CN101819269A - Space-time adaptive processing method under non-homogeneous clutter environment - Google Patents
Space-time adaptive processing method under non-homogeneous clutter environment Download PDFInfo
- Publication number
- CN101819269A CN101819269A CN 201010129723 CN201010129723A CN101819269A CN 101819269 A CN101819269 A CN 101819269A CN 201010129723 CN201010129723 CN 201010129723 CN 201010129723 A CN201010129723 A CN 201010129723A CN 101819269 A CN101819269 A CN 101819269A
- Authority
- CN
- China
- Prior art keywords
- clutter
- alpha
- psi
- empty
- space
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 230000003044 adaptive effect Effects 0.000 title claims abstract description 12
- 238000003672 processing method Methods 0.000 title claims abstract description 7
- 238000001228 spectrum Methods 0.000 claims abstract description 50
- 239000011159 matrix material Substances 0.000 claims abstract description 41
- 238000012549 training Methods 0.000 claims abstract description 40
- 238000000034 method Methods 0.000 claims abstract description 34
- 238000009792 diffusion process Methods 0.000 claims abstract description 11
- 239000013598 vector Substances 0.000 claims description 23
- 239000000284 extract Substances 0.000 claims description 13
- 238000013461 design Methods 0.000 claims description 8
- 238000011084 recovery Methods 0.000 claims description 6
- 230000009467 reduction Effects 0.000 claims description 6
- 238000005070 sampling Methods 0.000 claims description 5
- JEIPFZHSYJVQDO-UHFFFAOYSA-N ferric oxide Chemical compound O=[Fe]O[Fe]=O JEIPFZHSYJVQDO-UHFFFAOYSA-N 0.000 claims description 4
- 238000000605 extraction Methods 0.000 claims description 3
- 238000005516 engineering process Methods 0.000 abstract description 6
- 230000001629 suppression Effects 0.000 abstract 1
- 238000010586 diagram Methods 0.000 description 11
- 238000001914 filtration Methods 0.000 description 7
- 230000006872 improvement Effects 0.000 description 7
- 230000008569 process Effects 0.000 description 5
- 238000013459 approach Methods 0.000 description 4
- 238000012545 processing Methods 0.000 description 4
- 239000012141 concentrate Substances 0.000 description 3
- 230000008878 coupling Effects 0.000 description 3
- 238000010168 coupling process Methods 0.000 description 3
- 238000005859 coupling reaction Methods 0.000 description 3
- 238000012360 testing method Methods 0.000 description 3
- 108700031620 S-acetylthiorphan Proteins 0.000 description 2
- 101150113537 Spib gene Proteins 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- 238000006243 chemical reaction Methods 0.000 description 2
- 238000005457 optimization Methods 0.000 description 2
- 230000004044 response Effects 0.000 description 2
- 238000004088 simulation Methods 0.000 description 2
- 230000009897 systematic effect Effects 0.000 description 2
- 241001269238 Data Species 0.000 description 1
- 240000007594 Oryza sativa Species 0.000 description 1
- 235000007164 Oryza sativa Nutrition 0.000 description 1
- 235000014676 Phragmites communis Nutrition 0.000 description 1
- 238000009825 accumulation Methods 0.000 description 1
- 238000000354 decomposition reaction Methods 0.000 description 1
- 230000007423 decrease Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 239000012467 final product Substances 0.000 description 1
- 238000009472 formulation Methods 0.000 description 1
- 238000010921 in-depth analysis Methods 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 238000010606 normalization Methods 0.000 description 1
- 235000009566 rice Nutrition 0.000 description 1
- 238000012163 sequencing technique Methods 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 238000005728 strengthening Methods 0.000 description 1
- 230000002087 whitening effect Effects 0.000 description 1
Images
Landscapes
- Radar Systems Or Details Thereof (AREA)
Abstract
Space-time adaptive processing method under non-homogeneous clutter environment belongs to airborne radar clutter suppression technology field. Along distance to a snap training sample xl is extracted, clutter space-time two-dimensional spectrum is solved, by sparse solution
The clutter covariance matrix for bringing into estimate; Clutter covariance matrix weighting to estimating; According to the covariance matrix of estimation, calculate corresponding space-time adaptive filter, and output end do moving-target whether there is or not judgement. The IID training sample that the present invention occurs for non-homogeneous clutter environment lacks, and is composed with the method super-resolution estimation clutter space-time two-dimensional of overcomplete sparse representation, and then realize that single frames training sample estimates clutter covariance matrix. The present invention can effectively avoid becoming influence fastly because of discrete noise source, landform, while the also more robust of the space-time spectrum diffusion caused by the factors such as clutter internal motion and channel be inconsistent. Mountaintop real data demonstrates the method and is able to maintain fine clutter recognition performance, is highly suitable for strong non-homogeneous clutter environment.
Description
Technical field
The present invention relates to space-time adaptive under the non-homogeneous clutter environment (STAP) disposal route, be a kind of STAP framework CS-STAP that estimates based on sparse property clutter, utilize the sparse priori of clutter distribution, super-resolution estimation clutter space-time two-dimensional spectrum (space-time spectrum), and then realize that a small amount of training sample can estimate the clutter covariance matrix method, improve the purpose of estimating speed of convergence thereby reach, belong to airborne radar clutter and suppress technical field.
Background technology
The present invention is directed to the problem that independent same distribution in the actual non-homogeneous clutter environment (IID) training sample lacks, spectrum when proposition utilizes a small amount of training sample of method reality of overcomplete sparse representation (Sparse Overcomplete Representations) to get final product high-resolution estimation clutter sky, and then the purpose of estimation clutter covariance matrix, improve the speed of convergence of estimating greatly, solved the problem that the IID sample lacks in the actual non-homogeneous clutter environment well.
Following article and patent documentation have covered the main background technology in this field substantially.In order to present the evolution of technology, allow their time sequencings arrange, and introduce the main contribution and the shortcoming of document one by one.
1、Haimovich?A.,Bar?Y.,An?eigenanalysis?interference?canceler,IEEETransactions?on?Signal?Processing?1992,39,1,76-84.
The clutter autocorrelation matrix of estimating is carried out characteristic value decomposition, think that the contribution of clutter is mainly by estimating some big components (clutter subspace) contribution in the clutter autocorrelation matrix, so with the orthogonal intersection space projection of signal to the clutter subspace, reaching the purpose of clutter reduction principal component, and this moment, needed IID training also was reduced to 2 times of clutter subspace sizes.Shortcoming is that not enough robust is revealed in the clutter subspace, in actual STAP system when having clutter internal motion and STAP systematic error poor-performing.
2、Guerci?J.R.,Theory?and?application?of?covariance?matrix?tapers?for?robustadaptive?beamforming,IEEE?Transactions?on?Signal?Processing,1999,47,4,977-985.
The phenomenon of revealing at the clutter subspace of causing such as factors such as clutter internal motion, channel errors that exists in the actual environment, adopt the method for autocorrelation matrix weighting that the influence of these factors is brought in the estimation of clutter covariance matrix, improved the robustness that the clutter subspace is revealed.Shortcoming is to utilize the prior estimate weighting of systematic error comparatively complicated in the reality.
3、Sarkar?T.,Wang?H.,Park?S.,Adve?R.,Koh?J.,Kim?K.,Zhang?Y.,Wicks?M.,BrownR.D.,A?deterministic?least?squares?approach?to?space-time?adaptive?processing(STAP),IEEE?Transactions?on?Antennas?Propagation,2001,49,1,91-103.
At the discrete noise source that only in detecting unit, occurs, the target component that may exist in the method filtering detecting unit that at first adopts empty time domain to offset, when the two dimension that does not contain target is empty, slide in the sampling afterwards, obtain a plurality of the sub-aperture training units that contain clutter, at last clutter in the sub-aperture of wave filter filtering of the low dimension of design.Shortcoming is to even clutter restraint relatively poor (the wave filter degree of freedom is low), secondly is that the acquisition hardware spending of subspace training sample is big.
