CN104459659B - Wave beam Doppler domain space-time two-dimensional self-adaptive processing method based on data fitting - Google Patents

Abstract

The invention discloses a wave beam Doppler domain space-time two-dimensional self-adaptive processing method based on data fitting, and relates to radar technology. The method comprises the steps that 1, wave beam Doppler domain dimensionality reduction echo data and space-time guide vectors are obtained; 2, a data basis matrix of a distance unit to be detected is constructed; 3, a data basis matrix with targets blocked is obtained; 4, the optimal fitting coefficient of auxiliary echo data is obtained; 5, the minimum fitting error of data of the distance unit to be detected is obtained; 6, unit average constant false alarm rate detection is carried out on the minimum fitting error of the data of the distance unit to be detected. The wave beam Doppler domain space-time two-dimensional self-adaptive processing method mainly solves the problem that a traditional STAP method is huge in computation burden and strict in sample requirement, improves movable target detection probability, reduces detection false alarm rate, and has the good application prospect in measured data processing and STAP performance improvement.

Description

Wave beam Doppler domain space-time adaptive processing method based on data matching

Technical field

The invention belongs to communication technical field, it is related to Radar Technology, many particularly to a kind of wave beam based on data matching General Le domain space-time adaptive processing method, for airborne early warning Radar Signal Processing.

Background technology

The main task of airborne early warning radar is detection target in complex clutter background, and carries out locating and tracking to it, And it is the core means improving airborne early warning radar service behaviour that clutter is effectively suppressed.Space-time adaptive is processed (space-time adaptive processing, stap) technology makes full use of spatially and temporally information, to echo signal While carrying out coherent accumulation, processed by space-time adaptive and filter ground clutter, realizing airborne early warning radar has to target Effect detection, such as the e2-d airborne early warning radar of the U.S. just adopt this technology.

In actual applications, stap technology is primarily present following two aspect problems: on the one hand, in clutter environment heterogeneous In, enough independent same distribution (independent and identically for estimate covariance matrix will be obtained Distributed, iid) training sample is extremely difficult；On the other hand, even if the demand of training sample is met, at full space-time The excessive problem of reason amount of calculation can lead to real-time to be difficult to ensure that.

For solving the above problems, promote stap technology more practical, there has been proposed many corrective measures or method.Clearly Patent of invention " the space-time adaptive processing method under non-homogeneous clutter environment " (number of patent application of Hua Da application 201010129723.3, publication No. cn 101819269 a) disclose a kind of in non-homogeneous clutter environment super-resolution estimate miscellaneous The overcomplete sparse representation method of ripple space-time two-dimensional spectrum.The method achieve in the case of independent same distribution sample number deficiency, Estimate clutter covariance matrix using single frames training sample, thus avoiding strong non-homogeneous clutter environment to self-adaptive processing effect Impact.But, the method yet suffers from following main deficiency is: one is the big problem of operand, carries out sparse table to clutter spectrum The super complete radix mesh showing is uncertain, but is much larger than degree of freedom in system, and degree of freedom in system is generally thousands of in practice, so exists In the covariance matrix restructuring procedure of each range cell sample, required operand is very big, is unfavorable for real-time processing, from And have influence on the effect in practical engineering application.Two is base mismatch problems, and used in the method, actually one group of base is inserted Value discrete fourier dft vector, and the echo data of reality because there is the non-ideal factors such as error it is impossible to basic vector Amount rarefaction representation；In addition, the method needs to carry out discretization expression to space-time plane, such that not being located at discrete grid block point The signal at center is revealed to all mesh points, thus destroying the openness of echo data.

Content of the invention

Overcomplete sparse representation side for above-mentioned super-resolution estimation clutter space-time two-dimensional spectrum in non-homogeneous clutter environment The deficiency of method, the present invention proposes a kind of wave beam Doppler domain space-time adaptive processing method based on data matching, energy Enough detections realized to echo signal, and solve the problems, such as in above-mentioned sparse recoverys stap method operand huge with base mismatch, Simultaneously with respect to traditional wave beam Doppler domain stap method, improve the detection probability of moving-target, reduce the false alarm rate of detection.

For reaching above-mentioned purpose, the present invention employ the following technical solutions pre- with realize.

A kind of wave beam Doppler domain space-time adaptive processing method based on data matching is it is characterised in that include Following steps:

Step 1, receives the space-time two-dimensional echo data x of airborne early warning radar, to space-time using mechanical early warning radar antenna Two-dimentional echo data x carries out joint domain positioning jdl dimensionality reduction, obtains dimensionality reduction echo data z and the space-time guiding of wave beam Doppler domain Vector s_z-jdl；

Step 2, constructs the data basic matrix φ of range cell to be detected_k；

Step 3, using the dimensionality reduction space-time steering vector s of wave beam Doppler domain_z-jdlDerive blocking matrix b；Using obstruction square Battle array b and data z of range cell to be detected_k, obtain target and block later auxiliary echo dataThis range cell to be detected Data z_kTake from the dimensionality reduction echo data z of the wave beam Doppler domain obtaining in step 1 successively；Using blocking matrix b and to be checked Survey the data basic matrix φ of range cell_kObtain target and block later data basic matrix

