CN110297247A - Weather radar wind power plant clutter suppression method based on the sparse recovery of low-rank matrix - Google Patents
Weather radar wind power plant clutter suppression method based on the sparse recovery of low-rank matrix Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/88—Radar or analogous systems specially adapted for specific applications
- G01S13/95—Radar or analogous systems specially adapted for specific applications for meteorological use
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
- G01S7/36—Means for anti-jamming, e.g. ECCM, i.e. electronic counter-counter measures
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- G—PHYSICS
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- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
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- G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
- G01S7/414—Discriminating targets with respect to background clutter
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Abstract
The invention discloses a kind of weather radar wind power plant clutter suppression methods based on the sparse recovery of low-rank matrix, this method utilizes the spatial coherence of meteorologic signal, it first will simultaneously include the distance unit zero setting of meteorologic signal and wind turbine clutter, and in the distance unit two sides to weighing 40 distance unit, then distance vector is reconstructed into the low-rank snap matrix for meeting neutral element random distribution by Pulse by Pulse, is finally minimized nuclear norm using non-precision augmented vector approach (IALM) and is effectively restored meteorological data.The simulation experiment result shows that the present invention can effectively inhibit wind turbine clutter (WTC) and noise, improves meteorologic signal output signal-to-noise ratio, is suitble to engineer application.
Description
Technical field
The present invention relates to a kind of weather radar wind power plant clutter suppression methods based on the sparse recovery of low-rank matrix, belong to gas
As radar clutter inhibits field.
Background technique
To cope with the potential collision hazard of the energy and the deterioration of the ecological environments such as global warming, countries in the world all active development can
Clean energy resource is regenerated, wind-power electricity generation receives global highest attention as a kind of important form of renewable and clean energy resource.
In recent years, the wind power plant scale in global range and the positive grow exponentially of quantity, the revolving speed and length of wind turbine blade are continuous
Increase, but studies have shown that wind power plant wind turbine moves clutter to radar, communication and navigation due to caused by blade high speed rotation
Equal electronic equipments, which can generate, to be seriously affected, and brings new challenge to all kinds of Radar Targets'Detections, and existing clutter recognition skill
Art can not effectively filter out wind turbine clutter (WTC), produce serious influence to the precision of prediction of weather information, therefore
Wind turbine clutter has become the key problem of current weather radar clutter recognition.
Existing Clutter Rejection Technique such as time-domain filtering method, frequency domain filtering method, the filtering method based on power spectrum characteristic
The video stretching as caused by the high speed rotation of wind turbine leads to not effectively press down so that weather information loss is serious
WTC processed greatly affected the precision of prediction of weather information.European and American scientists are in detailed analysis weather radar different operating mould
Formula apparatus for lower wind turbine clutter and weather echo when, after frequency domain distribution characteristic, being restored based on the Multiquadric method of proposition,
The wind turbines Clutter suppression algorithms such as range-Doppler spectrum returns, the sparse reconstruct of recurrence, are also turned by wind power plant scale, blower
The physical conditions such as speed, weather radar operating mode limitation, above-mentioned algorithm can not combine wind turbine clutter recognition with
Weather information Distortionless.Furthermore tradition WTC suppressing method only individually handles the data of each distance unit respectively, without
Utilize the information of other distance unit.And noise signal cannot be effectively inhibited.
Summary of the invention
Technical problem to be solved by the invention is to provide a kind of weather radar wind power plant based on bidimensional joint interpolation is miscellaneous
Sparse optimum theory is introduced weather radar WTC and inhibited, studies the weather radar Miniature wind based on matrix completion by wave suppressing method
Electric field clutter suppression method, wherein two important research direction one of of the matrix completion theoretical (MC) as sparse Renew theory,
It can evade the problem of causing weather information to lack in above-mentioned several method, the meteorological letter that the completion of high-precision matrix is interfered by WTC
Number, improve the precision of prediction of weather information.