4、Roman?J.,Rangaswamy?M.,Davis?D.,Zhang?Q.,Himed?B.,and?Michels?J.,Parametric?adaptive?matched?filter?for?airborne?radar?applications,IEEETransactions?on?Aerospace?and?Electronic?Systems.,2000,36,2,677-692.
Cascade time domain and spatial domain whitening filtering, wherein relativity of time domain AR (P) model formulation, parametric method significantly reduces required training sample, significantly accelerates speed of convergence, to containing comparatively robust of target in the training sample.Shortcoming is comparatively difficulty of AR (P) problem of determining the order, CFAR poor-performing simultaneously.
5、Goldstein?J.,Scharf?L.,and?Reed?I.,A?multistage?representation?of?the?Wienerfilter?based?on?orthogonal?projections,IEEE?Transactions?on?Information?Theory,1998,44,7,2943-2959.
Multistage wiener filter is split as multilevel hierarchy with the traditional integral Wiener filtering, all design every grade subspace and ask optimal filter in each level by maximizing simple crosscorrelation and minimizing MSE (square error), so just avoid asking the clutter autocorrelation matrix of desirable full rank, but progressively select basic filtering clutter component, each so remaining clutter degree of freedom is all reducing gradually, thereby has reduced the requirement of training sample.Shortcoming is that operand is big, complex structure.
6、Wicks?M.C.,Rangaswamy?M.,Adve?R.,and?Hale?T.B.,Space-time?adaptiveprocessing,IEEE?Signal?Processing?Magazine,2006,23,51-65.
Proposition is based on the STAP framework of knowledge, mainly be by the selective power of associating priori (as other sensing datas such as topomap, SAR images) raising to the IID training sample, simultaneously data are carried out prewhitening, reduce clutter residue degree of freedom, improve convergence speed.Shortcoming is that the additional system expense is big, and the difficult assurance of the accurate use of priori.
By can finding out at the summary and the comparison of actual non-homogeneous clutter environment STAP technology existing, existing STAP technology is by to the clutter modeling or utilize the priori prewhitening on the one hand, reduces the clutter degree of freedom to reach, and improves the purpose of convergence speed.But in fact priori obtains with accurately registration is all comparatively difficult.Be the selectivity of strengthening training sample on the other hand, reject nonuniform sample, to reduce such as the influence that exists target to detecting unit in the training sample.And existing non-homogeneous detecting device (NHD) also all needs the clutter covariance matrix pre-estimation, also can't avoid the clutter statistical information and estimate this problem, and if the clutter covariance of estimating have error, also can the judgement of non-homogeneous detecting device be exerted an influence.
Summary of the invention
After the in-depth analysis to the clutter echo model, we find the motion of airborne radar just, have coupling during land clutter empty, promptly
Wherein, f
dBe the space angle of the relative airborne radar of the static noise source in ground, θ
sBe the Doppler frequency that is had owing to airborne motion clutter, v is the speed of airborne platform, and λ is corresponding wavelength.Therefore at (f
d, θ
s) clutter distributes and mainly concentrates near the diagonal line zone on the space-time two-dimensional plane, so have near sparse property (main energy all concentrates on the diagonal line) in the spectrum when clutter two dimension is empty.And state theory with sparse sampling according to super complete base table, if in case signal has sparse property, distribute in the time of then just might only estimating that by a small amount of sample clutter is empty, and then under the situation that does not increase system overhead, spectrum when the super-resolution estimation clutter is empty so just provides new approaches for estimating clutter covariance matrix and reducing the clutter degree of freedom.
The sparse recovery that the present invention is based on super complete base is theoretical, with the fast beat of data (snapshot) of vectorization as input, distribute when utilizing the estimation clutter of the method super-resolution that overcomplete sparse recovers empty, and then realize that a small amount of sample estimates the purpose of clutter covariance matrix, effectively avoid traditional STAP method because discrete strong noise source, landform become soon, contain the influence of non-homogeneous clutter environments such as target in the training sample, improved clutter rejection under the non-homogeneous clutter environment significantly.
Space-time adaptive processing method under non-homogeneous clutter environment, this method step is as follows:
Step 1: spectrum is estimated when empty based on the clutter of sparse property
(1.1) single frames model (SMV)
At first STAP three-dimensional data (array element dimension N, M and distance dimension L are tieed up in pulse) is turned to x along distance to fast beat of data of extraction and with its vector, all the noise source echo summations on the expression respective distances unit, promptly the clutter echo model is:
Wherein dimension is the fast beat of data after the x of NM * 1 represents vectorization, Q
cRepresent separate noise source number, γ
i, θ
S, iAnd f
D, iRepresented i the pairing complex magnitude of noise source, space angle and Doppler frequency respectively, and φ (θ
S, i, f
D, i) guiding vector when the expression dimension is NM * 1 empty; To Doppler and space angle discretize, then (2) formula can be rewritten matrix form
Here N
s, N
dThe quantifying unit number (grid number) of angle and Doppler frequency is divided in representative respectively, wherein
Represent all noise sources pairing guiding set of vectors and corresponding noise source complex magnitude when empty respectively;
According to the theory that super complete base recovers and sparse sampling recovers, when finding the solution underdetermined equation, add the sparse constraint of priori, obtain separating of approximate actual distribution, i.e. the true space-time two-dimensional amplitude distribution of clutter scene, formula is as follows
Ideally degree of rarefication is defined as || ||
0Norm (being nonzero element number in the vector), use || ||
1Come approximate || ||
0The norm optimum solution; Owing to The noise, the ideal model in (3) formula is revised as in actual scene
x=Ψα+e, (7)
Wherein e represents noise, establishes || e||
2≤ ε, ε represents noise power; With || ||
1Norm is approached actual sparse solution, promptly
Wherein
Be the minimum of (7) formula || ||
1Norm is separated, α
0Be the desirable sparse solution that does not contain noise, (Q S) is stability parameter to Λ;
(1.2) multiframe model (MMV)
When having the independent identically distributed training sample of multiframe, introduce multiframe model (MMV) model:
X=[x
1,…x
r]=Ψ[α
1,…α
r] (9)
Wherein, r is the frame number (fast umber of beats) of multiframe model, and dimension is N
sN
dThe X of * r represents the fast beat of data after the multiframe vectorization, and dimension is NM * N
sN
d(NM<<N
sN
d) super complete basic Ψ guiding vector when covering all noise source correspondences empty, dimension is N
sN
d[the α of * r
1... α
r] represent multiframe clutter distribution estimating;
The observation data X that to dimension is NM * r is by randomly drawing to such an extent that single frames mixes observation x
Extract, former like this problem just is reduced to single frames model (SMV) promptly
x
extract=Ψα
extract (10)
Then utilize formula (6) to ask to mixed SMV model || ||
1The minimum corresponding sparse solution of norm
Utilize the similar priori of sparsity structure, the sparsity structure that the definable clutter distributes is
Wherein Γ represents
The subscript of nonzero element; Alternative sparsity structure has been arranged like this, former problem from owe to decide problem reduction be overdetermined problem (S≤r, wherein S=|| α here ||
0Degree of rarefication for desirable problem), solve promptly by minimizing variance
min||X-Ψ
Γα||
2 (12)
Spectrum diffusion influence when (1.3) empty
When having empty time error in (3) formula echo model be revised as
T wherein
S-i(θ
S, i, f
D, i) be corresponding empty time error weighting, further expand into
T wherein
s(θ
S, i, f
D, i) and t
t(θ
S, i, f
D, i) stochastic error of representing the inconsistent and clutter internal motion of passage to be caused respectively; Spectrum diffusion to some extent when clutter is empty, so desirable degree of rarefication || α ||
0(number of nonzero element) will become greatly, and we define relative degree of rarefication here, i.e. the number of remarkable component;
Step 2: wave filter when estimating clutter covariance matrix and design sky
In case obtain high-resolution clutter when empty spectrum distribute, the estimation formulas of clutter covariance further is reduced to
1≤p≤N wherein
sN
d, 1≤q≤N
sN
dRepresent the resolution element subscript, guiding vector when Ψ (p) represents p noise source correspondence empty; If it is uncorrelated mutually between each noise source,
The clutter covariance matrix of estimating like this is reduced to
Wherein on behalf of multiframe, E () ask expectation; If have only the single frames training sample, then utilize instantaneous sample to replace expectation (the multiframe scene can similarly be handled)
Because small component is estimated to have error in the sparse recovery of underdetermined equation, so need screen the subscript p in (18) here, ignores
The contribution that middle small component distributes to clutter; In order to improve the numerical stability of estimating autocorrelation matrix, it is carried out the diagonal angle load, promptly at last
Here β represents the size that the diagonal angle loads, and can provide according to the noise power of estimating in advance; So just obtained the estimation of alternative clutter autocorrelation matrix; Be brought into the optimal filter form at last
The present invention is directed to the IID training sample shortage problem that non-homogeneous clutter environment occurs, proposition utilizes the method super-resolution estimation clutter space-time two-dimensional spectrum (space-time spectrum) of overcomplete sparse representation, and then realization single frames training sample is estimated the purpose of clutter covariance matrix, the method can have been avoided effectively because of influences such as discrete noise source, fast change of landform, simultaneously to factors such as clutter internal motion and passage be inconsistent cause empty the time spectrum spread also comparatively robust.The Mountaintop real data has proved that the method can keep fine clutter rejection, is highly suitable for strong non-homogeneous clutter environment.
