CN107135026A - Robust ada- ptive beamformer method based on matrix reconstruction in the presence of unknown mutual coupling - Google Patents
Robust ada- ptive beamformer method based on matrix reconstruction in the presence of unknown mutual coupling Download PDFInfo
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Abstract
The invention discloses a kind of robust ada- ptive beamformer method under unknown mutual coupling information state.The present invention is primarily based on the particularity of array mutual coupling structure, and reconstruct includes the interference plus noise covariance matrix of mutual coupling informationAnd Eigenvalues Decomposition is carried out to it, the corresponding characteristic vector of dominant eigenvalue is taken, the subspace U for only including interference information is constitutediAnd its orthogonal complement space；Mapped by the way that signal will be received to the orthogonal complement space, can obtain the reception signal y (n) only comprising desired signal and noise signal, and obtain its sample covariance matrixIt is rightFeature decomposition is carried out, the corresponding characteristic vector of eigenvalue of maximum is taken, construction only includes the signal subspace u of desired signal informations；Utilize usIt is present in the characteristic of same sub-spaces with real desired signal steering vector, it is optimal weight vector w when can obtain existing mutual coupling to carry out simple operationopt.The present invention without knowing mutual coupling specifying information, then can obtain accurate weight vector when there is array element mutual coupling.
Description
Technical field
The present invention relates to the beam-forming technology of adaptive array signal process field, particularly directed to even linear array in battle array
There is the robust ada- ptive beamformer method under mutual coupling, but specific mutual coupling information unknown situation in row.
Background technology
In array signal processing, commonly using Beam-former.The effect of Beam-former is, by changing wave filter
Weights, make the signal of assigned direction pass through wave filter.Assuming that desired signal incides Homogeneous linear array from angle, θ, do not consider
Receiver noise, then array received signal can be expressed as：
X (n)=a (θ) s (n) (1)
Wherein a (θ)=[1, e-jφ,…,e-j(M-1)φ]TIt is the steering vector of signal, the π dsin θ of φ=2/λ, e is that nature is normal
Number, j is imaginary unit, and M is element number of array, and s (n) is the complex envelope of n reception signals.
The weight vector of wave filter is expressed as：
W=[w0,w1,…,wM-1`]T (2)
Wave filter is output as：
U (n)=wHX (n)=wHa(θ)s(n) (3)
From formula (3), if making w meet w=a (θ), u (n)=wHA (θ) s (n)=a (θ)HA (θ) s (n)=Fs (n),
That is the signal in θ directions is exaggerated F times by wave filter.
If considering a N-dimensional far field narrow band signal, it is assumed that desired signal incident angle is θ0, K from different directions
Independent interference signal, its incident angle is respectively θk, k=1,2 ..., K.Ideally, the reception signal x (n) at n moment is：
X (n)=A (θ) s (n)+e (n), n=1,2 ..., N (4)
Wherein A (θ)=[a (θ0),a(θ1),…,a(θK)] it is the array steering vector matrix that size is M × (K+1)；s
(n)=[s0(n),s1(n),…,sK(n)]T, n=1,2 ..., N is the complex envelope of n time-ofday signals；E (n), n=1,2 ..., N is
Zero-mean, variance isNoise vector.And the steering vector of desired signal is a (θ0), the complex envelope of desired signal is s0
(n).And assume mutually independent between desired signal, interference and noise.
Most classical beamforming algorithm has MVDR Estimation of Spatial Spectrum methods, and its thought is to ensure that desired signal is undistorted logical
While crossing spatial filter, selection weight vector w make it that the average output power P (θ) of spatial filter is minimum, i.e., to its other party
To signal and noise all as far as possible suppress.Such a constrained extremal problem is described as：
Wherein RxTo receive the spatial correlation matrix of signal：
Rx=E { x (n) xH(n)} (6)
The optimal weight vector that MVDR Beam-formers can be obtained by solving above formula is：
Average output powerFor：
WillSpatial domain after the possible arrival bearing of desired signal is eliminated is integrated, and the interference reconstructed, which adds, makes an uproar
Sound covariance matrixFor
Wherein, spatial domain Θ is the set that desired signal is possible to arrival bearing, andIt is Θ in whole signal space
Mend.By the interference plus noise covariance matrix of reconstructIt is updated in MVDR beamforming algorithms, can obtains based on MVDR spectrums
Covariance matrix reconstruction steady beamforming algorithm (RAB-Rec, Robust Adaptive the Beamforming of of estimation
Reconstruction optimal weight vector)：
Another common algorithms are beamforming algorithm (ESB, the Eigen-Subspace of feature based subspace
Beamforming) (see document：L.Chang,C.C.Yeh.Performance of DMI and eigenspace-based
beamformers[J].IEEE Transactions on Antennas and Propagation,1992,40(11),Page
(s):1336-1347.), to the spatial correlation matrix R of arrayxFeature decomposition is carried out, is obtained：
Wherein,It is RxM characteristic value, σn 2It is noise power.
