CN107342836A - Weighting sparse constraint robust ada- ptive beamformer method and device under impulsive noise - Google Patents
Weighting sparse constraint robust ada- ptive beamformer method and device under impulsive noise Download PDFInfo
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04J—MULTIPLEX COMMUNICATION
- H04J11/00—Orthogonal multiplex systems, e.g. using WALSH codes
- H04J11/0023—Interference mitigation or co-ordination
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B7/00—Radio transmission systems, i.e. using radiation field
- H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
- H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
- H04B7/0408—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas using two or more beams, i.e. beam diversity
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B7/00—Radio transmission systems, i.e. using radiation field
- H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
- H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
- H04B7/0413—MIMO systems
- H04B7/0456—Selection of precoding matrices or codebooks, e.g. using matrices antenna weighting
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04J—MULTIPLEX COMMUNICATION
- H04J11/00—Orthogonal multiplex systems, e.g. using WALSH codes
- H04J11/0023—Interference mitigation or co-ordination
- H04J11/0026—Interference mitigation or co-ordination of multi-user interference
- H04J11/003—Interference mitigation or co-ordination of multi-user interference at the transmitter
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Abstract
The present invention is applied to communication technique field, there is provided the weighting sparse constraint robust ada- ptive beamformer method and device under a kind of impulsive noise.This method includes：The openness of absolute value statistical average and beam pattern is exported according to array, optimization formula is established, by Infinite Norm normalization and proper subspace method, builds weighting matrix, optimal weight vector is solved based on iteration complex weighting least square method, and Signal to Interference plus Noise Ratio is calculated according to optimal weight vector.Compared to prior art, the present invention adaptively applies larger constraint to interference signal, significantly improves interference rejection capability, improve output Signal to Interference plus Noise Ratio.
Description
Technical field
The invention belongs to a kind of sane wave beam shape of the weighting sparse constraint under communication technique field, more particularly to impulsive noise
Into (Weighted l1- norm Sparse Constraint Robust Beamforming, Wl1- RBF) method and device.
Background technology
No matter in radar or communication technical field, array signal process technique always is a weight of multiaerial system
Technology is wanted, and Wave beam forming (Beamforming, BF) technology is a most important technology in array signal process technique.Ripple
The basic thought that beam forms technology is echo signal of the enhancing from desired orientation, suppress interference signal from other directions and
Noise, to maximize output Signal to Interference plus Noise Ratio (signal-to-interference-plus-noise, SINR).
Beam-former is broadly divided into two major classes, and a kind of is the conventional beamformer with Dynamic data exchange, another kind be with
The modern Beam-former of data dependence.Conventional beamformer is independent with reception signal, and its interference rejection capability is limited.It is modern
Beam-former is all data dependence, wherein it is most classical for minimum variance distortionless response (MVDR) Beam-former, should
Beam-former maximize array output power, while keep desired signal direction array gain be 1.Due to such wave beam shape
Grow up to be a useful person and assume signal Gaussian distributed, and impulsive noise is a kind of signal type more more conventional than gaussian signal, can be stable with α
It is distributed to model the type impulsive noise.Because statistic more than second order and second order is not present in α Stable distritations, so in pulse
Under noise circumstance, MVDR and other Beam-former performance degradations based on second-order statistic.
In recent years, scholars proposed a series of Wave beam formings being directed under α (0 ＜ α≤2) Stable distritation impulsive noise and calculated
Method.Such as based on Fractional Lower Order Moments (Fractional Lower Order Statistics, FLOS) Beamforming Method, it is based on
Minimum divergence criterion (Minimum Dispersion, MD) Beamforming Method, geometric power minimize (Geometric
Power, GP) Beamforming Method etc..Beamforming Method based on FLOS utilizes the fraction p (0 ＜ p ＜ 2) of array output
Rank statistic is the ＜ p ＜ α of requirement 0 the shortcomings that the Beam-former, that is, requires known or pre-estimation α is stable as object function
The characteristic index α of distribution.Beamforming Method based on MD, utilizes lpNorm design object function, but this method still needs
Suitable p value is selected, and computation complexity is higher.Beamforming Method based on GP utilizes the zero order statistical based on logarithmic moment
Measure design object function, characteristic index α of this method without known a priori α Stable distritations, without the corresponding exponent number p of setting
Value, but this method requires that sample number is more, and when sample number deficiency, its secondary lobe is higher.