Description of drawings
Fig. 1 is that SATP receives three-dimensional data to clutter space-time two-dimensional spectrum conversion synoptic diagram.
Fig. 2 is an associating multiframe method of estimation process flow diagram.
Fig. 3 is a process flow diagram of the present invention.
Fig. 4 be the high-resolution clutter of CS-STAP and Capon when empty spectrum estimate synoptic diagram
High-resolution spectrum Capon and CS-STAP contrast when Fig. 5 is sky, Δ
ε=0.02, Δ
φ=2 °.
Fig. 6 is the response synoptic diagram of CS-STAP wave filter along space angle.
Fig. 7 is signal to noise ratio improvement factor (improvement factor) curve synoptic diagram of CS-STAP and DL.
Embodiment
Further specify the present invention below in conjunction with accompanying drawing.
Space-time adaptive processing method under non-homogeneous clutter environment, this method step is as follows:
Step 1: spectrum is estimated when empty based on the clutter of sparse property
● single frames model (SMV)
Fig. 1 receives three-dimensional data to clutter space-time two-dimensional spectrum conversion synoptic diagram for SATP.As shown in Figure 1, at first (array element is tieed up N with the STAP three-dimensional data among the figure, M and distance dimension L are tieed up in pulse) turn to x along distance to an extraction fast beat of data (snapshot) and with its vector, all the noise source echo summations on the expression respective distances unit, promptly the clutter echo model is
Wherein dimension is the fast beat of data after the x of NM * 1 represents vectorization, Q
cRepresent separate noise source number, γ
i, θ
S, iAnd f
D, iRepresented i the pairing complex magnitude of noise source, space angle and Doppler frequency respectively, and φ (θ
S, i, f
D, i) guiding vector when the expression dimension is NM * 1 empty.To Doppler and space angle discretize, then (2) formula can be rewritten matrix form
Here N
s, N
dThe quantifying unit number (grid number) of angle and Doppler frequency is divided in representative respectively, wherein
Ψ=[φ(1),…φ(N
s),…φ(N
sN
d)], (23)
Represent all noise sources pairing guiding set of vectors and corresponding noise source complex magnitude when empty (Ψ for example respectively
iGuiding vector when just representing i empty that noise source had, and α
iRepresent its corresponding complex magnitude).As previously mentioned, so coupling during just because of sky is actual noise source number Q
cCount N much smaller than the space-time two-dimensional plane lattice
sN
d, clutter space-time two-dimensional plane presents sparse property.As shown in Figure 1, in (3) formula, estimating that the clutter space-time two-dimension distributes just is equivalent at alternative known observation data x and find the solution the inverse problem of α among guiding set of vectors Ψ when empty, and since the purpose that high-resolution is estimated or super complete base table is stated, the dimension NM of matrix Ψ * N
sN
dIn usually choose NM<<N
sN
dSo this problem owes fixed, countless a plurality of separating are arranged usually.But because coupling during land clutter empty, the main energy of the empty time-frequency spectrum of desirable clutter two dimension only can distribute along diagonal line, whole relatively plane, frequency spectrum is comparatively sparse, be that the priori of the actual α of separating own has sparse property and (has only minority element non-zero in the α component in the equation (3), and other elements are all very little or approach 0), so according to the theory that super complete base recovers and sparse sampling recovers, when finding the solution underdetermined equation, add the sparse constraint of priori, just might obtain separating of approximate actual distribution, be the space-time two-dimensional amplitude distribution of true clutter scene, concrete formula is as follows
Ideally degree of rarefication is defined as || ||
0Norm (being nonzero element number in the vector), but || ||
0The computation optimization amount is huge, so use usually in the reality || ||
1Come approximate || ||
0The norm optimum solution, wherein ask || ||
1The norm minimum value is protruding optimization problem, and degree of stability is higher, about L
1/ L
0The condition and the proof of equivalence, see list of references [D.L.Donoho for details, M.Elad, and V.N.Temlyakov. " Stable recovery of sparse overcomplete representations in the presenceof noise, " IEEE Transactions on Information Theory, vol.52, no.1, pp.6-18, Jan.2006.], just deeply do not introduce here.Secondly, in actual scene, certainly exist The noise, so the ideal model in (3) formula need be revised as
x=Ψα+e, (26)
Wherein e represents noise, and miscellaneous noise ratio (CNR) is bigger in the general echo, so but here simple hypothesis be || e||
2≤ ε, ε represents noise power.Just because of the existence of noise e, original model can only be similar to sparse, and then has influence on the stability of restoration methods.Have The noise even people such as Donoho proved in 2005, use || ||
1Norm also can well be approached actual sparse solution, promptly
Wherein
Be the minimum of (7) formula || ||
1Norm is separated, α
0Be the desirable sparse solution that does not contain noise, (Q S) is stability parameter to Λ, maximum correlation Q is relevant between each row of it and priori degree of rarefication S and super complete basic Ψ, can find out under the sparse prerequisite of priori, from (8) formula if the degree of correlation is very little between the less and super complete base of noise level, then || ||
1The degree of stability of approaching actual optimum is still good.