Use RxThe corresponding characteristic vector e of the big characteristic value of preceding K+11,…,eK+1Signal subspace S is opened into, E is designated ass=[e1,…,
eK+1], then by the optimal weight vector of MVDR Beam-formersWhereinTo
Signal subspace EsUpper projection, obtains the principal component (ESB-PC, Principal Component) of feature based subspace method
The optimal weight vector of beamforming algorithm：
Diagonal loading (DL, Diagonal Loading) beamforming algorithm is (see document：
B.D.Carlson.Covariance matrix estimation errors and diagonal loading in
adaptive arrays[J].IEEE Transactions on Aerospace and Electronic Systems,
1988,24(4),Page(s):397-401) it is another classic algorithm, is usually used in solving that sample covariance matrix is unusual asks
Topic.Following optimization problem can be modeled as：
σL 2For loading level, I is unit matrix.Using Lagrange Multiplier Methods, can solve best initial weights is：
The value of loading level is very crucial, optimal loading level be influenceed by desired signal and interference signal quantity and
Dynamic change, select loading level according to different purposes in actual applications.
Due to receiving the spatial correlation matrix R of signalxTypically it is difficult to obtain, therefore in actual applications, with reception signal
Sample covariance matrixTo substitute：
When array has mutual coupling, the real steering vector in θ directions should be (see document：Estimation of Spatial Spectrum theory and algorithm,
Wang Yongliang；Beijing, publishing house of Tsing-Hua University, 2004, Page (s):418-419)：
Array steering vector matrix is then accordingly：
Wherein, Z is the mutual coupling matrix for containing mutual coupling information.Because mutual coupling effect is inversely proportional with array element spacing, and very
Easily obtained according to principle of reciprocity, mutual coupling matrix Z is a symmetrical matrix.The mutual coupling matrix of even linear array is as follows：
Wherein ci(i=1 ..., M-1) it is the mutual coupling coefficient.Therefore the receipt signal model in the presence of mutual coupling should be：
Observe the weight vector expression formula of MVDR algorithmsRAB-Rec algorithmsESB algorithmsAnd DL algorithmsWhen array has mutual coupling, due to all directly having used the desired signal of mismatch to lead
To vector a (θ0), the performance of these algorithms can all decline.Wherein the weight vector of MVDR algorithms is in a (θ0) mismatch when, expect letter
When number power is stronger, it may appear that the repressed situation of desired signal；A (θ of the RAB-Rec algorithms due to having used mismatch0) carry out
Interference noise covariance matrix is reconstructed so that reconstructNo longer accurately include interference information, it is impossible to effectively suppress interference.
The content of the invention
The present invention proposes a kind of sane wave beam shape under unknown mutual coupling information state for there is array element mutual coupling
Into method.The present invention is without known mutual coupling information, it is only necessary to carry out Eigenvalues Decomposition, it is possible to obtain optimal power by calculating
Vector.
The present invention is primarily based on the particularity of array mutual coupling structure, and reconstruct includes the interference-plus-noise covariance of mutual coupling information
MatrixThen it is rightEigenvalues Decomposition is carried out, the corresponding characteristic vector of dominant eigenvalue is taken, constituted only comprising interference information
Subspace UiAnd its orthogonal complement space；Mapped, obtained only comprising expectation letter to the orthogonal complement space by the way that signal will be received
Number and noise signal reception signal y (n), and obtain its sample covariance matrixIt is rightFeature decomposition is carried out, maximum is taken
The corresponding characteristic vector of characteristic value, construction only includes the signal subspace u of desired signal informations；Utilize usExpect to believe with real
Number steering vector is present in the characteristic in same space, and it is optimal weight vector when can obtain existing mutual coupling to carry out simple operation
wopt.This method can Fast Convergent, be a kind of method of closely optimal beam forming.