The present invention uses following technical scheme to solve above-mentioned technical problem:
The present invention provides the weather radar wind power plant clutter suppression method based on the sparse recovery of low-rank matrix, the tool of this method
Steps are as follows for body:
Step 1, meteorological radar echo signal is inputted, wherein input signal under i-th of distance unit, m-th of pulse are as follows:
xi(m)=si(m)+ci(m)+wi(m)+ni(m)
In formula, i=1 ..., L, L are distance unit number, m=1 ..., M, and M is coherent accumulation umber of pulse, si(m)、ci
(m)、wi(m) and ni(m) it is respectively meteorologic signal, land clutter signal, wind turbine under i-th of distance unit, m-th of pulse
Clutter WTC signal and noise signal；
Step 2, the low-rank snap matrix building of stochastical sampling, specifically:
40 distance unit are respectively taken i-th of distance unit two sides, and by the echo-signal [x in i-th of distance uniti
(1),xi(2),..,xi(M)] zero setting obtains observing matrix XL×M:
By XL×MThe low-rank snap matrix of stochastical sampling is constructed, constructs criterion are as follows: gradually by observing matrix XL×MM-th
Vector [x under pulse1(m),x2(m),...,xL(m)]TIt is built into snap matrixWherein, m1And m2Respectively indicate snap
The line number and columns of matrix, m1×m2=L, m1=m2, the element of the pth row of snap matrix, q column
Echo-signal xi(m) the low-rank snap matrix X constructed are as follows:
Meteorologic signal s is defined by Xi(m) the low-rank snap matrix S constructed after i-th of distance unit zero setting are as follows:
Land clutter signal c is defined by Xi(m) the low-rank snap Matrix C constructed after i-th of distance unit zero setting are as follows:
Noise signal n is defined by Xi(m) the low-rank snap matrix N constructed after i-th of distance unit zero setting are as follows:
WTC signal wi(m) the low-rank snap matrix W constructed is null matrix；
Step 3, restore the meteorologic signal after inhibition WTC by matrix completion model:
Wherein, min () indicates minimum processing, | | | |*Indicate nuclear norm, PΩExpression projects to only in index set Ω
Mapping on the sparse matrix subspace of non-zero, it makes member of the matrix in Ω constant, first zero setting other than Ω,
Step 4, using non-precision augmented vector approach IALM solution matrix completion model, after output inhibits WTC
Signal.
As further technical solution of the present invention, Lagrangian in step 4 are as follows:
Wherein, Y=Y0+ μ (X-S-N-W) is Lagrange multiplier matrix；Y0For Lagrange multiplier matrix initial value, value
It is 0；μ is penalty factor, | | | |FIndicate F norm, | | | |FIndicate F norm,Tr () expression takes
The mark of matrix,Expression takes real,<, the inner product of>representing matrix.
As further technical solution of the present invention, the step of solution using non-precision augmented vector approach IALM
Are as follows:
The step of being solved using non-precision augmented vector approach IALM are as follows:
1) Y is enabled0=0, W0=0, N=0, μ0> 0, ρ > 1, k=0, η=10-3, wherein W0=0 indicates the wind for needing to inhibit
The initial value of power turbine clutter；
2) formula (U, Σ, V are utilizedH)=svd (X-Nk-Wk+μk -1Yk) andUpdate S:Wherein Sk+1And SkRespectively indicate the kth+1 and k update of meteorologic signal S, WkIt indicates
The kth time of WTC signal W updates, NkIndicate that the kth time of noise N updates, YkIndicate that the kth time of Lagrange multiplier matrix Y updates,
μkIndicate that the kth time of penalty factor μ updates；
3) W is updated:WhereinIndicate the index set other than Ω；
4)
5) Y is updatedk: Yk+1=Yk+μk(X-Sk+1-Nk+1-Wk+1)；
6) μ is updatedkTo μk+1: μk+1=ρ μk；
If 7) following formula is invalid, algorithm is not converged, enables k ← k+1, goes to step 2, otherwise goes to step 8:
||Sk-Sk-1||F/||Sk||F≤η；
8) end loop, output:
As further technical solution of the present invention, echo under each pulse recovered after completion is gradually output using IALM
The snap matrix of signalThe first row and last line in each matrix are successively extracted, by first row and finally
A line constitutes the vector that M L × 1 is tieed upThen the vector of above-mentioned M L × 1 dimension is formed into a M
The echo-signal of × L dimension restores matrixIt takesIn the i-th row vector as dilute
Dredge the meteorologic signal of i-th of the distance unit recovered
Beneficial effect
The present invention can effectively restore meteorological data using IALM algorithm, it is suppressed that wind turbine clutter improves back
The signal-to-noise ratio of wave signal, practical, operand is smaller, has good future in engineering applications.