With the development of compressive sensing theory and its extensive use in the signal processing, occur a series of based on sparse
The Beamforming Method of constraint.Such as it is based on l1Minimum variance distortionless response (the l of norm sparse constraint1- MVDR) Wave beam forming
Method, based on l1Undistorted response (the l of minimum divergence of norm sparse constraint1- MDDR) beam-forming schemes, weighting l1Norm is sparse
Constrain minimum variance distortionless response (Wl1- MVDR) beam-forming schemes etc..For under Gauss model, l1- MVDR methods are in MVDR
L is added in the object function of Beam-former1Sparse constraint item, sidelobe level is reduced, but this method is to different angle direction
All signals be applied with identical sparse constraint, the selection of sparse regular parameter is larger to the performance impact of Wave beam forming.Pin
To under non-gaussian α Stable distritation models, l1- MDDR methods introduce l in minimum definitely statistical average1Sparse constraint, it is therefore an objective to improve
Wave beam forming performance.Wl1- MVDR methods are right using the subspace thought construction weighting matrix based on covariance matrix feature decomposition
Interference signal artificially applies larger constraint, significantly improves interference rejection capability, but this method is directed to be Gauss
Situation existing for signal and signal covariance matrix.
The content of the invention
The weighting sparse constraint that technical problem to be solved of the embodiment of the present invention is to provide under a kind of impulsive noise is steady
Strong Beamforming Method and device, it is intended to solve the problem of prior art interference rejection capability is not strong, and output Signal to Interference plus Noise Ratio is not high.
First aspect of the embodiment of the present invention provides the weighting sparse constraint robust ada- ptive beamformer side under a kind of impulsive noise
Method, methods described include：
Openness, the foundation optimization formula of absolute value statistical average and beam pattern is exported according to array；
By Infinite Norm normalization and proper subspace method, weighting matrix is built；
Optimal weight vector is solved based on iteration complex weighting least square method, and is calculated according to the optimal weight vector and believes dry make an uproar
Than.
Second aspect of the embodiment of the present invention provides the weighting sparse constraint robust ada- ptive beamformer dress under a kind of impulsive noise
Put, described device includes：
Formula establishes module, for exporting absolute value statistical average and openness, the foundation of beam pattern according to array
Optimize formula；
Matrix builds module, for by Infinite Norm normalization and proper subspace method, building weighting matrix；
Vector solves module, for based on iteration complex weighting least square method, solving optimal weight vector；
Signal to Interference plus Noise Ratio computing module, for calculating Signal to Interference plus Noise Ratio according to the optimal weight vector.
It was found from the embodiments of the present invention, the present invention exports absolute value statistical average and beam pattern according to array
It is openness, optimization formula is established, by Infinite Norm normalization and proper subspace method, weighting matrix is built, is answered based on iteration
Weighted least-squares method solves optimal weight vector, and calculates Signal to Interference plus Noise Ratio according to optimal weight vector.Compared to prior art, this hair
It is bright adaptively to apply larger constraint to interference signal, interference rejection capability is significantly improved, improves output Signal to Interference plus Noise Ratio.
Brief description of the drawings
In order to illustrate more clearly about the embodiment of the present invention or technical scheme of the prior art, below will be to embodiment or existing
There is the required accompanying drawing used in technology description to be briefly described, it should be apparent that, drawings in the following description are only this
Some embodiments of invention, for those skilled in the art, without having to pay creative labor, can be with root
Other accompanying drawings are obtained according to these accompanying drawings.