The technical solution adopted in the present invention is：The complex envelope of the information containing mutual coupling is tried to achieve first, and revaluation is corresponding
Signal power, chooses signal power more than the signal at the angle of predetermined threshold value, the accurate interference for including mutual coupling information of reconstruct
Plus noise covariance matrix；Interference plus noise covariance matrix to the reconstruct carries out Eigenvalues Decomposition, takes dominant eigenvalue correspondence
The characteristic vector construction interference signals subspace and its orthogonal complement space；Signal will be received to project to the orthogonal complement space, obtained
Feature decomposition is carried out containing only desired signal and the signal of noise, and to its sample covariance matrix, takes eigenvalue of maximum corresponding
Characteristic vector composition desired signal subspace, and construct weight vector.Comprise the following steps：
a：By spatial domain angular region where interference signalIt is divided into the angle at L points, l-th of point at equal intervals to be designated asL=1,
2,…,L.According to formulaCalculate M × Q dimension matrixesL=1,2 ..., L, wherein Q are mutual coupling
Coefficient non-zero number,Respectively：
b：Utilize formulaL=1,2 ..., L, which is calculated, to be present mutually
The estimator of complex envelope during coupling, whereinFor the reception sample of signal association comprising mutual coupling information
Variance matrix；And according to formulaL=1,2 ..., the power of L revaluation signalsTo powerCarry out
Ascending order is arranged, and obtains sequenceSince first element of sequence, search and meetFirst sequence location value rq, wherein predetermined coefficient β span is：β≥2；
c：Utilize formulaReconstruct interference noise association side
Poor matrixWhereinFor the reception sample of signal covariance matrix comprising mutual coupling information；
d：To matrixSingular value decomposition is carried out, interference signals subspace is obtained for Ui, and obtain UiThe orthogonal complement spaceWherein I is unit matrix；
Further according to formulaObtain the reception signal y (n) containing only desired signal and noise；
e：Use formulaConstruct y (n) sample covariance matrixAnd it is rightCarry out singular value
Decompose, take the corresponding characteristic vector composition subspace u of eigenvalue of maximums；
f：Utilize formulaTry to achieve weight vector wopt, complete Wave beam forming.
Beneficial effects of the present invention are, when there is array element mutual coupling, without knowing mutual coupling specifying information, only reconstruct and include
The interference plus noise covariance matrix of mutual coupling information, and carry out feature decomposition twice, it is possible to obtain accurate weight vector.
Brief description of the drawings
Fig. 1 is：The beam pattern comparison diagram of even linear array algorithms of different when array has mutual coupling；
Fig. 2 is：The output SINR of algorithms of different is with the change comparison diagram for inputting SNR when array has mutual coupling；
Fig. 3 is：When there is mutual coupling in array the output SINR of algorithms of different with fast umber of beats N change comparison diagram.
Embodiment
To make the object, technical solutions and advantages of the present invention clearer, with reference to embodiment and accompanying drawing, to this hair
It is bright to be described in further detail.
When Wave beam forming is handled, when there is mutual coupling, the steering vector in θ directionsIt is represented by：The mutual coupling coefficient non-zero number is represented with Q, then non-zero the mutual coupling coefficient vector c=[1, c1,c2,…,cQ-1]T, M
× Q dimension matrix T [θ] construction be：T [θ]=T1[θ]+T2[θ], M represents array number.Wherein：
Symbol { }m,nThe m rows n-th of representing matrix arrange corresponding element, []m+n-1[]m-n+1Represent respectively to
The m+n-1 element and the m-n+1 element of amount.a(θi) represent on incident angle θiSteering vector, then there is mutual coupling
When receive data can be expressed as again：
WhereinReception complex envelope comprising mutual coupling information
When desired signal and interference signal and orthogonal noise, signal covariance matrix is receivedFor：
WhereinFor complex envelopeAutocorrelation matrix.