Step 4, blocks later data basic matrix using targetTo auxiliary echo dataCarry out data matching, obtain Take auxiliary echo dataOptimal fitting coefficient

Step 5, using the data basic matrix φ of range cell to be detected_kWith auxiliary echo dataOptimal fitting coefficientTreat detecting distance cell data z_kCarry out data matching, obtain minimum error of fitting y of the data of range cell to be detected_k； Data z of this range cell to be detected_kTake from the dimensionality reduction echo data z of the wave beam Doppler domain obtaining in step 1 successively；

Step 6, treats minimum error of fitting y of the data of detecting distance unit_kCarry out CA-CFAR detection, and There is target or there is not target in output.

The feature of technique scheme and further improvement is that:

(1) step 1 specifically includes following sub-step:

1a) utilize airborne early warning radar antenna, receive the space-time two-dimensional number of echoes of ground return within the coherent accumulation time According to x, this space-time two-dimensional echo data x ties up for mn, and wherein m represents umber of pulse, and n represents the array number of airborne early warning radar antenna.

1b) the dimensionality reduction transition matrix p that joint domain positions jdl dimensionality reduction is obtained according to following formula_jdl:

$p_{\mathrm{jdl}}=p_t(f_{i-1},f_i,f_{i+1})\&circletimes;p_s(g_{j-1},g_j,g_{j+1})$

Wherein, p_t(f_i-1,f_i,f_i+1) represent the time domain transition matrix being made up of three Doppler filters closing on, f_iTable Show the normalization Doppler frequency of target, f_i-1,f_i+1Represent the two normalization Doppler frequencies adjacent with target；I be less than Natural number equal to m and more than or equal to 1, m represents umber of pulse, p_s(g_j-1,g_j,g_j+1) represent be made up of three spatial filters Spatial domain transition matrix, the wave filter in the middle of these three spatial filters is the spatial domain steering vector of target, g_jReturning for target One change spatial frequency, g_j-1,g_j+1Be neighbouring two normalization spatial frequency, j be less than or equal to m and more than or equal to 1 from So count；Represent Kronecker product；Dimensionality reduction transition matrix p_jdlDimension be mn × 9 tie up；

1c) according to dimensionality reduction transition matrix p_jdlObtain the dimensionality reduction echo data z of wave beam Doppler domain, be expressed as following formula:

$z=p_{\mathrm{jdl}}^{h}x$

Wherein, ()^hRepresent conjugate transposition operation, according to dimensionality reduction transition matrix p_jdlDimension be mn × 9 dimension and space-time The dimension that two-dimentional echo data x can obtain dimensionality reduction echo data z for mn dimension is 9 dimensions.

The dimensionality reduction space-time steering vector s of wave beam Doppler domain_z-jdl, it is expressed as following formula:

$s_{z-\mathrm{jdl}}={\left[\begin{array}{ccc}0& 1& 0\end{array}\right]}^t\&circletimes;{\left[\begin{array}{ccc}0& 1& 0\end{array}\right]}^t$

Wherein, ()^tRepresent transposition operation, s_z-jdlFor the space-time steering vector after the dimensionality reduction of wave beam Doppler domain.

(2) step 2 specifically includes:

Data basic matrix φ_kIt is expressed as following formula:

φ_k=[z₁z₂… z_l]∈c^d×l

Wherein, φ_kRepresent the data basic matrix of k-th range cell to be detected；K represents the data of range cell to be detected Sequence number, be less than the natural number of all range cells to be detected sums；D represents the dimensionality reduction echo data of wave beam Doppler domain The dimension of z, l represents the number of the data closing on range cell, c^d×lRepresent the complex matrix space of d × l dimension, z_l(l=1,2 ..., L) represent and close on the data of range cell, close on range cell and represent range cell near range cell to be detected, do not comprise Two are used for protecting the range cell of target.

(3) step 3 specifically includes following sub-step:

3a) the dimensionality reduction space-time steering vector s to wave beam Doppler domain_z-jdlTransposed form carry out singular value decomposition, obtain Left singular matrix u, right singular matrix v and singular value matrix λ；Decomposition formula such as following formula:

$s_{z-\mathrm{jdl}}^t=u\·\mathrm{\λ}\·v^h$

Wherein, u represents that left singular matrix, v represent that right singular matrix, λ represent singular value matrix；(·)^tRepresent transposition behaviour Make, ()^hRepresent conjugate transposition operation；

3b) blocking matrix b is arranged to make up by the 2nd to n row order of right singular matrix, by right singular matrix v, and following formula Derive blocking matrix b；

B=[v (:, 2:n)]^t

Wherein, v (:, 2:n) represents all row of right singular matrix v, the 2nd arrives n column matrix element；(·)^tRepresent transposition behaviour Make；