● multiframe model (MMV)
Traditional overcomplete sparse representation method all is based on single frames model (SMV) basically, and when we had the independent identically distributed training sample of multiframe, we can further improve estimated performance by introducing multiframe model (MMV) model, and model is as follows
X=[x
1,…x
r]=Ψ[α
1,…α
r] (28)
Wherein r is the frame number (fast umber of beats) of multiframe model, and dimension is N
sN
dThe X of * r represents the fast beat of data after the multiframe vectorization, and dimension is NM * N
sN
d(NM<<N
sN
d) super complete basic Ψ guiding vector when covering all noise source correspondences empty, dimension is N
sN
d[the α of * r
1... α
r] represent multiframe clutter distribution estimating, though in general slightly rise and fall with the noise source amplitude between different samples that distributes, pairing when empty guiding vector (space angle and Doppler frequency) should remain unchanged in theory, promptly equation (9) separates α
i, i=1 ... the sparsity structure of r should unanimity (be α
iThe row of nonzero element number should be consistent or similar), the method of intuitively utilizing multiframe information is that the clutter of separately the fast beat of data of each range unit being done based on sparse property recovers, utilize multiframe to ask expectation E () afterwards, but so just not using multiframe separates the similar priori of sparsity structure, here we use for reference the multi-frame joint estimation model, make full use of priori α
i, i=1 ... the similar characteristic of sparsity structure between r improves estimation performance, and concrete multi-frame joint estimation approach flow process as shown in Figure 2.Fig. 2 is an associating multiframe method of estimation process flow diagram.At first, be that the observation data X of NM * r is by randomly drawing to such an extent that single frames mixes observation x to dimension
Extract, former like this problem just is reduced to single frames model (SMV) promptly
x
extract=Ψα
extract (29)
Then utilize formula (6) to ask to mixed SMV model || ||
1The minimum corresponding sparse solution of norm
Utilize the similar priori of sparsity structure, the sparsity structure that the definable clutter distributes is
Wherein Γ represents
The subscript of nonzero element.Alternative sparsity structure has been arranged like this, and we just can simplify corresponding super complete base (extracting some row of Γ correspondence among the super complete basic Ψ) greatly, former like this problem just can from owe to decide problem reduction be overdetermined problem (S≤r, wherein S=|| α here ||
0Degree of rarefication for desirable problem), solve preferably promptly by minimizing variance
min||X-Ψ
Γα||
2 (31)
● spectrum diffusion influence when empty
CS-STAP only uses seldom the IID training sample just can well estimate the clutter covariance battle array, thereby can avoid effectively that classic method needs that a large amount of training sample introduces such as discrete strong noise source, sample power is non-homogeneous, contain the influence of non-homogeneous clutter environments such as target in the training sample, even but in fact only use single sample to estimate clutter covariance matrix, still can exist because the problem that clutter internal motion (ICM) or channel characteristic inconsistent (Channel Mismatch) cause desirable clutter subspace to be revealed, when having empty time error in (3) formula echo model need be revised as
T wherein
S-t(θ
S, i, f
D, i) be corresponding empty time error weighting, can further expand into
T wherein
s(θ
S, i, f
D, i) and tx (θ
S, i, f
D, i) stochastic error of representing the inconsistent and clutter internal motion of passage to be caused respectively.Just because of the existence of empty time error, spectrum diffusion to some extent when causing clutter empty, so desirable degree of rarefication || α ||
0(number of nonzero element) will become big, similar and the scene of handling noise, here we define relative degree of rarefication, i.e. the remarkable number of component, and the The Realization of Simulation proof CS-STAP method by our back has robustness preferably to the echoed signal that has empty time error.
Step 2: wave filter when estimating clutter covariance matrix and design sky
In case we have obtained high-resolution clutter spectrum (the separating of equation (6) that distribute when empty
), the estimation formulas of clutter covariance can further be reduced to
1≤p≤N wherein
sN
d, 1≤q≤N
sN
dRepresent the resolution element subscript, guiding vector when Ψ (p) represents p noise source correspondence empty.Further can suppose between each noise source uncorrelated mutually
The clutter covariance matrix of estimating like this can be reduced to
Wherein on behalf of multiframe, E () ask expectation.If have only the single frames training sample, can utilize instantaneous sample to replace expectation (the multiframe scene can similarly be handled)
Here note,,,, ignore so need screen the subscript p in (18) here because small component is estimated to have error in the sparse recovery of underdetermined equation as last argumentation the in the step 1
The contribution that middle small component distributes to clutter.In order to improve the numerical stability of estimating autocorrelation matrix, it is carried out the diagonal angle load, promptly at last
Here β represents the size that the diagonal angle loads, and can provide according to the noise power of estimating in advance.We have just obtained the estimation of alternative clutter autocorrelation matrix like this.Be brought into the optimal filter form at last
Design corresponding when empty wave filter come clutter reduction component effectively.
Process flow diagram of the present invention as shown in Figure 3.As shown in Figure 3,
A) at first to the STAP three-dimensional data that receives along distance to the training sample x that extracts a snap
l, design the super complete basic Ψ that guides vector when covering empty that noise source had, bring formula (6) into, find the solution L
1The sparse solution of norm minimum value correspondence
It is clutter space-time two-dimensional spectrum.
B) with sparse solution
Be brought into the clutter covariance matrix that formula (18) can be estimated, diagonal line loads and improves numerical stability.
C) estimate the clutter diffusion according to priori (as average channel error, clutter internal motion speed etc.), to the clutter covariance matrix weighting of estimating.
D) according to the covariance matrix of estimating, calculate corresponding space-time adaptive wave filter, and do the judgement that moving-target has or not at output terminal.
We use the Mountaintop real data to verify based on sparse property clutter the validity of spectrum restoration methods when empty, the Mountaintop project is that MIT Lincoln laboratory is in order to verify the experimental project of STAP performance development, major parameter is 14 array elements, 16 pulses, distance is to 403 sampled points, and [http://spib.rice.edu/spib/mtn_top.html] seen in concrete detailed parameter introduction.The data scene of using concentrates on angle-15 degree as the main clutter district, the position of Doppler 156Bz, and true moving-target is positioned at apart from 152km, angle 15 degree, the position of Doppler 156Hz.
● step 1: spectrum is estimated when empty
At first the estimated performance of spectrum when verifying that clutter is empty uses the Capon spectrum as reference.Notice that the Capon spectrum used sufficient IID training sample (60 frame) to estimate noise performance, and based on sparse property clutter when empty spectrum recover CS-STAP and only use single frames information, find the solution sparse clutter according to (6) formula and distribute.Here we use the protruding majorized function bag cvx of people such as Boyd exploitation to realize || || 1 minimized finding the solution obtains among the cvx function Bao Kecong [http://www.stanford.edu/~boyd/cvx].Fig. 4 be the high-resolution clutter of CS-STAP and Capon when empty spectrum estimate synoptic diagram, X-axis is the wave beam territory among the figure, Y-axis is represented the Doppler territory, image intensity is represented the amplitude (the red more explanation amplitude of color is big more, and the blue more explanation amplitude of color is low more) of corresponding empty time-frequency spectrum.But described in (8) formula, because equation itself owes fixed, we can only guarantee to utilize || ||
1Norm is minimized separate and actual sparse solution between evaluated error can be retrained by noise level, be presented as to occur the pseudo-peaks of some small components in the estimated result, shown in Fig. 4 (b).Inevitably exist in the simultaneously actual STAP system such as clutter internal motion and the inconsistent factor of passage, thus here we also emulation testing CS-STAP have the stability that clutter recovers under the empty time error situation.Simulation parameter: the STAP system architecture is 8 of array numbers, 8 of umber of pulses, noise source number Q
c=60, and along the normalization space-time-frequency
Evenly distribute.Only consider channel error (influence of clutter internal motion is similar), random weighting is during empty in the formula (13)
Here we suppose ε
iAnd φ
iAll satisfy evenly distribution, represent relevant amplitude and phase error respectively, concrete form is as follows
Wherein
Similar range error is distributed as
Wherein
High-resolution spectrum Capon and CS-STAP contrast when Fig. 5 is sky, the channel error parameter is taken as Δ here
ε=0.02, Δ
φIn the time of=2 °, the Capon spectrum is selected 60 frame IID training samples for use, and CS-STAP only uses single frames.X-axis is represented Doppler among Fig. 5, Y-axis representation space angle.As can be seen from Figure, the Capon spectrum is because the frequency spectrum of channel error is even more serious along the diffusion of angle dimension, and the relative Capon of CS-STAP spectrum is to channel error robust more, and the energy distribution centrality is better, so estimate that by the high-resolution of CS-STAP the actual clutter that recovers of robust distributes.