Detailed description of the invention
Fig. 1 is signal processing flow figure of the present invention；
Fig. 2 is snap matrix eigenvalue distribution figure；
Fig. 3 is to interfere matrix completion front and back meteorologic signal power spectrum under meteorologic signal, noise jamming without making an uproar；
Fig. 4 is to interfere matrix completion front and back meteorologic signal amplitude under meteorologic signal, noise jamming without making an uproar；
Fig. 5 is signal restoration errors curve graph under different signal-to-noise ratio；
Fig. 6 is that IALM algorithm restores front and back meteorologic signal velocity amplitude.
Specific embodiment
Technical solution of the present invention is described in further detail with reference to the accompanying drawing:
The present invention mainly studies the weather radar wind power plant clutter matrix completion restrainable algorithms based on snap matrix, and Fig. 1 is
Method process flow.Its key step is as follows:
Step 1: the modeling of meteorological radar echo signal, specifically:
It mainly includes meteorologic signal, land clutter signal and noise signal that weather radar, which receives signal,.It is assumed that i-th of distance is single
Member includes simultaneously WTC signal, and input signal is denoted as under m-th of pulse:
xi(m)=si(m)+ci(m)+wi(m)+ni(m), m=1 ..., M (1)
Wherein, siIt (m) is meteorologic signal, ciIt (m) is land clutter signal, wiIt (m) is WTC signal, niIt (m) is noise signal,
M is coherent accumulation umber of pulse, can use M=64；
Step 2: the low-rank snap matrix of stochastical sampling constructs, specifically:
40 distance unit are respectively taken i-th of distance unit two sides, and by the echo-signal [x in i-th of distance uniti
(1),xi(2),..,xi(M)] zero setting can obtain observing matrix XL×M
Wherein XL×MDimension be L × M, L be distance unit number, can use L=81, M is umber of pulse；
By XL×MLow-rank stochastical sampling snap matrix is constructed, criterion is constructed are as follows:
Gradually by observing matrix XL×MVector [x under m-th of pulse1(m),x2(m),...,xL(m)]TIt is built into snap square
Battle arraym1,m2Meet m1×m2=L, m1=m2=9, respectively indicate the row and column number of snap matrix.Snap matrix pth row,
The element k of q columnp,qIt indicates, i.e.,MeetEcho-signal xi(m)
The low-rank snap matrix X of building are as follows:
Meteorologic signal s is defined by Xi(m) the low-rank snap matrix S constructed after i-th of distance unit zero setting are as follows:
Land clutter c is defined by Xi(m) the low-rank snap Matrix C constructed after i-th of distance unit zero setting are as follows:
Noise n is defined by Xi(m) the low-rank snap matrix N constructed after i-th of distance unit zero setting are as follows:
Known WTC exists only in i-th of distance unit, i.e., WTC signal is W in observing matrixL×M:
To WL×MAfter signal zero setting in i-th of distance unit, WTC signal wi(m) the low-rank snap matrix W constructed is
Null matrix, i.e. W=0.