3c) auxiliary echo data is obtained according to blocking matrix bBlock later data basic matrix with targetAs follows Shown in formula:

${\stackrel{\&overbar;}{z}}_k={\mathrm{bz}}_k,{\stackrel{\&overbar;}{\mathrm{\φ}}}_k=b{\mathrm{\φ}}_k$

Wherein, z_kRepresent the data of k-th range cell to be detected, the data of this range cell to be detected takes from step successively The dimensionality reduction echo data z of the wave beam Doppler domain obtaining in rapid 1, k represents the sequence number of the data of range cell to be detected, for not surpassing Cross the natural number of all range cell sums to be detected；Represent that target blocks later auxiliary echo data,Represent target Block later data basic matrix, b is blocking matrix.

(4) step 4 specifically includes following sub-step:

4a) block later data basic matrix with targetLater auxiliary echo data is blocked to targetCarry out A young waiter in a wineshop or an inn takes advantage of matching to represent, is expressed as following formula:

$\underset{{\stackrel{\&overbar;}{\mathrm{\α}}}_k}{\min }{\left|\right|{\stackrel{\&overbar;}{z}}_k-{\stackrel{\&overbar;}{\mathrm{\φ}}}_k{\stackrel{\&overbar;}{\mathrm{\α}}}_k\left|\right|}_2$

Wherein, | | | |₂Represent 2 norms solving vector,Represent auxiliary echo dataFitting coefficient.

4b) pass through to solve above formula optimization problem, obtain assisting echo dataOptimal fitting coefficient

(5) step 5 specifically includes following sub-step:

5a) with the data basic matrix φ of range cell to be detected_k, treat data z of detecting distance unit_kCarry out a young waiter in a wineshop or an inn Take advantage of matching；And with assisting echo dataOptimal fitting coefficientIt is calculated optimization restrained boundary η_k, especially by following formula Least square fitting formula is realized:

$\underset{{\mathrm{\α}}_k}{\min }{\left|\right|z_k-{\mathrm{\φ}}_k{\mathrm{\α}}_k\left|\right|}_2,\mathrm{subject\; to}{\left|\right|{\mathrm{\α}}_k\left|\right|}_2\≤{\mathrm{\η}}_k$

Wherein, α_kRepresent the fitting coefficient of the data of range cell to be detected, η_kRepresent and optimize restrained boundary,

5b) by solving the optimal fitting coefficient that above formula least square fitting formula obtains the data of range cell to be detected

The optimal fitting coefficient of the data according to range cell to be detectedObtain the data of range cell to be detected Little error of fitting y_k, obtained by following formula:

$y_k={\left|\right|z_k-{\mathrm{\φ}}_k{\mathrm{\α}}_k^{*}\left|\right|}_2$

Wherein, z_kRepresent the data of k-th range cell to be detected, the data of this range cell to be detected takes from step successively The dimensionality reduction echo data z of the wave beam Doppler domain obtaining in rapid 1, k represents the sequence number of the data of range cell to be detected, for not surpassing Cross the natural number of all range cell sums to be detected；φ_kRepresent the data basic matrix of range cell to be detected；Represent to be checked Survey the optimal fitting coefficient of the data of range cell；||·||₂Represent 2 norms solving vector.

Compared with prior art, the present invention has prominent substantive distinguishing features and significantly improves.The present invention with existing Stap method is compared, and has the advantage that

(1) it is directed to the big problem of operand in existing sparse recovery stap method, sparse restoration methods are carried out to clutter spectrum The super complete radix mesh of rarefaction representation is uncertain, but is much larger than degree of freedom in system, and degree of freedom in system is generally thousands of in practice, So required in the covariance matrix restructuring procedure of each range cell sample operand is very big, is unfavorable for locating in real time Reason, thus have influence on its practical engineering application effect.Institute's extracting method is positioned (joint domain with combining domain by the present invention Localized, jdl) method combines, and the little optimization problem that the optimization problem of original mn dimension is decomposed into m 9 dimension to solve, Greatly reduce the computation complexity of algorithm, solve the problems, such as that in sparse recovery stap method, operand is big.

(2) it is directed to base mismatch problems in existing sparse recovery stap method, used in sparse restoration methods, represent that base is real It is one group of interpolation dft vector on border, and real data is because have the non-ideal factors such as error it is impossible to sparse with given base vector Represent, in addition, the method needs to carry out discretization expression to space-time plane, such that not being located at discrete grid block dot center Signal is revealed to all mesh points, thus destroying the openness of data.The present invention is directly used real data sample as base Matrix, rather than the ideal flowing pattern being determined using certain, thus evaded asking of base mismatch in above-mentioned sparse recovery stap method Topic.