● step 2: filtering performance when empty
Spectrum is estimated during according to high-resolution clutter sky
Construct corresponding wave filter according to formula (18)-(20), and the 152km frame data that Mountaintop contains moving-target are carried out filtering.Fig. 6 has provided the response of CS-STAP wave filter along space angle.In order to simulate the non-homogeneous influence that causes of clutter, the training sample of DL method is got the training sample (interval is far away more on the distance, and clutter distribution independent same distribution is difficult to guarantee more) apart from test sample book 7.5-12km and 13.5-18km place respectively here.X-axis is represented angle domain among the figure, the output power (dB of unit) when Y-axis is represented sky behind the wave filter.As can be seen from Figure, when training sample is non-homogeneous, need the DL method performance of multiframe IID training sample to descend greatly, cause the main clutter district clutter that is arranged in angle-15 degree to suppress poor ability (shown in last upper thread of figure and the medium line), and CS-STAP is owing to only need the single frames sample can realize the high-resolution estimation, avoided clutter influence heterogeneous to a great extent, so can form very dark clutter depression in actual main clutter district, clutter reduction makes the moving-target that is arranged in about 15 degree distinguish (shown in the figure trough minimum rate of accumulation) from clutter on every side simultaneously effectively.
● step 3: signal to noise ratio is improved performance
Keep above-mentioned test parameters constant, investigate signal to noise ratio improvement factor (improvement factor) curve of CS-STAP and DL, Fig. 7 is signal to noise ratio improvement factor (improvement factor) curve synoptic diagram of CS-STAP and DL.As shown in Figure 7, X-axis is represented Doppler frequency, and Y-axis is represented the signal to noise ratio improvement factor.Just as shown in FIG., the DL method is owing to lack enough IID training samples, can't accurately estimate noise performance, cause position and all obviously decline (shown in red line among the figure and purple line) of depth performance of IF curve clutter depression, and training unit distance detecting unit is far away more, noise performance is inconsistent more, signal to noise ratio is improved just poor more, but simultaneously, because CS-STAP only uses the single frames training sample just to estimate the clutter distribution and then accurately estimate the clutter covariance battle array by high-resolution, so greatly reduce the non-homogeneous influence that the STAP signal to noise ratio is improved of clutter environment, keep good signal to noise ratio to improve performance (shown in the top curve among the figure).
The present invention is directed to the IID training sample shortage problem that non-homogeneous clutter environment occurs, proposition utilizes the method super-resolution estimation clutter space-time two-dimensional spectrum (space-time spectrum) of overcomplete sparse representation, and then realization single frames training sample is estimated clutter covariance matrix, the present invention can avoid effectively because of influences such as discrete noise source, fast change of landform, simultaneously to factors such as clutter internal motion and passage be inconsistent cause empty the time spectrum spread also comparatively robust.Real data has proved that the method can keep fine clutter rejection, is applicable to strong non-homogeneous clutter environment.
Claims (2)
1. space-time adaptive processing method under non-homogeneous clutter environment is characterized in that, this method step is as follows:
Step 1: spectrum is estimated when empty based on the clutter of sparse property
(1.1) single frames model (SMV)
At first STAP three-dimensional data (array element dimension N, M and distance dimension L are tieed up in pulse) is turned to x along distance to fast beat of data of extraction and with its vector, all the noise source echo summations on the expression respective distances unit, promptly the clutter echo model is:
Wherein dimension is the fast beat of data after the x of NM * 1 represents vectorization, Q
cRepresent separate noise source number, γ
i, θ
S, iAnd f
D, iRepresented i the pairing complex magnitude of noise source, space angle and Doppler frequency respectively, and φ (θ
S, i, f
D, i) guiding vector when the expression dimension is NM * 1 empty; To Doppler and space angle discretize, then (2) formula can be rewritten matrix form
Here N
s, N
dThe quantifying unit number (grid number) of angle and Doppler frequency is divided in representative respectively, wherein
Ψ=[φ(1),…φ(N
s),…φ(N
sN
d)],(3)
Represent all noise sources pairing guiding set of vectors and corresponding noise source complex magnitude when empty respectively;
According to the theory that super complete base recovers and sparse sampling recovers, when finding the solution underdetermined equation, add the sparse constraint of priori, obtain separating of approximate actual distribution, i.e. the true space-time two-dimensional amplitude distribution of clutter scene, formula is as follows
Ideally degree of rarefication is defined as || ||
0Norm (being nonzero element number in the vector), use || ||
1Come approximate || ||
0The norm optimum solution; Owing to The noise, the ideal model in (3) formula is revised as in actual scene
x=Ψα+e,(6)
Wherein e represents noise, establishes || e||
2≤ ε, ε represents noise power; With || ||
1Norm is approached actual sparse solution, promptly
Wherein,
Be the minimum of (7) formula || ||
1Norm is separated, α
0Be the desirable sparse solution that does not contain noise, (Q S) is stability parameter to Λ;
Spectrum diffusion influence when (1.2) empty
When having empty time error in (3) formula echo model be revised as
T wherein
S-t(θ
S, i, f
D, i) be corresponding empty time error weighting, further expand into
T wherein
s(θ
S, i, f
D, i) and t
t(θ
S, i, f
D, i) stochastic error of representing the inconsistent and clutter internal motion of passage to be caused respectively; Spectrum diffusion to some extent when clutter is empty, so desirable degree of rarefication || α ||
0(number of nonzero element) will become greatly, and we define relative degree of rarefication here, i.e. the number of remarkable component;
Step 2: wave filter when estimating clutter covariance matrix and design sky
In case obtain high-resolution clutter when empty spectrum distribute, the estimation formulas of clutter covariance further is reduced to
1≤p≤N wherein
sN
d, 1≤q≤N
sN
dRepresent the resolution element subscript, guiding vector when Ψ (p) represents p noise source correspondence empty; If it is uncorrelated mutually between each noise source,
The clutter covariance matrix of estimating like this is reduced to
Wherein on behalf of multiframe, E () ask expectation; If have only the single frames training sample, then utilize instantaneous sample to replace expectation (the multiframe scene can similarly be handled)
Because small component is estimated to have error in the sparse recovery of underdetermined equation, so need screen the subscript p in (18) here, ignores
The contribution that middle small component distributes to clutter; In order to improve the numerical stability of estimating autocorrelation matrix, it is carried out the diagonal angle load, promptly at last
Here β represents the size that the diagonal angle loads, and can provide according to the noise power of estimating in advance; So just obtained the estimation of alternative clutter autocorrelation matrix; Be brought into the optimal filter form at last
2. space-time adaptive processing method under non-homogeneous clutter environment is characterized in that, this method step is as follows:
Step 1: spectrum is estimated when empty based on the clutter of sparse property
(1.1) multiframe model (MMV)
When having the independent identically distributed training sample of multiframe, introduce multiframe model (MMV) model:
X=[x
1,…x
r]=Ψ[α
1,…α
r](16)
Wherein, r is the frame number (fast umber of beats) of multiframe model, and dimension is N
sN
dThe X of * r represents the fast beat of data after the multiframe vectorization, and dimension is NM * N
sN
d(NM<<N
sN
d) super complete basic Ψ guiding vector when covering all noise source correspondences empty, dimension is N
sN
d[the α of * r
1... α
r] represent multiframe clutter distribution estimating;
The observation data X that to dimension is NM * r is by randomly drawing to such an extent that single frames mixes observation x
Extract, former like this problem just is reduced to single frames model (SMV) promptly
x
extract=Ψα
extract(17)
Then utilize following formula (6) to ask to mixed SMV model || ||
1The minimum corresponding sparse solution of norm
Utilize the similar priori of sparsity structure, the sparsity structure that the definable clutter distributes is
Wherein Γ represents
The subscript of nonzero element; Alternative sparsity structure has been arranged like this, former problem from owe to decide problem reduction be overdetermined problem (S≤r, wherein S=|| α here ||
0Degree of rarefication for desirable problem), solve promptly by minimizing variance
min||X-Ψ
Γα||
2(20)
Spectrum diffusion influence when (1.2) empty
When having empty time error in (3) formula echo model be revised as
T wherein
S-t(θ
S, i, f
D, i) be corresponding empty time error weighting, further expand into
T wherein
s(θ
S, i, f
D, i) and t
t(θ
S, i, f
D, i) stochastic error of representing the inconsistent and clutter internal motion of passage to be caused respectively; Spectrum diffusion to some extent when clutter is empty, so desirable degree of rarefication || α ||
0(number of nonzero element) will become greatly, and we define relative degree of rarefication here, i.e. the number of remarkable component;
Step 2: wave filter when estimating clutter covariance matrix and design sky
In case obtain high-resolution clutter when empty spectrum distribute, the estimation formulas of clutter covariance further is reduced to
1≤p≤N wherein
sN
d, 1≤q≤N
sN
dRepresent the resolution element subscript, guiding vector when Ψ (p) represents p noise source correspondence empty; If it is uncorrelated mutually between each noise source,
The clutter covariance matrix of estimating like this is reduced to
Wherein on behalf of multiframe, E () ask expectation; If have only the single frames training sample, then utilize instantaneous sample to replace expectation (the multiframe scene can similarly be handled)
Because small component is estimated to have error in the sparse recovery of underdetermined equation, so need screen the subscript p in (18) here, ignores
The contribution that middle small component distributes to clutter; In order to improve the numerical stability of estimating autocorrelation matrix, it is carried out the diagonal angle load, promptly at last
Here β represents the size that the diagonal angle loads, and can provide according to the noise power of estimating in advance; So just obtained the estimation of alternative clutter autocorrelation matrix; Be brought into the optimal filter form at last
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN 201010129723 CN101819269A (en) | 2010-03-19 | 2010-03-19 | Space-time adaptive processing method under non-homogeneous clutter environment |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN 201010129723 CN101819269A (en) | 2010-03-19 | 2010-03-19 | Space-time adaptive processing method under non-homogeneous clutter environment |
Publications (1)
Publication Number | Publication Date |
---|---|
CN101819269A true CN101819269A (en) | 2010-09-01 |
Family
ID=42654437
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN 201010129723 Pending CN101819269A (en) | 2010-03-19 | 2010-03-19 | Space-time adaptive processing method under non-homogeneous clutter environment |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN101819269A (en) |
Cited By (40)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN102231205A (en) * | 2011-06-24 | 2011-11-02 | 北京戎大时代科技有限公司 | Multimode monitoring device and method |
JP2012058234A (en) * | 2010-09-10 | 2012-03-22 | Mitsubishi Electric Research Laboratories Inc | Method for detecting target in radar signal using space-time adaptive processing |
CN102542126A (en) * | 2011-10-10 | 2012-07-04 | 上海交通大学 | Soft measurement method based on half supervision learning |
CN102544755A (en) * | 2011-12-31 | 2012-07-04 | 哈尔滨工业大学 | Uniform linear array calibration method based on strong scattering points |
CN102778669A (en) * | 2012-07-19 | 2012-11-14 | 北京理工大学 | Dimensionality reduction space-time adaptive processing method based on covariance matrix weighting |
CN102788970A (en) * | 2011-12-22 | 2012-11-21 | 河海大学 | High-efficiency self-adapting suppression method for short-range clutters of airborne digital array radar |
CN102866388A (en) * | 2012-06-28 | 2013-01-09 | 北京理工大学 | Iterative computation method for self-adaptive weight number in space time adaptive processing (STAP) |
CN102879767A (en) * | 2012-06-28 | 2013-01-16 | 北京理工大学 | Detection method of interference target for space-time adaptive processing |
CN103267964A (en) * | 2013-04-21 | 2013-08-28 | 北京航空航天大学 | Missile-borne seeker radar Sigma-Delta-STAP method based on low-rank matrix recovery |
CN103293517A (en) * | 2013-05-13 | 2013-09-11 | 西安电子科技大学 | Diagonal-loading robust adaptive radar beam forming method based on ridge parameter estimation |
CN103364764A (en) * | 2013-06-25 | 2013-10-23 | 西安电子科技大学 | Airborne radar non-stationary clutter suppression method |
CN103364770A (en) * | 2013-07-14 | 2013-10-23 | 西安电子科技大学 | Radar target detecting system based on matrix filling and detecting method thereof |
CN103412290A (en) * | 2013-08-06 | 2013-11-27 | 电子科技大学 | Knowledge-assisted APR non-uniform sample detection method |
CN104035078A (en) * | 2014-05-30 | 2014-09-10 | 北京理工大学 | Dimension reduction space-time adaptive weight calculation method based on array element order recursion |
CN104090271A (en) * | 2014-07-30 | 2014-10-08 | 广西大学 | Space time adaptive processing (STAP) method of high-speed radar at accelerated movement state |
CN104237883A (en) * | 2014-09-15 | 2014-12-24 | 西安电子科技大学 | Airborne radar space time self-adaptation processing method with sparse representation |
CN104363078A (en) * | 2014-12-02 | 2015-02-18 | 重庆邮电大学 | Underdetermined system real orthogonal space-time block code blind identification method based on robust competitive clustering |
CN104459657A (en) * | 2014-06-19 | 2015-03-25 | 西安电子科技大学 | Extension factorization space-time two-dimensional self-adaptive processing method based on data fitting |
CN104573341A (en) * | 2014-12-25 | 2015-04-29 | 中国科学院信息工程研究所 | Method and device for judging existence of character information in electromagnetic leakage signal |
CN104849707A (en) * | 2015-04-27 | 2015-08-19 | 中国电子科技集团公司第二十八研究所 | Sea clutter suppression method based on multi-radar distributed detection |
CN104931938A (en) * | 2015-05-07 | 2015-09-23 | 清华大学 | Coherent agile frequency radar clutter inhibition method and system |
CN105891793A (en) * | 2016-03-30 | 2016-08-24 | 中国人民解放军国防科学技术大学 | Radar seeker space-time correlation coherent K distribution clutter modeling method |
CN106053382A (en) * | 2016-06-23 | 2016-10-26 | 武智瑛 | Method and analyzer for analyzing and extracting characteristic contents of near-infrared small molecule trace gases |
CN106338723A (en) * | 2016-09-12 | 2017-01-18 | 深圳大学 | Space-time adaptive processing method based on co-prime pulse recurrence interval and apparatus thereof |
CN104459658B (en) * | 2014-06-19 | 2017-01-25 | 西安电子科技大学 | Unite-domain positioning space-time two-dimensional self-adaptive processing method based on data fitting |