Step 3: the sparse recovery meteorologic signal of low-rank matrix, specifically:
Matrix completion model can realize the benefit to unknown element by constraint order minimization problem according to part matrix element
Entirely, after WTC being rejected, meteorological data can be restored by following matrix completion model and inhibits WTC:
Wherein min () indicates minimum processing, | | | |FIndicate F norm:Tr () is indicated
Take the mark of matrix, PΩIt indicates to project to the mapping only on the sparse matrix subspace of index set Ω non-zero, it makes matrix exist
Member in Ω is constant, and first zero setting other than Ω is formulated as follows:
Utilize the optimization problem in non-precision augmented vector approach (IALM) solution above formula (2), classical matrix
Completion algorithm is only applicable to real matrix, and matrix constructed by this paper is complex matrix, and classical matrix completion algorithm is expanded to complex field,
Deserved LagrangianL (S, W, N, Y, μ) can be indicated are as follows:
Wherein Y=Y0+ μ (X-S-N-W) is Lagrange multiplier matrix, Y0For initial value, value 0, μ > 0 indicate punishment because
Son, | | | |*It indicates nuclear norm, is the sum of all singular values of matrix,Expression takes real, < X, Y >=tr
(XHY) the inner product of representing matrix.
A contraction operator is defined first are as follows:
IALM algorithm steps are as follows:
Input: XijObservation sample, (i, j) ∈ Ω, matrix X ∈ Rm×n
1) Y is enabled0=0, W0=0, N=0, μ0> 0, ρ > 1, k=0, η=10-3, wherein W0=0 indicates the wind for needing to inhibit
The initial value of power turbine clutter, value 0；
2) when not meeting convergence formula in 9), using 3), 4) solvingWherein
SkIndicate that the kth time of meteorologic signal S updates, WkIndicate that the kth time of WTC signal W updates, NkIndicate that the kth time of noise N updates, Yk
Indicate that the kth time of Lagrange multiplier matrix Y updates, μkIndicate that the kth time of penalty factor μ updates；
3)(U,Σ,VH)=svd (X-Nk-Wk+μk -1Yk)；
4)
5) W is updated:WhereinIndicate the index set other than Ω；
6)
7) Y is updatedk: Yk+1=Yk+μk(X-Sk+1-Nk+1-Wk+1)；
8) μ is updatedkTo μk+1: μk+1=ρ μk；
If 9) following formula is invalid, algorithm is not converged, enables k ← k+1, goes to step 2, otherwise goes to step 10:
||Sk-Sk-1||F/||Sk||F≤η；
10) end loop, output:
The snap matrix of echo-signal under each pulse recovered after completion is gradually output using IALM iterative algorithmK is the number of iterations, first row and last line in each matrix is successively extracted, by first row and last
Row constitutes the vector that M L × 1 is tieed upThen vector M L × 1 tieed up forms one
The echo-signal of M × L dimension restores matrix
Take matrixIn the i-th row vector are as follows:
WhereinFor the meteorologic signal of sparse i-th of the distance unit recovered.
Algorithm is tested below by MATLAB, validation matrix completion algorithm restores the validity of meteorologic signal, thunder
It is as shown in table 1 up to system emulation parameter；Assuming that echo-signal is present in the 1st to the 100th distance unit, 64 pulses are shared,
There are WTC for the 25th distance unit of sampling matrix, handle its zero setting, the vector under each pulse is successively configured to snap square
Battle array.
1 snap matrix simulation parameter of table
| Pulse recurrence frequency | 1000Hz |
| Carrier frequency | 5.5GHz |
| Radar altitude | 1000m |
| Signal-to-noise ratio | 30dB |
| Umber of pulse | 64 |
| Distance unit number | 100 |
| Matrix line number | 10 |
| Matrix columns | 10 |
Known snap matrix is the matrix of 10 rows, 10 column, and the matrix after completion is rearranged for L × 1 and ties up matrix, successively
63 pulse signals of residue are emulated, the 25th vector element constitutes returning after WTC inhibits under each pulse extracted
Wave signal.To WTC and noise suppression effect, Fig. 3 gives addition different capacity and makes an uproar understanding inventive algorithm for clarity
In the case of sound, the doppler spectral of meteorologic signal, the ground noise as caused by noise and flaw indication reconstructed error is in matrix completion
Inhibit to fluctuate before WTC larger.Simulation result is shown in Fig. 3, in the case where Signal to Noise Ratio (SNR)=30dB, before and after matrix completion
Meteorologic signal centre frequency is about 360Hz, and when close to centre frequency, the influence of noise jamming is very faint, and meteorologic signal restores essence
Du Genggao.Peak side-lobe has certain inhibiting effect to high interference noise, and noise power reduces about 5~10dB.Doppler frequency
fdNoise reduces 15~20dB when=300Hz, 400Hz, and inhibitory effect is best.