Accompanying drawing 1 is the weighting sparse constraint robust ada- ptive beamformer method under the impulsive noise that first embodiment of the invention provides
Implementation process schematic diagram；
Accompanying drawing 2 is the weighting sparse constraint robust ada- ptive beamformer device under the impulsive noise that second embodiment of the invention provides
Structural representation；
When accompanying drawing 3 is that α takes 1.6, MVDR methods, l1- MADR methods and Wl proposed by the present invention1The wave beam side of-RBF methods
Xiang Tu；
When accompanying drawing 4 is that α takes 1, MVDR methods, l1- MADR methods and Wl proposed by the present invention1The beam direction of-RBF methods
Figure；
Accompanying drawing 5 is MVDR methods, l1- MADR methods and Wl proposed by the present invention1The output SINR of-RBF methods and different spies
Levy the relation curve of index；
Accompanying drawing 6 is MVDR methods, l1- MADR methods and Wl proposed by the present invention1The output SINR and difference of-RBF methods are fast
The relation curve of umber of beats；
Accompanying drawing 7 is MVDR methods, l1- MADR methods and Wl proposed by the present invention1The output SINR and difference of-RBF methods are defeated
Enter SNR relation curve.
Embodiment
To enable goal of the invention, feature, the advantage of the embodiment of the present invention more obvious and understandable, below in conjunction with
Accompanying drawing in the embodiment of the present invention, the technical scheme in the embodiment of the present invention is clearly and completely described, it is clear that retouched
The embodiment stated is only part of the embodiment of the present invention, and not all embodiments.Based on the embodiment in the present invention, this area
The every other embodiment that technical staff is obtained under the premise of creative work is not made, belong to the model that the present invention protects
Enclose.
Accompanying drawing 1 is referred to, accompanying drawing 1 is that the weighting sparse constraint under the impulsive noise that first embodiment of the invention provides is sane
The implementation process schematic diagram of Beamforming Method.As shown in Figure 1, this method mainly includes the following steps that：
S101, openness, the foundation optimization formula according to array output absolute value statistical average and beam pattern；
One echo signal and P interference signal are incided on the even linear array comprising M array element from far field, utilize wave beam
Openness, the minimum definitely average and l of united beam formation output of directional diagram1Norm minimum establishes optimization formula：
s.t.wHv(θ0)=1
Wherein, E | wHX (n) | absolute value statistical average is exported for array, λ | | wHAQ||1For l1Sparse constraint item, w be M ×
1 right-safeguarding vector, x (n) are M × 1 dimensional signal of the array in n receptions, and E { } represents to seek average statistical, | | | |1Expression takes
1 norm, A=[v (θ1),v(θ2),…,v(θL)] it is that M × L that Space domain sampling is formed in secondary lobe angular regions ties up steering vector square
Battle array, L are number of samples in angular regions, and Q is the diagonal weight matrix of L × L dimensions, and λ exports geometry for balance degree of rarefication and array
The regularization parameter of power,Expression takes parameter w corresponding to minimum value,
It is array in θiThe steering vector in direction, d are array element spacing, and ζ is wavelength, θ0For target signal direction, s.t. represents constraint bar
Part.
S102, pass through Infinite Norm normalization and proper subspace method, structure weighting matrix；
N (N >=1) is the number of snap that the array received arrives, its reception signal be expressed as X=[x (1), x (2) ...,
X (N)], wherein, snap signal x (n)=[x of the array in n receptions1(n),x2(n),…,xM(n)], (1≤n≤N).
Do Infinite Norm normalized to x (n), the signal after Infinite Norm normalized is
Calculate the signal after Infinite Norm normalizedSample covariance matrix
By sample covariance matrixEigenvalues Decomposition is done, tries to achieve noise subspace Un；
Space domain sampling M × L in secondary lobe angular regions is tieed up into steering vector matrix A conjugate transposition and noise subspace UnPhase
Multiply, obtain matrix E；
L is taken to matrix E every a line2Norm, and take it reciprocal as the element on diagonal matrix on corresponding diagonal, i.e.,
Obtain weighting matrices Q.
S103, optimal weight vector is solved based on iteration complex weighting least square method, and letter is calculated according to optimal weight vector and done
Make an uproar ratio.