CN106443620A (en) * | 2016-09-09 | 2017-02-22 | 深圳大学 | Sparse recovery STAP method based on array amplitude and phase error correction |
CN106546965A (en) * | 2016-10-31 | 2017-03-29 | 西安电子科技大学 | Based on radar amplitude and the space-time adaptive processing method of Doppler-frequency estimation |
CN106802408A (en) * | 2017-03-07 | 2017-06-06 | 中国人民解放军空军工程大学 | Airborne non-positive side battle array short range clutter range ambiguity suppressing method based on sparse recovery |
CN106872949A (en) * | 2017-02-21 | 2017-06-20 | 南京邮电大学 | A kind of clutter spectrum registration compensation method based on adaptive equalization loading |
WO2018045594A1 (en) * | 2016-09-12 | 2018-03-15 | 深圳大学 | Space-time adaptive processing method and apparatus based on co-prime pulse recurrence interval |
CN108566238A (en) * | 2018-01-23 | 2018-09-21 | 广东工业大学 | A kind of adaptive approach, system and the equipment of wave beam forming robustness |
CN110133603A (en) * | 2019-06-27 | 2019-08-16 | 哈尔滨工业大学 | High-frequency ground wave radar ocean clutter cancellation method based on rooting Euclidean geometry center of gravity |
CN110196412A (en) * | 2019-04-25 | 2019-09-03 | 电子科技大学 | A kind of STAP method of joint sparse |
CN110764069A (en) * | 2019-11-14 | 2020-02-07 | 内蒙古工业大学 | Sparse recovery STAP color loading method based on knowledge assistance |
CN111273249A (en) * | 2020-03-04 | 2020-06-12 | 清华大学 | Intelligent clutter partition method based on radar false alarm preprocessing time |
CN111650574A (en) * | 2020-06-24 | 2020-09-11 | 中国科学院声学研究所 | Underwater space-time self-adaptive processing method and system based on sparse recovery |
CN112906476A (en) * | 2021-01-20 | 2021-06-04 | 电子科技大学中山学院 | Airborne radar training sample selection method based on signal-to-noise-ratio loss |
CN113466797A (en) * | 2021-06-24 | 2021-10-01 | 电子科技大学 | Bistatic SAR space-time clutter suppression method based on clutter ridge matching sparse recovery |
CN114609626A (en) * | 2022-03-16 | 2022-06-10 | 电子科技大学 | Multi-target detection method for vehicle-mounted millimeter wave radar |
CN115575946A (en) * | 2022-10-20 | 2023-01-06 | 中国人民解放军海军航空大学 | Intelligent fusion detection method for radar target oblique symmetry subspace |
Citations (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO2000023818A1 (en) * | 1998-10-19 | 2000-04-27 | Raytheon Company | Efficient multi-resolution space-time adaptive processor |
CN101226236A (en) * | 2007-12-18 | 2008-07-23 | 西安电子科技大学 | Sky wave over-the-horizon radar self-adaption interference rejection method based on sidelobe constraint |
CN101561497A (en) * | 2009-05-22 | 2009-10-21 | 西安电子科技大学 | Airborne radar clutter suppression method |
-
2010
- 2010-03-19 CN CN 201010129723 patent/CN101819269A/en active Pending
Patent Citations (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO2000023818A1 (en) * | 1998-10-19 | 2000-04-27 | Raytheon Company | Efficient multi-resolution space-time adaptive processor |
EP1121607A1 (en) * | 1998-10-19 | 2001-08-08 | Raytheon Company | Efficient multi-resolution space-time adaptive processor |
US6292592B1 (en) * | 1998-10-19 | 2001-09-18 | Raytheon Company | Efficient multi-resolution space-time adaptive processor |
CN101226236A (en) * | 2007-12-18 | 2008-07-23 | 西安电子科技大学 | Sky wave over-the-horizon radar self-adaption interference rejection method based on sidelobe constraint |
CN101561497A (en) * | 2009-05-22 | 2009-10-21 | 西安电子科技大学 | Airborne radar clutter suppression method |
Non-Patent Citations (2)
Title |
---|
《电子学报》 19991130 黄勇,彭应宁,张瓅玶,张颢,王希勤 基于调频函数和遗传算法的非线性调频信号产生方法 77-79 1-2 第27卷, 第11期 2 * |
《自然科学进展》 20070131 吴洪,王永良,武文 非均匀杂波环境下线性预测类空时自适应处理性能分析 86-92 1-2 第17卷, 第1期 2 * |
Cited By (61)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JP2012058234A (en) * | 2010-09-10 | 2012-03-22 | Mitsubishi Electric Research Laboratories Inc | Method for detecting target in radar signal using space-time adaptive processing |
CN102231205A (en) * | 2011-06-24 | 2011-11-02 | 北京戎大时代科技有限公司 | Multimode monitoring device and method |
CN102542126B (en) * | 2011-10-10 | 2015-04-22 | 上海交通大学 | Soft measurement method based on half supervision learning |
CN102542126A (en) * | 2011-10-10 | 2012-07-04 | 上海交通大学 | Soft measurement method based on half supervision learning |
CN102788970A (en) * | 2011-12-22 | 2012-11-21 | 河海大学 | High-efficiency self-adapting suppression method for short-range clutters of airborne digital array radar |
CN102544755A (en) * | 2011-12-31 | 2012-07-04 | 哈尔滨工业大学 | Uniform linear array calibration method based on strong scattering points |
CN102544755B (en) * | 2011-12-31 | 2013-12-11 | 哈尔滨工业大学 | Uniform linear array calibration method based on strong scattering points |
CN102866388A (en) * | 2012-06-28 | 2013-01-09 | 北京理工大学 | Iterative computation method for self-adaptive weight number in space time adaptive processing (STAP) |
CN102879767A (en) * | 2012-06-28 | 2013-01-16 | 北京理工大学 | Detection method of interference target for space-time adaptive processing |
CN102879767B (en) * | 2012-06-28 | 2014-02-19 | 北京理工大学 | Detection method of interference target for space-time adaptive processing |
CN102778669A (en) * | 2012-07-19 | 2012-11-14 | 北京理工大学 | Dimensionality reduction space-time adaptive processing method based on covariance matrix weighting |
CN103267964A (en) * | 2013-04-21 | 2013-08-28 | 北京航空航天大学 | Missile-borne seeker radar Sigma-Delta-STAP method based on low-rank matrix recovery |
CN103293517A (en) * | 2013-05-13 | 2013-09-11 | 西安电子科技大学 | Diagonal-loading robust adaptive radar beam forming method based on ridge parameter estimation |
CN103293517B (en) * | 2013-05-13 | 2015-06-17 | 西安电子科技大学 | Diagonal-loading robust adaptive radar beam forming method based on ridge parameter estimation |
CN103364764A (en) * | 2013-06-25 | 2013-10-23 | 西安电子科技大学 | Airborne radar non-stationary clutter suppression method |
CN103364770A (en) * | 2013-07-14 | 2013-10-23 | 西安电子科技大学 | Radar target detecting system based on matrix filling and detecting method thereof |
CN103364770B (en) * | 2013-07-14 | 2015-04-22 | 西安电子科技大学 | Radar target detecting system based on matrix filling and detecting method thereof |
CN103412290A (en) * | 2013-08-06 | 2013-11-27 | 电子科技大学 | Knowledge-assisted APR non-uniform sample detection method |
CN103412290B (en) * | 2013-08-06 | 2015-01-21 | 电子科技大学 | Knowledge-assisted APR non-uniform sample detection method |
CN104035078A (en) * | 2014-05-30 | 2014-09-10 | 北京理工大学 | Dimension reduction space-time adaptive weight calculation method based on array element order recursion |
CN104459658B (en) * | 2014-06-19 | 2017-01-25 | 西安电子科技大学 | Unite-domain positioning space-time two-dimensional self-adaptive processing method based on data fitting |
CN104459657A (en) * | 2014-06-19 | 2015-03-25 | 西安电子科技大学 | Extension factorization space-time two-dimensional self-adaptive processing method based on data fitting |
CN104459657B (en) * | 2014-06-19 | 2017-01-25 | 西安电子科技大学 | Extension factorization space-time two-dimensional self-adaptive processing method based on data fitting |
CN104090271A (en) * | 2014-07-30 | 2014-10-08 | 广西大学 | Space time adaptive processing (STAP) method of high-speed radar at accelerated movement state |
CN104237883A (en) * | 2014-09-15 | 2014-12-24 | 西安电子科技大学 | Airborne radar space time self-adaptation processing method with sparse representation |
CN104237883B (en) * | 2014-09-15 | 2017-02-01 | 西安电子科技大学 | Airborne radar space time self-adaptation processing method with sparse representation |
CN104363078B (en) * | 2014-12-02 | 2017-07-18 | 重庆邮电大学 | The real orthogonal space time packet blind-identification method of under determined system based on robust Competition Clustering |
CN104363078A (en) * | 2014-12-02 | 2015-02-18 | 重庆邮电大学 | Underdetermined system real orthogonal space-time block code blind identification method based on robust competitive clustering |
CN104573341A (en) * | 2014-12-25 | 2015-04-29 | 中国科学院信息工程研究所 | Method and device for judging existence of character information in electromagnetic leakage signal |
