating the Doppler frequency of the target F _{d} calculated by multiplying the Hermitianconjugate vector corresponding to the received azimuthal packet and the weight vector of optimal processing.
The RF works as follows: the signal from the analogtodigital converter (ADC) is fed to input data generating unit 1, which is a vector corresponding to the received azimuthal packet. In block 2, a useful signal vector is formed
.Depending on the intended purpose of detecting certain airborne objects having different speeds and the signaljamming situation, the radar operator assigns a “notch mode” that corresponds to a certain width of the passive jamming notch zone. The code of the “notch mode” is fed to the input of block 3. Depending on the selected “notch mode” in block 3, a matrix S is formed, composed of the minimum required number of column vectors
whose Doppler frequencies cover the passive interference frequency range to provide a given suppression ratio. In block 4, a projector matrix is formed on the interference subspace P = S (S ^{H} S) ^{1} S ^{H} , the action of which on any linear combination of sinusoids leads to its vanishing (suppression of passive interference). Then, data from blocks 4 and 2, respectively, arrives at the first and second inputs of block 5, where, by replacing a priori unknown inverse correlation matrix R ^{1 with} its realistic approximation and multiplying by the vector of the useful signal, the weight vector of optimal processing is formed . Then, using the data from block 1 and block 5 in block 6, the Doppler frequency F _{d is} calculated by multiplying the Hermitianconjugate vector corresponding to the received azimuthal packet and the weight vector of optimal processing.The essence of the proposed method is as follows. It is known [9] that the complex amplitude of a radar signal reflected from a point object (assuming its complete coherence) has an interperiod temporal structure characterized by a discrete sinusoid vector
Where
 Doppler frequency, T  period of sounding of radar pulses, N  number of pulses in the received packet.In this case, the passive interference signal is a combination of many sinusoids of the form (3), forming a continuum in Doppler frequency. In fact, for example, the foliage of trees in the presence of wind or meteorological conditions arising in a turbulent air environment contains point reflective objects that have an almost continuous distribution over the radial velocity [8]. Nevertheless, it is clear that this continuum can be approximated by a finite set of sinusoids with a sufficiently dense arrangement of them in Doppler frequency.
In mathematical terms, such an approximation is equivalent to projecting a passive interference signal onto a finitedimensional subspace, after which it can be represented as a linear combination of a finite number of complex sinusoids (3) with Doppler frequencies
. In this case, the sinusoid vectors have random and statistically independent amplitude factors a _{1} , a _{2} , ..., a _{M} , where M is the number of sinusoids used to approximate passive interference. Here, of course, the condition M <N must be satisfied, otherwise the introduced subspace will coincide with the entire Ndimensional space, and selection of useful signals and interference will become impossible.Then the correlation matrix of the vector of interfering signals (i.e., the sum of passive interference and intrinsic noise) can be represented as
where E is the unit matrix of dimension N × N characterizing the correlation properties of the intrinsic noise (without loss of generality, we assume that the power of an individual component of the noise vector is equal to unity); ν _{m} = < a _{m}  ^{2} > is the power of the mth sine wave of passive interference. Moreover, we assume that ν _{m} >> 1, that is, the power of external interference at the Doppler frequency
significantly exceeds the power of the intrinsic noise, which is usually the case in practice.Since matrix (4) is Hermitian and positive definite, the matrix inverse to it can be represented by spectral decomposition
where λ _{m} are nonzero eigenvalues of the second term in the righthand side of (4), the sum of which determines the power of passive interference; U _{1} , ..., U _{M}  orthonormal eigenvectors corresponding to these eigenvalues and which are the basis in the subspace of interference; U _{M + 1} , ..., U _{N} are the orthonormal eigenvectors of the matrix R, which form a basis in the subspace orthogonal to passive interference.
Since the passive interference is strong by assumption, the inequality λ _{m} >> 1 holds, which allows us to neglect the first term on the righthand side of (5), after which we obtain an approximation of the inverse matrix in the form
which is a projector on the orthogonal passive interference subspace [7]. The action of operator (6) on any linear combination of sinusoids
leads to its cancellation.In the practical implementation of this method, it is not necessary to determine the operator (6) to use the laborious procedure of finding the eigenvectors and eigenvalues of the matrix (4), since the projector on the orthogonal passive interference subspace can be represented in the form [7]
where E is the identity matrix;
P = S (S ^{H} S) ^{1} S ^{H}  matrix projector on the subspace of interference;
S  matrix composed of column vectors
whose Doppler frequencies with some step they cover the frequency range of passive interference.It should be emphasized that (7) does not depend on the powers of the sinusoids forming a passive noise, as a result of which there is no need for any a priori assumptions about the shape of its spectrum. In this case, the parameters of the notch zone are regulated only by the number and placement of zeros of the transfer characteristic, the coordinates of which are determined by the frequencies
.Taking into account approximation (7), the optimal weight vector of interperiod processing takes the form
and the optimal processing procedure will be to calculate the expression module
where Y is the vector corresponding to the received azimuthal packet.