Meteorologic signal amplitude is as shown in figure 4, the variation feelings of the signal amplitude before and after matrix completion can be clearly showed that out
Condition.The amplitude restored by matrix completion percent error compared with original value is 1.2412e-002%.For quantitative analysis MC calculation
The restorability of method is defined as follows root-mean-square error as performance indicator:
The root-mean-square error of the meteorologic signal amplitude based on the building completion of snap matrix can be obtained according to the emulation data of Fig. 4
RMSE=1.71e-2 defines variance Var to measure the dispersion degree for inhibiting front and back signal
Matrix completion inhibits the variance Var1=7.1107e-005 of meteorologic signal amplitude under preceding time domain, after inhibition under time domain
The variance Var2=1.3303e-005 of meteorologic signal amplitude, it can be seen that dispersion degree substantially reduces, the deviation journey with true value
Degree reduces, and fluctuation caused by noise jamming reduces, and signal-to-noise ratio is changed and improved.Simulation result, data analysis shows, the algorithm
The performance for substantially increasing matrix completion realizes the Exact recovery of meteorologic signal while inhibiting WTC.
In order to analyze influence of the input signal signal-to-noise ratio to this algorithm performance, the signal under different signal-to-noise ratio is restored to miss
Difference draws out curve graph as shown in Figure 5, and wherein signal-to-noise ratio variation range is 0dB~30dB, it can be seen from the figure that missing number
It is reduced according to the root-mean-square error for carrying out matrix completion with the increase of the signal-to-noise ratio of input signal, the signal-to-noise ratio of input signal is got over
Height, when carrying out singular value decomposition, influence of the noise factor to singular value decomposition is smaller, and it is higher that matrix completion restores precision.
Fig. 6 is the value that IALM algorithm restores front and back meteorologic signal speed, the meteorologic signal speed after recovery are as follows:Wherein ∠ is to take phase,Indicate the single order of echo samples sequence from phase
Parameter is closed, whereinFor the conjugate transposition of the meteorologic signal after recovery.Radial velocity estimation under low signal-to-noise ratio fluctuation compared with
Greatly, there are relatively large deviations with true value.But with the increase of signal-to-noise ratio, the error of radial velocity estimation is gradually reduced, final to restrain
To true value.
The invention rejects interference data first with WTC detection algorithm, secondly the spatial coherence of foundation meteorologic signal,
The low-rank completion matrix for meeting absent element random distribution is constructed, and constraint order minimum is solved by IALM iterative algorithm and is asked
Topic effectively restores meteorological data.The simulation experiment result shows that the algorithm can effectively inhibit WTC and noise jamming, improves echo letter
Number signal-to-noise ratio has good future in engineering applications.
Claims (4)
1. the weather radar wind power plant clutter suppression method based on the sparse recovery of low-rank matrix, which is characterized in that the tool of this method
Steps are as follows for body:
Step 1, meteorological radar echo signal is inputted, wherein input signal under i-th of distance unit, m-th of pulse are as follows:
xi(m)=si(m)+ci(m)+wi(m)+ni(m)
In formula, i=1 ..., L, L are distance unit number, m=1 ..., M, and M is coherent accumulation umber of pulse, si(m)、ci(m)、wi
(m) and ni(m) it is respectively meteorologic signal, land clutter signal, wind turbine clutter under i-th of distance unit, m-th of pulse
WTC signal and noise signal；
Step 2, the low-rank snap matrix building of stochastical sampling, specifically:
40 distance unit are respectively taken i-th of distance unit two sides, and by the echo-signal [x in i-th of distance uniti(1),xi
(2),..,xi(M)] zero setting obtains observing matrix XL×M:
By XL×MThe low-rank snap matrix of stochastical sampling is constructed, constructs criterion are as follows: gradually by observing matrix XL×MUnder m-th of pulse
Vector [x1(m),x2(m),...,xL(m)]TIt is built into snap matrixWherein, m1And m2Respectively indicate snap matrix
Line number and columns, m1×m2=L, m1=m2, the element of the pth row of snap matrix, q column
Echo-signal xi(m) the low-rank snap matrix X constructed are as follows:
Meteorologic signal s is defined by Xi(m) the low-rank snap matrix S constructed after i-th of distance unit zero setting are as follows:
Land clutter signal c is defined by Xi(m) the low-rank snap Matrix C constructed after i-th of distance unit zero setting are as follows:
Noise signal n is defined by Xi(m) the low-rank snap matrix N constructed after i-th of distance unit zero setting are as follows:
WTC signal wi(m) the low-rank snap matrix W constructed is null matrix；
Step 3, restore the meteorologic signal after inhibition WTC by matrix completion model:
Wherein, min () indicates minimum processing, | | | |*Indicate nuclear norm, PΩExpression projects to only in index set Ω non-zero
Sparse matrix subspace on mapping, it makes member of the matrix in Ω constant, first zero setting other than Ω,
Step 4, using non-precision augmented vector approach IALM solution matrix completion model, output inhibits the letter after WTC
Number.