CN104849707A (en) * | 2015-04-27 | 2015-08-19 | 中国电子科技集团公司第二十八研究所 | Sea clutter suppression method based on multi-radar distributed detection |
CN104849707B (en) * | 2015-04-27 | 2017-03-22 | 中国电子科技集团公司第二十八研究所 | Sea clutter suppression method based on multi-radar distributed detection |
CN104931938A (en) * | 2015-05-07 | 2015-09-23 | 清华大学 | Coherent agile frequency radar clutter inhibition method and system |
CN104931938B (en) * | 2015-05-07 | 2017-07-28 | 清华大学 | Coherent frequency-agile radar clutter suppression method and system |
CN105891793A (en) * | 2016-03-30 | 2016-08-24 | 中国人民解放军国防科学技术大学 | Radar seeker space-time correlation coherent K distribution clutter modeling method |
CN106053382A (en) * | 2016-06-23 | 2016-10-26 | 武智瑛 | Method and analyzer for analyzing and extracting characteristic contents of near-infrared small molecule trace gases |
CN106443620A (en) * | 2016-09-09 | 2017-02-22 | 深圳大学 | Sparse recovery STAP method based on array amplitude and phase error correction |
CN106338723B (en) * | 2016-09-12 | 2018-04-24 | 深圳大学 | A kind of space-time adaptive processing method and device based on relatively prime pulse recurrence interval |
WO2018045594A1 (en) * | 2016-09-12 | 2018-03-15 | 深圳大学 | Space-time adaptive processing method and apparatus based on co-prime pulse recurrence interval |
CN106338723A (en) * | 2016-09-12 | 2017-01-18 | 深圳大学 | Space-time adaptive processing method based on co-prime pulse recurrence interval and apparatus thereof |
CN106546965B (en) * | 2016-10-31 | 2019-05-21 | 西安电子科技大学 | Space-time adaptive processing method based on radar amplitude and Doppler-frequency estimation |
CN106546965A (en) * | 2016-10-31 | 2017-03-29 | 西安电子科技大学 | Based on radar amplitude and the space-time adaptive processing method of Doppler-frequency estimation |
CN106872949A (en) * | 2017-02-21 | 2017-06-20 | 南京邮电大学 | A kind of clutter spectrum registration compensation method based on adaptive equalization loading |
CN106802408A (en) * | 2017-03-07 | 2017-06-06 | 中国人民解放军空军工程大学 | Airborne non-positive side battle array short range clutter range ambiguity suppressing method based on sparse recovery |
CN106802408B (en) * | 2017-03-07 | 2020-05-26 | 中国人民解放军空军工程大学 | Airborne non-positive side array short range clutter distance fuzzy suppression method based on sparse recovery |
CN108566238A (en) * | 2018-01-23 | 2018-09-21 | 广东工业大学 | A kind of adaptive approach, system and the equipment of wave beam forming robustness |
CN108566238B (en) * | 2018-01-23 | 2021-07-06 | 广东工业大学 | Adaptive method, system and equipment for beamforming robustness |
CN110196412A (en) * | 2019-04-25 | 2019-09-03 | 电子科技大学 | A kind of STAP method of joint sparse |
CN110196412B (en) * | 2019-04-25 | 2023-04-25 | 电子科技大学 | STAP method combining sparsity |
CN110133603A (en) * | 2019-06-27 | 2019-08-16 | 哈尔滨工业大学 | High-frequency ground wave radar ocean clutter cancellation method based on rooting Euclidean geometry center of gravity |
CN110764069A (en) * | 2019-11-14 | 2020-02-07 | 内蒙古工业大学 | Sparse recovery STAP color loading method based on knowledge assistance |
CN110764069B (en) * | 2019-11-14 | 2021-08-10 | 内蒙古工业大学 | Sparse recovery STAP color loading method based on knowledge assistance |
CN111273249B (en) * | 2020-03-04 | 2022-07-08 | 清华大学 | Intelligent clutter partition method based on radar false alarm preprocessing time |
CN111273249A (en) * | 2020-03-04 | 2020-06-12 | 清华大学 | Intelligent clutter partition method based on radar false alarm preprocessing time |
CN111650574A (en) * | 2020-06-24 | 2020-09-11 | 中国科学院声学研究所 | Underwater space-time self-adaptive processing method and system based on sparse recovery |
CN111650574B (en) * | 2020-06-24 | 2022-12-23 | 中国科学院声学研究所 | Underwater space-time self-adaptive processing method and system based on sparse recovery |
CN112906476A (en) * | 2021-01-20 | 2021-06-04 | 电子科技大学中山学院 | Airborne radar training sample selection method based on signal-to-noise-ratio loss |
CN113466797A (en) * | 2021-06-24 | 2021-10-01 | 电子科技大学 | Bistatic SAR space-time clutter suppression method based on clutter ridge matching sparse recovery |
CN113466797B (en) * | 2021-06-24 | 2023-04-28 | 电子科技大学 | Double-base SAR space-time clutter suppression method based on clutter ridge matching sparse recovery |
CN114609626A (en) * | 2022-03-16 | 2022-06-10 | 电子科技大学 | Multi-target detection method for vehicle-mounted millimeter wave radar |
CN115575946A (en) * | 2022-10-20 | 2023-01-06 | 中国人民解放军海军航空大学 | Intelligent fusion detection method for radar target oblique symmetry subspace |
CN115575946B (en) * | 2022-10-20 | 2024-06-04 | 中国人民解放军海军航空大学 | Intelligent fusion detection method for radar target oblique symmetry subspace |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN101819269A (en) | Space-time adaptive processing method under non-homogeneous clutter environment | |
CN100385253C (en) | High-resolution radon transform for processing seismic data | |
CN103593669B (en) | A kind of method that polarimetric synthetic aperture radar image four component decomposes | |
Wang et al. | Maximum likelihood estimation of compound-Gaussian clutter and target parameters | |
CN104237883A (en) | Airborne radar space time self-adaptation processing method with sparse representation | |
CN104931931A (en) | Bistatic multiple-input and multiple-output (MIMO) radar angle estimation method based on tensor absolute-value subspace in cross-coupling condition | |
Albert et al. | Model equations for waves in stratified fluids | |
CN100460893C (en) | Method for analyzing noise elimination of earthquake based on independent components in Pearson system | |
CN103901416B (en) | A kind of multichannel clutter suppression method based on steadiness factor method | |
CN113567985A (en) | Inverse synthetic aperture radar imaging method, device, electronic equipment and storage medium | |
CN106468770A (en) | Closely optimum radar target detection method under K Distribution Clutter plus noise | |
CN106680815A (en) | Tensor-sparse-representation-based MIMO radar's imaging method | |
CN105117736A (en) | Polarized SAR image classification method based on sparse depth stack network | |
CN105652273A (en) | MIMO (Multiple Input Multiple Output) radar sparse imaging algorithm based on hybrid matching pursuit algorithm | |
CN109683126A (en) | Direction of arrival measurement method, signal handling equipment and storage medium | |
CN108008386A (en) | A kind of distance based on single snap MUSIC algorithms is to processing method | |
CN107907855A (en) | A kind of coprime array switching DOA estimation method and device for even linear array | |
CN104865556A (en) | MIMO radar system DOA estimation method based on real domain weighting minimization l1-norm method | |
CN105005035A (en) | Target detection method based on two-dimensional sliding window robust space-time self-adaptive processing | |
CN109058089A (en) | A method of the vacuum pump overload fault detection based on acoustic emission signal | |
CN104833952B (en) | It is a kind of determine it is multiple when frequency aliasing signal step-out time method | |
Wang et al. | An overview of parametric modeling and methods for radar target detection with limited data | |
CN102866425A (en) | Blind source seismic signal stable-superposition model based blind separation method | |
Liu et al. | $\ell _ {0} $-Norm Minimization-Based Robust Matrix Completion Approach for MIMO Radar Target Localization | |
Zhu et al. | Robust shallow water reverberation reduction methods based on low-rank and sparsity decomposition |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
C06 | Publication | ||
PB01 | Publication | ||
C10 | Entry into substantive examination | ||
SE01 | Entry into force of request for substantive examination | ||
C02 | Deemed withdrawal of patent application after publication (patent law 2001) | ||
WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20100901 |