When mutual coupling information is unknown, interference plus noise when there is mutual coupling for the structural form according to (23) formula to reconstruct
Covariance matrix, it is only necessary to solve unknown vectorAnd its autocorrelation matrix
To solveUsing minimum cost functionMethod：
| | represent the 2- norms of vector.The W that solves in formula (24) andIt is multiple a weight matrix and vector respectively.Exhibition
OpenIt can obtain：
Minimum formula (25) can be obtainedEstimator be：
Optimal multiple weight matrix is：
In the hope of the reception complex envelope comprising mutual coupling informationFor：
The arrival bearing of all incoming signals is unknown during due to actual treatment, in order to utilize the covariance square of formula (23)
The structural form of battle array, accurate reconstruction is carried out to interference noise covariance matrix, of the invention by the spatial domain where interference signal incoming wave
Angular regionIt is divided into the angle at L points, l-th of point at equal intervals to be designated asL=1,2 ..., L.Therefore, above-mentioned minimum generation is utilized
Valency functionMethod, estimation l-th of angle at complex envelope be
WhereinFor the reception sample of signal covariance matrix comprising mutual coupling information：
Its power is estimated as：
Ascending order arrangement is carried out to estimation power, is designated as：It is to meet following formula to make rq
Minimum value：Wherein β is default constant coefficient, and preferred value is set to 2.
After above-mentioned processing, interference noise covariance matrix is reconstructed, while only containing noise power to abandon
Angle direction, using formula (23) reconstruct when, only meet conditionAngle be just included into reconstruct scope,
It is reconstructed into:
WhereinRl=rq, rq+1 ..., rL,For interference signal angle sector that may be present.So reconstruct and
'sAccurately contain mutual coupling information and interference signal arrival bearing's information.
Obtaining accurately reconstructing interference plus noise covariance matrixAfterwards, it is rightFeature decomposition is carried out, main feature is taken
It is worth corresponding characteristic vector, constitutes subspace Ui, it is clear that subspace UiOnly include interference signal.U can further be obtainedi's
The orthogonal complement space is：
Using the reception signal expression (19) when there is array mutual coupling, after allowing it by subspace D, obtain only including the phase
Prestige signal and the reception signal y (n) of noise signal information are：
Wherein a ' (θ0)=DZa (θ0)。
Y (n) autocorrelation matrix is：
Ry=E (y (n) y (n)H), n=1,2 ..., N (35)
In practice, y (n) autocorrelation matrix RySubstituted with its sample covariance matrix：
It is rightEigenvalues Decomposition is carried out, the corresponding characteristic vector of eigenvalue of maximum is taken, desired signal subspace is obtained for us。
a′(θ0) in usRow subspace in.Isospace wave filter is the same, in order that the signal for obtaining desired signal direction passes through,
Weighting vector is：
Wave filter output is designated as u, then has：
Then optimal weight vector is：
Most only poor constant multiples between the weight vector and real weight vector so tried to achieve, therefore have no effect on ripple
The performance of beam formation.
In specific implementation, the present invention is that specific implementation step is as follows：
S1：By spatial domain angular region where interference signalIt is divided into the angle at L points, l-th of point at equal intervals to be designated asUtilize
Formula (20), (21), calculating matrixL=1,2 ... L；
S2：Angle is calculated using formula (29)-(31)Signal power at l=1,2 ... L, and ascending order row is carried out to it
Row, obtain reconstructing dry noise covariance matrix using formula (32)
S3：To matrixSingular value decomposition is carried out, it is U to take dominant eigenvalue to constitute interference signals subspacei；
S4：The reception signal y (n) only comprising desired signal and noise information is constructed with formula (34), and y is constructed with formula (36)
(n) sample covariance matrixSingular value decomposition is carried out, the corresponding characteristic vector composition of eigenvalue of maximum is taken only comprising expectation
The subspace u of signals；
S5：Weight vector w is tried to achieve using formula (39)opt, complete Wave beam forming.
In order to verify the present invention Beamforming Method can in the case of array element mutual coupling and unknown mutual coupling specifying information,
Good Wave beam forming performance can be kept, to traditional robust ada- ptive beamformer algorithm (MVDR algorithms, RAB-Rec algorithms, ESB algorithms
And DL algorithms) and Beamforming Method proposed by the present invention (Proposed) carried out simulation comparison, the wave beam shape of contrast
Performance indications into method are：Wave beam forming figure and output Signal to Interference plus Noise Ratio (SINR).