(3) traditional wave beam Doppler domain stap method, such as joint domain localization method (jdl), by choosing distance to be detected Data sample around unit, to estimate the statistical property of range cell data to be detected, to carry out clutter and AF panel.So And when not containing interference data to be suppressed in the data sample chosen, it is present in the interference letter in range cell data to be detected Number cannot suppress, form false-alarm.The inventive method on the basis of blocking operation is carried out to echo signal, to pending number According to carrying out abundant matching expression, so that main lobe echo signal is retained, and (formula is also referred to as cheated to secondary lobe echo signal Interference) carry out abundant suppression.

It can be seen that, in prior art, stap carries out airborne radar signal processing generally in high-dimensional data space, thus can Greatly increase computational complexity and required training sample number, in order to obtain better performance, the present invention is directed to above-mentioned asking Topic, closes on the data sample of range cell come Optimal Fitting range cell to be detected clutter data using range cell to be detected, Thus improving the clutter recognition performance of Adaptive Signal Processing, improve the detection probability of target.

Brief description

The present invention will be further described with reference to the accompanying drawings and detailed description.

Fig. 1 is flow chart of the present invention；

Fig. 2 is the distance-Doppler result figure after general pulse doppler processing；

Fig. 3 is the change curve with border compensation dosage for the detection probability；

Fig. 4 is the change curve with sample number for the detection probability；

Fig. 5 is the result figure of the data of partial distance-doppler cells；Fig. 5 (a) is conventional wave beam Doppler domain Distance-Doppler result figure after the process of stap algorithm jdl；Fig. 5 (b) is the distance-Doppler after the inventive method process Result figure；

Fig. 6 is the change curve with target power for the detection probability；

Fig. 7 is the change curve graph of a relation with false alarm rate for the detection probability.

Specific embodiment

With reference to Fig. 1, a kind of wave beam Doppler domain space-time adaptive processing side based on data matching of the present invention is described Method, the present invention is used for airborne early warning Radar Signal Processing, and its specific implementation step is as follows:

Step 1, receives the space-time two-dimensional echo data x of airborne early warning radar, to space-time using mechanical early warning radar antenna Two-dimentional echo data x carries out joint domain positioning jdl dimensionality reduction, obtains dimensionality reduction echo data z and the space-time guiding of wave beam Doppler domain Vector s_z-jdl；

Step 1 specifically includes following sub-step:

1a) utilize airborne early warning radar antenna, receive the space-time two-dimensional number of echoes of ground return within the coherent accumulation time According to x, this space-time two-dimensional echo data x ties up for mn, and wherein m represents umber of pulse, and n represents the array number of airborne early warning radar antenna.

1b) the dimensionality reduction transition matrix p that joint domain positions jdl dimensionality reduction is obtained according to following formula_jdl:

$p_{\mathrm{jdl}}=p_t(f_{i-1},f_i,f_{i+1})\&circletimes;p_s(g_{j-1},g_j,g_{j+1})$

Wherein, p_t(f_i-1,f_i,f_i+1) represent the time domain transition matrix being made up of three Doppler filters closing on, f_iTable Show the normalization Doppler frequency of target, f_i-1,f_i+1Represent the two normalization Doppler frequencies adjacent with target；I be less than Natural number equal to m and more than or equal to 1, m represents umber of pulse, p_s(g_j-1,g_j,g_j+1) represent be made up of three spatial filters Spatial domain transition matrix, the wave filter in the middle of these three spatial filters is the spatial domain steering vector of target, g_jReturning for target One change spatial frequency, g_j-1,g_j+1Be neighbouring two normalization spatial frequency, j be less than or equal to m and more than or equal to 1 from So count；Represent Kronecker product；Dimensionality reduction transition matrix p_jdlDimension be mn × 9 tie up.

1c) according to dimensionality reduction transition matrix p_jdlObtain the dimensionality reduction echo data z of wave beam Doppler domain, be expressed as following formula:

$z=p_{\mathrm{jdl}}^{h}x$

Wherein, ()^hRepresent conjugate transposition operation, according to dimensionality reduction transition matrix p_jdlDimension be mn × 9 dimension and space-time The dimension that two-dimentional echo data x can obtain dimensionality reduction echo data z for mn dimension is 9 dimensions.

The dimensionality reduction space-time steering vector s of wave beam Doppler domain_z-jdl, it is expressed as following formula:

$s_{z-\mathrm{jdl}}={\left[\begin{array}{ccc}0& 1& 0\end{array}\right]}^t\&circletimes;{\left[\begin{array}{ccc}0& 1& 0\end{array}\right]}^t$

Wherein, ()^tRepresent transposition operation, s_z-jdlFor the space-time steering vector after the dimensionality reduction of wave beam Doppler domain.