In this case, the projector matrix (7) performs the function of a passive interference rejector and is common to all Doppler filters. Zeros of the notch area are determined by the frequency values
vectors forming the matrix S in expression (7).The final step of the proposed method is to compare the decisive statistics obtained during signal processing in the Russian Federation with a certain threshold. Depending on the result of the comparison, a decision is made about the presence or absence of a useful signal in this resolution element.
In figures 45 shows a view of a twodimensional primary processing before selection of moving targets and after.
Thus, the synthesized time processing method (7) significantly exceeds in its characteristics the wellknown quasioptimal methods for interperiod filtering of the useful signal against the background of intrinsic noise and passive external interference. Emerging from the optimal method (2) by replacing an a priori unknown matrix R with its realistic approximation, the proposed method has, in essence, a Bayesian structure, which determines its high efficiency in terms of increasing the efficiency of radar noise immunity in the presence of passive interference.
Bibliography
1. Shirman Y.D., Manzhos V.N. The theory and technique of processing radar information against the background of interference.  M.: Radio and Communications, 1981.
2. Mikheev P.V. Optimal analoguediscrete filter by the criterion of signaltonoise ratio // Radar, navigation, communication. XI International Scientific and Technical Conference. April 1214, 2005 T. 1. Voronezh, 2005. 2028.
3. Proskurin V.I. Quadratic filters to detect an unknown signal against a background of correlated interference.  Radio engineering and electronics, 1992, No. 7.
4. Marple ml. S.L. Digital spectral analysis and its application: Per. from English  M.: Mir, 1990.
5. Trunk J.V. Loss coefficient during the accumulation of noise in SDS systems. TIIER, 1977, v. 65, No. 11, p. 115116.
6. Goldenberg L. M., Matyushkin B. D., Polyak M. N. Digital signal processing. Directory.  M .: Radio and communications, 1985.
7. Lancaster P. Matrix Theory: Per. from English  M.: Science, 1982.
8. Bakulev P.A. Methods and devices for moving targets selection / P.A. Bakulev, V.M. Stepin.  M .: Radio and communications, 1986.  288 p.
Claims (1)
 A method for selecting moving targets, including preliminary coherent rejection of passive interference and the subsequent accumulation of a useful signal using a multichannel system of coherent interperiod filters, each of which is matched to a certain Doppler frequency of the signal, characterized in that the passive interference signal is approximated by its projection onto a finitedimensional subspace and output weight vector according to the formula:
where E is the identity matrix;
P = S (S ^{H} S) ^{1} S ^{H}  matrix projector on the subspace of interference;
S  matrix composed of column vectors whose Doppler frequencies with a certain step they cover the frequency range of passive interference in order to provide a given suppression coefficient;
is the vector of the useful signal,  Doppler frequency of the useful signal,
and also produce subsequent coherent accumulation of the useful signal, after which the useful signal is extracted from the received signal using a predetermined threshold.
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RU172404U1 (en) *  20170220  20170707  Федеральное государственное бюджетное образовательное учреждение высшего образования "Рязанский государственный радиотехнический университет"  Passive interference manager 
RU172405U1 (en) *  20170403  20170707  Федеральное государственное бюджетное образовательное учреждение высшего образования "Рязанский государственный радиотехнический университет"  Passive interference reduction device 
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RU172404U1 (en) *  20170220  20170707  Федеральное государственное бюджетное образовательное учреждение высшего образования "Рязанский государственный радиотехнический университет"  Passive interference manager 
RU172405U1 (en) *  20170403  20170707  Федеральное государственное бюджетное образовательное учреждение высшего образования "Рязанский государственный радиотехнический университет"  Passive interference reduction device 
RU176751U1 (en) *  20170515  20180126  Федеральное государственное бюджетное образовательное учреждение высшего образования "Рязанский государственный радиотехнический университет"  Rejector filtration computer 
RU2674467C1 (en) *  20171013  20181211  Федеральное государственное бюджетное образовательное учреждение высшего образования "Рязанский государственный радиотехнический университет"  Filter compensation of passive interference 
RU2674468C1 (en) *  20171016  20181211  Федеральное государственное бюджетное образовательное учреждение высшего образования "Рязанский государственный радиотехнический университет"  Interference rejection filter 
RU2735216C2 (en) *  20181214  20201028  Российская Федерация, от имени которой выступает Министерство промышленности и торговли Российской Федерации (Минпромторг России)  Method for spatiotemporal adaptive signal processing in a monopulse shipborne radar with an active phased antenna array 
RU2729886C1 (en) *  20190807  20200813  Акционерное общество "Научноисследовательский институт Приборостроения имени В.В. Тихомирова"  Method for passive jamming suppression with low doppler shift 
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