Simulation parameter：12 yuan of even linear arrays.Desired signal arrival bearing is -1 °.Because bearing estimate is inaccurate, it is assumed that
The desired signal incidence angle known is 5 °.Desired signal is that may be present interval for Θ=[- 7 °, 7 °].Two dry to make an uproar than for 20dB
Interference incide array from -30 ° and 60 ° of directions respectively.Each signal is separate, and separate with noise.The mutual coupling coefficient
Non-zero number Q is that the mutual coupling coefficient vector between 3, array element is：[1,0.6237+j*0.3875,0.3658+j*0.2316,
zeros(1,M-Q)]。
Emulation experiment 1：Fast umber of beats is 100, desired signal signal to noise ratio (SNR, Signal to Interference
Ratio) it is 5dB.As seen from Figure 1, MVDR algorithms and DL algorithms are due to steering vector mismatch, although formed in interference radiating way
Null, is suppressed but it is desirable to signal arrival bearing also form null, i.e. desired signal.RAB-Rec algorithms are due to being oriented to
The mismatch of vector, so that main lobe direction is not aligned with the true arrival bearing of desired signal, and because uses the steering vector of mismatch
Reconstruct interference plus noise covariance matrix so that the matrix of reconstruct does not completely include interference information, therefore does not have in interference radiating way
To form null.Although ESB algorithm main lobes targeted by real desired signal arrival bearing, zero is not formed in interference radiating way
Fall into, that is, disturb without suppressed.Institute's extracting method (Proposed in corresponding diagram 1) only of the present invention, both expected letter real
Number arrival bearing forms main lobe, and forms null in interference radiating way.And be mainly the reason for cause this phenomenon traditional
Beamforming algorithm does not consider steering vector mismatch caused by mutual coupling.
Emulation experiment 2：Fast umber of beats remains as 100.The signal to noise ratio excursion of desired signal is that -5dB arrives 35dB.Emulation knot
Fruit is drawn based on 500 Monte Carlo Experiments.The experiment is primarily to influences of the checking input SNR to algorithm performance.Fig. 2 gives
Curve maps of all method output SINR with input SNR changes is gone out.Wherein optimal output SINR is appeared in as judgment criteria
In figure (opt in Fig. 2).As shown in Figure 2 it can be found that the performance of institute's extracting method of the present invention (Proposed in corresponding diagram 2)
It is closest to optimal beam forming.Although RAB-Rec algorithms and DL algorithms output SINR increase with input SNR increase
Greatly, but their performance is much worse than institute's extracting method.ESB algorithms export SINR and declined on the contrary in input SNR increase.MVDR
Algorithm is with input SNR increase, and output SINR does not improve, and performance is worse compared to for other method.Because
RAB-Rec algorithms and ESB algorithms are without effectively suppression interference, and MVDR algorithms and DL algorithms fail to come in real desired signal
Ripple direction forms main lobe.
Emulation experiment 3：Desired signal signal to noise ratio snr is 5dB.Fast umber of beats excursion is 20 times to 200 times.Simulation result
500 Monte Carlo Experiments are also based on to draw.The experiment is primarily to research convergence of algorithm speed, i.e., each algorithm performance
Situation about changing with fast umber of beats.Fig. 3 gives change curves of the output SINR with fast umber of beats N.Institute's extracting method (correspondence of the present invention
Proposed in Fig. 3) performance is closest to optimal beam forming (opt in Fig. 3), and convergence rate is very fast.Although RAB-
Also quickly, but output SINR still will be much worse than institute's extracting method for Rec algorithms, ESB algorithms and DL algorithm the convergence speed.MVDR
Algorithm performance is all poorer than other method.
As fully visible, institute's extracting method of the present invention is that one kind also can effectively solve array element in the case of unknown mutual coupling specifying information
The method of mutual coupling problem.