General space-time steering vector s is represented by the Kronecker product form of time domain steering vector and spatial domain steering vector, I.e.f_dAnd f_sRepresent normalized Doppler frequency and spatial frequency respectively.Time domain and spatial domain steering vector It is represented by respectively $\begin{array}{c}{s}_t\left(f_d\right)={\left[\begin{array}{cccc}1& \exp \left(j2\mathrm{\π}{f}_d\right)& ...& \exp \left(j2\mathrm{\π}(m-1)f_d\right)\end{array}\right]}^t\\ s_s\left(f_s\right)={\left[\begin{array}{cccc}1& \exp \left(j2\mathrm{\π}{f}_s\right)& ...& \exp \left(j2\mathrm{\π}(n-1)f_s\right)\end{array}\right]}^t\end{array}.$

Step 2, constructs the data basic matrix φ of range cell to be detected_k；Data basic matrix φ_kIt is expressed as following formula:

φ_k=[z₁z₂… z_l]∈c^d×l

Wherein, φ_kRepresent the data basic matrix of k-th range cell to be detected；K represents the data of range cell to be detected Sequence number, be less than the natural number of all range cells to be detected sums；D represents the dimensionality reduction echo data of wave beam Doppler domain The dimension of z, l represents the number of the data closing on range cell, c^d×lRepresent the complex matrix space of d × l dimension, z_l(l=1,2 ..., L) represent and close on the data of range cell, close on range cell and represent range cell near range cell to be detected, do not comprise Two are used for protecting the range cell of target.

Step 3, using the dimensionality reduction space-time steering vector s of wave beam Doppler domain_z-jdlDerive blocking matrix b；Using obstruction square Battle array b and data z of range cell to be detected_k, obtain target and block later auxiliary echo dataThis range cell to be detected Data z_kTake from the dimensionality reduction echo data z of the wave beam Doppler domain obtaining in step 1 successively；Using blocking matrix b and to be checked Survey the data basic matrix φ of range cell_kObtain target and block later data basic matrix

Step 3 specifically includes following sub-step:

3a) the dimensionality reduction space-time steering vector s to wave beam Doppler domain_z-jdlTransposed form carry out singular value decomposition, obtain Left singular matrix u, right singular matrix v and singular value matrix λ；Decomposition formula such as following formula:

$s_{z-\mathrm{jdl}}^t=u\·\mathrm{\λ}\·v^h$

Wherein, u represents that left singular matrix, v represent that right singular matrix, λ represent singular value matrix；(·)^tRepresent transposition behaviour Make, ()^hRepresent conjugate transposition operation；

3b) blocking matrix b is arranged to make up by the 2nd to n row order of right singular matrix, by right singular matrix v, and following formula Derive blocking matrix b；

B=[v (:, 2:n)]^t

Wherein, v (:, 2:n) represents all row of right singular matrix v, the 2nd arrives n column matrix element；(·)^tRepresent transposition behaviour Make；

3c) auxiliary echo data is obtained according to blocking matrix bBlock later data basic matrix with targetAs follows Shown in formula:

${\stackrel{\&overbar;}{z}}_k={\mathrm{bz}}_k,{\stackrel{\&overbar;}{\mathrm{\φ}}}_k=b{\mathrm{\φ}}_k$

Wherein, z_kRepresent the data of k-th range cell to be detected, the data of this range cell to be detected takes from step successively The dimensionality reduction echo data z of the wave beam Doppler domain obtaining in rapid 1, k represents the sequence number of the data of range cell to be detected, for not surpassing Cross the natural number of all range cell sums to be detected；Represent that target blocks later auxiliary echo data,Represent target Block later data basic matrix, b is blocking matrix.

Step 4, blocks later data basic matrix using targetTo auxiliary echo dataCarry out data matching, obtain Take auxiliary echo dataOptimal fitting coefficient

4a) block later data basic matrix with targetLater auxiliary echo data is blocked to targetCarry out A young waiter in a wineshop or an inn takes advantage of matching to represent, is expressed as following formula:

$\underset{{\stackrel{\&overbar;}{\mathrm{\α}}}_k}{\min }{\left|\right|{\stackrel{\&overbar;}{z}}_k-{\stackrel{\&overbar;}{\mathrm{\φ}}}_k{\stackrel{\&overbar;}{\mathrm{\α}}}_k\left|\right|}_2$

Wherein, | | | |₂Represent 2 norms solving vector,Represent auxiliary echo dataFitting coefficient.

4b) pass through to solve above formula optimization problem, obtain assisting echo dataOptimal fitting coefficient

Step 5, using the data basic matrix φ of range cell to be detected_kWith auxiliary echo dataOptimal fitting coefficientTreat detecting distance cell data z_kCarry out data matching, obtain minimum error of fitting y of the data of range cell to be detected_k； Data z of this range cell to be detected_kTake from the dimensionality reduction echo data z of the wave beam Doppler domain obtaining in step 1 successively；

Step 5 specifically includes following sub-step:

5a) with the data basic matrix φ of range cell to be detected_k, treat data z of detecting distance unit_kCarry out a young waiter in a wineshop or an inn Take advantage of matching；And with assisting echo dataOptimal fitting coefficientIt is calculated optimization restrained boundary η_k, especially by following formula Least square fitting formula is realized:

$\underset{{\mathrm{\α}}_k}{\min }{\left|\right|z_k-{\mathrm{\φ}}_k{\mathrm{\α}}_k\left|\right|}_2,\mathrm{subject\; to}{\left|\right|{\mathrm{\α}}_k\left|\right|}_2\≤{\mathrm{\η}}_k$