2. the weather radar wind power plant clutter suppression method according to claim 1 based on the sparse recovery of low-rank matrix,
It is characterized in that, Lagrangian in step 4 are as follows:
Wherein, Y=Y0+ μ (X-S-N-W) is Lagrange multiplier matrix；Y0For Lagrange multiplier matrix initial value, value 0；μ
For penalty factor, | | | |FIndicate F norm, | | | |FIndicate F norm,Tr () expression takes matrix
Mark,Expression takes real,<, the inner product of>representing matrix.
3. the weather radar wind power plant clutter suppression method according to claim 2 based on the sparse recovery of low-rank matrix,
The step of being characterized in that, being solved using non-precision augmented vector approach IALM are as follows:
1) Y is enabled0=0, W0=0, N=0, μ0> 0, ρ > 1, k=0, η=10-3, wherein W0=0 indicates the wind-force whirlpool for needing to inhibit
The initial value of turbine clutter；
2) formula (U, Σ, V are utilizedH)=svd (X-Nk-Wk+μk -1Yk) andUpdate S:Wherein Sk+1And SkRespectively indicate the kth+1 and k update of meteorologic signal S, WkTable
Show that the kth time of WTC signal W updates, NkIndicate that the kth time of noise N updates, YkIndicate the kth time of Lagrange multiplier matrix Y more
Newly, μkIndicate that the kth time of penalty factor μ updates；
3) W is updated:WhereinIndicate the index set other than Ω；
4)
5) Y is updatedk: Yk+1=Yk+μk(X-Sk+1-Nk+1-Wk+1)；
6) μ is updatedkTo μk+1: μk+1=ρ μk；
If 7) following formula is invalid, algorithm is not converged, enables k ← k+1, goes to step 2, otherwise goes to step 8:
||Sk-Sk-1||F/||Sk||F≤η；
8) end loop, output:
4. the weather radar wind power plant clutter suppression method according to claim 3 based on the sparse recovery of low-rank matrix,
It is characterized in that, the snap matrix of echo-signal under each pulse recovered after completion is gradually output using IALM
The first row and last line in each matrix are successively extracted, first row and last line are constituted into the vector that M L × 1 is tieed upThen the echo-signal that the vector of above-mentioned M L × 1 dimension forms M × L dimension is restored into matrixIt takesIn the i-th row vector as sparse i-th of the distance unit recovered
Meteorologic signal
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| CN111239699A (en) * | 2020-03-17 | 2020-06-05 | 河海大学 | Meteorological radar wind power plant clutter suppression method based on incremental extreme learning machine |
| CN111273238A (en) * | 2020-01-06 | 2020-06-12 | 中国航天科工集团八五一一研究所 | SAR (synthetic aperture radar) wide-band and narrow-band interference simultaneous inhibition method based on low-rank recovery |
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| CN112379380A (en) * | 2020-10-29 | 2021-02-19 | 河海大学 | Wind power plant clutter suppression method based on mean value method reprocessing truncation matrix completion |
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