Claims (2)
1. the robust ada- ptive beamformer method based on matrix reconstruction in the presence of unknown mutual coupling, it is characterised in that comprise the following steps：
a：By spatial domain angular region where interference signalIt is divided into the angle at L points, l-th of point at equal intervals and is designated as θl, l=1,2 ...,
L；According to formula T [θl]=T1[θl]+T2[θl] calculate M × Q dimension matrix T [θl], l=1,2 ..., L, wherein Q are that the mutual coupling coefficient is non-
Zero number, T1[θl]、T2[θl] be respectively：
b：Utilize formulaCalculate when there is mutual coupling
The estimator of complex envelope, whereinFor the reception sample of signal covariance square comprising mutual coupling information
Battle array；And according to formulaThe power of revaluation signalTo powerCarry out ascending order row
Row, obtain sequenceSince first element of sequence, search and meet
The value rq of one sequence location, wherein predetermined coefficient β span is：β≥2；
c：Utilize formulaReconstruct interference noise covariance matrixWhereinFor the reception sample of signal covariance matrix comprising mutual coupling information；
d：To matrixSingular value decomposition is carried out, interference signals subspace is obtained for Ui, and obtain UiThe orthogonal complement spaceWherein I is unit matrix；
Further according to formulaObtain the reception signal y (n) containing only desired signal and noise；
e：Use formulaConstruct y (n) sample covariance matrixAnd it is rightCarry out singular value decomposition,
Take the corresponding characteristic vector composition subspace u of eigenvalue of maximums；
f：Utilize formulaTry to achieve weight vector wopt, complete Wave beam forming.
2. the method as described in claim 1, it is characterised in that the preferred value of factor beta is 2.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109639332A (en) * | 2019-02-28 | 2019-04-16 | 电子科技大学 | A kind of steady beam forming optimization method based on steering vector model |
| CN111988077A (en) * | 2020-08-20 | 2020-11-24 | 中国人民解放军空军工程大学 | Information processing method, information processing device, electronic equipment and storage medium |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1504022A (en) * | 2001-04-24 | 2004-06-09 | 阿雷伊通讯有限公司 | Spatial processing and timing estimation using training sequence in radio communications system |
| CN103630910A (en) * | 2013-12-13 | 2014-03-12 | 武汉大学 | Anti-interference method of GNSS (global navigation satellite system) receiver equipment |
| EP3016430A1 (en) * | 2013-03-28 | 2016-05-04 | LG Electronics Inc. | Method and apparatus for acquiring channel state information in antenna array |
| US20160315686A1 (en) * | 2015-04-23 | 2016-10-27 | Electronics And Telecommunications Research Institute | Antenna apparatus and method for beam forming thereof |
-
2017
- 2017-04-11 CN CN201710231899.1A patent/CN107135026B/en not_active Expired - Fee Related
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1504022A (en) * | 2001-04-24 | 2004-06-09 | 阿雷伊通讯有限公司 | Spatial processing and timing estimation using training sequence in radio communications system |
| EP3016430A1 (en) * | 2013-03-28 | 2016-05-04 | LG Electronics Inc. | Method and apparatus for acquiring channel state information in antenna array |
| CN103630910A (en) * | 2013-12-13 | 2014-03-12 | 武汉大学 | Anti-interference method of GNSS (global navigation satellite system) receiver equipment |
| US20160315686A1 (en) * | 2015-04-23 | 2016-10-27 | Electronics And Telecommunications Research Institute | Antenna apparatus and method for beam forming thereof |
Non-Patent Citations (2)
| Title |
|---|
| CHUN-HUNG LIN等: ""Joint Direction Finding and Propagation Delay"", 《2010 IEEE 71ST VEHICULAR TECHNOLOGY CONFERENCE》 * |
| GUO YING等: ""Robust direction finding for uniform circular array with mutual coupling"", 《2005 IEEE ANTENNAS AND PROPAGATION SOCIETY INTERNATIONAL SYMPOSIUM》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109639332A (en) * | 2019-02-28 | 2019-04-16 | 电子科技大学 | A kind of steady beam forming optimization method based on steering vector model |
| CN109639332B (en) * | 2019-02-28 | 2020-06-09 | 电子科技大学 | Steady wave beam forming optimization method based on guide vector model |
| CN111988077A (en) * | 2020-08-20 | 2020-11-24 | 中国人民解放军空军工程大学 | Information processing method, information processing device, electronic equipment and storage medium |
| CN111988077B (en) * | 2020-08-20 | 2023-01-31 | 中国人民解放军空军工程大学 | Information processing method, information processing device, electronic equipment and storage medium |
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