Wherein, α_kRepresent the fitting coefficient of the data of range cell to be detected, η_kRepresent and optimize restrained boundary,

5b) by solving the optimal fitting coefficient that above formula least square fitting formula obtains the data of range cell to be detected

The optimal fitting coefficient of the data according to range cell to be detectedObtain the data of range cell to be detected Little error of fitting y_k, obtained by following formula:

$y_k={\left|\right|z_k-{\mathrm{\φ}}_k{\mathrm{\α}}_k^{*}\left|\right|}_2$

Wherein, z_kRepresent the data of k-th range cell to be detected, the data of this range cell to be detected takes from step successively The dimensionality reduction echo data z of the wave beam Doppler domain obtaining in rapid 1, k represents the sequence number of the data of range cell to be detected, for not surpassing Cross the natural number of all range cell sums to be detected；φ_kRepresent the data basic matrix of range cell to be detected；Represent to be checked Survey the optimal fitting coefficient of the data of range cell；||·||₂Represent 2 norms solving vector.

Step 6, treats minimum error of fitting y of the data of detecting distance unit_kCarry out CA-CFAR detection, and There is target or there is not target in output.

Treat minimum error of fitting y of the data of detecting distance unit_kCarry out CA-CFAR detection (cell- Averaging constant false alarm rate, ca-cfar), will minimum error of fitting y_kSingle with distance to be detected The meansigma methodss of the minimum error of fitting of the data of the range cell of surrounding of unit compare, according to minimum error of fitting y_kWith meansigma methodss Ratio size, to determine whether there is target, finally there will be target or there is not target output.

With reference to emulation experiment, the effect of the present invention is described further.

(1) experiment condition:

The experiment of the present invention is carried out under matlab 7.11 software.In the experimental design of the present invention, in order to check this Inventive method effectiveness in actual applications, processing data is derived from the measured data mcarm data set of external admission, its radar It is operated under positive side battle array pattern, front is made up of the spatial domain array element that can be used for Adaptive Signal Processing that 2 row 11 arrange, its part system System parameter is with reference to table 1.In order to test conveniently, the present invention extracts 11 array elements of the first row in mcarm data, front 32 pulses Echo data is verified.

Table 1

(2) Comparison of experiment results

Accompanying drawing 2 is general pulse doppler processing result figure.Transverse axis represents Doppler's channel position, and the longitudinal axis represents that distance is single First sequence number.Range cell is also referred to as range gate in the present invention.Although as can be seen from Figure 2 this radar is operated in positive side battle array situation Under, but the strong clutter component in space-time two-dimensional echo data, by various in the different modulation of landform reflectance and actual environment The presence of false target, has certain heterogeneity.

Accompanying drawing 3 is the change curve with border compensation dosage for the detection probability.Transverse axis represents boundary compensation, and the longitudinal axis represents detection Probability.When boundary compensation refers to optimize restrained boundary in solution procedure 5, the optimization upper bound of gained adds a compensation dosage.From Fig. 3 In as can be seen that basic near 0 compensation dosage, detection probability can obtain maximum, and this also just illustrates step 5 in the present invention Calculation optimization restrained boundary is effective and optimum.

Accompanying drawing 4 is the change curve with sample number for the detection probability.Sample number refers to close on range cell in the present invention The number of data.Transverse axis represents sample number, and the longitudinal axis represents detection probability.In the present invention after the dimension-reduction treatment of data jdl, data Dimension (i.e. degree of freedom) is reduced to 9.Figure 4, it is seen that detection probability basically reaches maximum when sample number is more than 36, this Just illustrate, in the present invention, data basic matrix can nearby 4 times of number of degrees of freedom, purposes close on range cell by range cell to be detected Data, to constitute, obtains the detection performance of optimum.

Accompanying drawing 5 is the result figure of the data of partial distance-doppler cells.In the present invention, partial distance-how general The data strangling unit includes the data of multigroup range cell to be detected.Transverse axis represents Doppler's passage, from 17 to 32；The longitudinal axis represents Range gate sequence number, from 140 to 240.Range cell is also referred to as range gate in the present invention.In this experiment, two main lobe target (figures In represented with circle) and four secondary lobe targets (in figure is represented with rhombus) be artificially added to respectively in experimental data.We need To be detected is main lobe target, and secondary lobe target is Deceiving interference, shows as false-alarm.Figure (a) is that how general conventional wave beam is Strangle the result figure of domain stap algorithm jdl, it can be seen that main lobe target can not be evident from, and secondary lobe target is not Can be inhibited, false-alarm occurs.Figure (b) is the output result after the inventive method process, it is seen that secondary lobe target letter Number obtain fine suppression, main lobe target also can be detected well.

Fig. 6 is the change curve with target power for the detection probability.Transverse axis represents the target power of artificial setting, longitudinal axis table Show the detection probability of this target.The sparse restoration methods of direct data domain (d3sr), classical joint domain localization method is compared in Fig. 6 And joint domain localization method (df-jdl) based on data matching proposed by the present invention (jdl).From fig. 6 it can be seen that this The detection performance of bright method is much better than other two methods.

Fig. 7 is the change curve graph of a relation with false alarm rate for the detection probability.Transverse axis represents false alarm rate, and the longitudinal axis represents that detection is general Rate.Classical joint domain localization method (jdl) and the joint domain positioning side based on data matching proposed by the present invention is compared in Fig. 7 Method (df-jdl).It can be seen from figure 7 that the inventive method, with respect to traditional jdl method, has higher under certain false alarm rate Detection probability, and there is under identical detection probability lower false alarm rate.

Claims (5)

1. a kind of wave beam Doppler domain space-time adaptive processing method based on data matching it is characterised in that include with Lower step:

Step 1, receives the space-time two-dimensional echo data x of airborne early warning radar, to space-time two-dimensional using airborne early warning radar antenna Echo data x carries out joint domain positioning jdl dimensionality reduction, obtains dimensionality reduction echo data z and the space-time steering vector of wave beam Doppler domain s_z-jdl；

Step 2, constructs the data basic matrix φ of range cell to be detected_k；

Step 3, using the dimensionality reduction space-time steering vector s of wave beam Doppler domain_z-jdlDerive blocking matrix b；Using blocking matrix b and Data z of range cell to be detected_k, obtain target and block later auxiliary echo dataThe data of this range cell to be detected z_kTake from the dimensionality reduction echo data z of the wave beam Doppler domain obtaining in step 1 successively；Using blocking matrix b and distance to be detected The data basic matrix φ of unit_kObtain target and block later data basic matrix

Step 3 specifically includes following sub-step:

3a) the dimensionality reduction space-time steering vector s to wave beam Doppler domain_z-jdlTransposed form carry out singular value decomposition, obtain left strange Different matrix u, right singular matrix v and singular value matrix λ；Decomposition formula such as following formula:

$s_{z-jdl}^t=u\·\mathrm{\λ}\·v^h$

Wherein, u represents that left singular matrix, v represent that right singular matrix, a represent singular value matrix；()^tRepresent transposition operation, ()^hRepresent conjugate transposition operation；

3b) blocking matrix b is arranged to make up by the 2nd to n row order of right singular matrix, and by right singular matrix v, and following formula is derived Blocking matrix b；

B=[v (:, 2:n)]^t

Wherein, v (:, 2:n) represents all row of right singular matrix v, the 2nd arrives n column matrix element；(·)^tRepresent transposition operation；

3c) auxiliary echo data is obtained according to blocking matrix bBlock later data basic matrix with targetIt is shown below:

${\stackrel{\&overbar;}{z}}_k={\mathrm{bz}}_k,{\stackrel{\&overbar;}{\mathrm{\φ}}}_k={\mathrm{b\φ}}_k$

Wherein, z_kRepresent the data of k-th range cell to be detected, the data of this range cell to be detected is taken from step 1 successively The dimensionality reduction echo data z of the wave beam Doppler domain obtaining, k represents the sequence number of the data of range cell to be detected, is less than institute Need the natural number of detecting distance unit sum；Represent that target blocks later auxiliary echo data,Represent that target is blocked Later data basic matrix, b is blocking matrix；

Step 4, blocks later data basic matrix using targetTo auxiliary echo dataCarry out data matching, obtain auxiliary Echo dataOptimal fitting coefficient

Step 5, using the data basic matrix φ of range cell to be detected_kWith auxiliary echo dataOptimal fitting coefficientRight Range cell data z to be detected_kCarry out data matching, obtain minimum error of fitting y of the data of range cell to be detected_k；This is treated Data z of detecting distance unit_kTake from the dimensionality reduction echo data z of the wave beam Doppler domain obtaining in step 1 successively；

Step 6, treats minimum error of fitting y of the data of detecting distance unit_kCarry out CA-CFAR detection, and export There is target or there is not target.

2. the wave beam Doppler domain space-time adaptive processing method based on data matching according to claim 1, its It is characterised by, step 1 specifically includes following sub-step:

1a) utilize airborne early warning radar antenna, receive the space-time two-dimensional echo data x of ground return within the coherent accumulation time, This space-time two-dimensional echo data x ties up for mn, and wherein m represents umber of pulse, and n represents the array number of airborne early warning radar antenna；

1b) the dimensionality reduction transition matrix p that joint domain positions jdl dimensionality reduction is obtained according to following formula_jdl:

$p_{jdl}=p_t(f_{i-1},f_i,f_{i+1})\&circletimes;p_s(g_{j-1},g_j,g_{j+1})$

Wherein, p_t(f_i-1, f_i, f_i+1) represent the time domain transition matrix being made up of three Doppler filters closing on, f_iRepresent mesh Target normalization Doppler frequency, f_i-1, f_i+1Represent the two normalization Doppler frequencies adjacent with target；I is less than or equal to m And the natural number more than or equal to 1, m represents umber of pulse, p_s(g_j-1, g_j, g_j+1) represent the spatial domain being made up of three spatial filters Transition matrix, the wave filter in the middle of these three spatial filters is the spatial domain steering vector of target, g_jEmpty for the normalization of target Between frequency, g_j-1, g_j+1Be neighbouring two normalization spatial frequency, j be less than or equal to m and more than or equal to 1 natural number； Represent Kronecker product；Dimensionality reduction transition matrix p_jdlDimension be mn × 9 tie up；

1c) according to dimensionality reduction transition matrix p_jdlObtain the dimensionality reduction echo data z of wave beam Doppler domain, be expressed as following formula:

$z=p_{jdl}^{h}x$

Wherein, ()^hRepresent conjugate transposition operation, according to dimensionality reduction transition matrix p_jdlDimension be mn × 9 dimension and space-time two-dimensional The dimension that echo data x can obtain dimensionality reduction echo data z for mn dimension is 9 dimensions；

The dimensionality reduction space-time steering vector s of wave beam Doppler domain_z-jdl, it is expressed as following formula:

$s_{z-jdl}={\left[\begin{array}{ccc}0& 1& 0\end{array}\right]}^t\&circletimes;{\left[\begin{array}{ccc}0& 1& 0\end{array}\right]}^t$

Wherein, () t represents that transposition operates, s_z-jdlFor the space-time steering vector after the dimensionality reduction of wave beam Doppler domain.

3. the wave beam Doppler domain space-time adaptive processing method based on data matching according to claim 1, its It is characterised by, step 2 specifically includes:

Data basic matrix φ_kIt is expressed as following formula:

φ_k=[z₁z₂… z_l]∈c^d×l

Wherein, φ_kRepresent the data basic matrix of k-th range cell to be detected；K represents the sequence of the data of range cell to be detected Number, it is less than the natural number of all range cell sums to be detected；D represents the dimensionality reduction echo data z's of wave beam Doppler domain Dimension, l represents the number of the data closing on range cell, c^d×lRepresent the complex matrix space of d × l dimension, z_l(l=1,2 ..., l) Represent and close on the data of range cell, close on range cell and represent range cell near range cell to be detected, do not comprise two The individual range cell for protecting target.

4. the wave beam Doppler domain space-time adaptive processing method based on data matching according to claim 1, its It is characterised by, step 4 specifically includes following sub-step:

4a) block later data basic matrix with targetLater auxiliary echo data is blocked to targetCarry out least square Matching represents, is expressed as following formula:

$\begin{array}{cc}\underset{{\stackrel{\&overbar;}{\mathrm{\α}}}_k}{min}& \left|\right|{\stackrel{\&overbar;}{z}}_k-{\stackrel{\&overbar;}{\mathrm{\φ}}}_k{\stackrel{\&overbar;}{\mathrm{\α}}}_k|{|}_2\end{array}$

Wherein, | | | |₂Represent 2 norms solving vector,Represent auxiliary echo dataFitting coefficient；

4b) pass through to solve above formula optimization problem, obtain assisting echo dataOptimal fitting coefficient

5. the wave beam Doppler domain space-time adaptive processing method based on data matching according to claim 1, its It is characterised by, step 5 specifically includes following sub-step:

5a) with the data basic matrix φ of range cell to be detected_k, treat data z of detecting distance unit_kCarry out least square plan Close；And with assisting echo dataOptimal fitting coefficientIt is calculated optimization restrained boundary η_k, especially by a following formula young waiter in a wineshop or an inn Fitting formula is taken advantage of to realize:

$\begin{array}{cc}\underset{{\mathrm{\α}}_k}{min}\left|\right|z_k-{\mathrm{\φ}}_k{\mathrm{\α}}_k|{|}_2,& subjectto\left|\right|{\mathrm{\α}}_k|{|}_2\≤{\mathrm{\η}}_k\end{array}$

Wherein, α_kRepresent the fitting coefficient of the data of range cell to be detected, η_kRepresent and optimize restrained boundary,

5b) by solving the optimal fitting coefficient that above formula least square fitting formula obtains the data of range cell to be detected

The optimal fitting coefficient of the data according to range cell to be detectedObtain the minimum matching of the data of range cell to be detected Error y_k, obtained by following formula:

$y_k=\left|\right|z_k-{\mathrm{\φ}}_k{\mathrm{\α}}_k^{*}|{|}_2$

Wherein, z_kRepresent the data of k-th range cell to be detected, the data of this range cell to be detected is taken from step 1 successively The dimensionality reduction echo data z of the wave beam Doppler domain obtaining, k represents the sequence number of the data of range cell to be detected, is less than institute Need the natural number of detecting distance unit sum；φ_kRepresent the data basic matrix of range cell to be detected；Represent to be detected away from Optimal fitting coefficient from the data of unit；||·||₂Represent 2 norms solving vector.