Define the l of complex vector1Norm is：
|gi|1=| Re (gi)|+|Im(gi)|
According to complex vector l1Norm definition, complex variable is expressed as real and imaginary parts two parts, is extended to real variable,
Using iteration complex weighting least square method, the iterative formula for trying to achieve optimal weight vector in the optimization formula is：
And by following processing, try to achieve optimal weight vector：
A=[1,0]T
Π(wr)=diag | η (1) |-1,…,|η(2N+2L)|-1}
η=Drwr∈R2(N×L)
Wherein, wrIt is initialized as
Optimal weight vector w=wr(1:M)+j·wr(M+1:2M)。
Signal to Interference plus Noise Ratio SINR is a specialty evaluation index, is defined as
E { } represents to seek average statistical, and w is optimal weight vector, { }HRepresent conjugate transposition, v (θ0) led for echo signal
To vector, snap signal x (n)=[x of the array in n receptions1(n), x2(n),…,xM(n)] it is expressed as
Wherein,Represent interference plus noise signals,Represent P
Individual interference signal, n (k) represent noise signal, v (θ0) s (n) expression echo signals.
When w selections are optimal, the value of the denominator term in SINR calculation formula is minimum value, and SINR value is changing to maximum.
That is, when w selections are optimal, interference and noise inhibiting ability are with regard to best.
When accompanying drawing 3, accompanying drawing 4 are that α takes 1.6 and 1 respectively, MVDR methods, l1- MADR methods and Wl proposed by the present invention1-RBF
The beam pattern of method.
It is M=6 to make array number, and array element spacing is d=0.5 ζ (ζ is wavelength).For l1- MADR methods, λ=0.01, most
Big iterations is 20；For Wl proposed by the present invention1- RBF methods, λ1=0.01, maximum iteration is still 20.A for [-
90 °, 0 °) and (0 °, 90 °] Space domain sampling is formed in angular range steering vector matrix, sampling interval is 1 °, echo signal and
Two interference signals are all modeled as symmetric alpha-stable distribution, location parameter 0, and its characteristic function is(δ is
Scale parameter).Signal to noise ratio is defined as 10log δs ^α/δn ^α(δsAnd δnThe scale parameter of signal and noise is represented respectively), dry ratio of making an uproar
Definition it is identical with signal to noise ratio.The arrival direction of echo signal is 5 °, the arrival directions of two interference signals be respectively -40 ° and
40 °, signal to noise ratio 10dB, dry to make an uproar than being modeled as multiple symmetric alpha-stable distribution for 10dB, additivity impulsive noise, fast umber of beats N is 300,
What ordinate represented in figure is normalized radiation pattern gain, and abscissa represents the spatial domain scope [- 90 °, 90 °] of beam scanning.
From accompanying drawing 3, accompanying drawing 4 as can be seen that when characteristic index tapers into, Wl proposed by the present invention1- RBF methods
Show than MVDR method and l1The more excellent Wave beam forming effect of-MADR methods.
Accompanying drawing 5 is MVDR methods, l1- MADR methods and Wl proposed by the present invention1The output SINR of-RBF methods and different spies
Levy the relation curve of index；Accompanying drawing 6 is MVDR methods, l1- MADR methods and Wl proposed by the present invention1The output of-RBF methods
The relation curve of SINR and different fast umber of beats；Accompanying drawing 7 is MVDR methods, l1- MADR methods and Wl proposed by the present invention1- RBF sides
The output SINR of method and different input SNR relation curve.
By characteristic index α it can be seen from accompanying drawing 5 take 1~2 in arbitrary value when, Wl proposed by the present invention1The arteries and veins of-RBF methods
Rush noise and export SINR than MVDR method and l1- MADR methods will be high, i.e., Wl proposed by the present invention1- RBF methods are made an uproar to pulse
Sound has more preferable robustness.From accompanying drawing 6, taken soon low, Wl proposed by the present invention1The Wave beam forming performance of-RBF methods
Than MVDR method and l1- MADR methods are well a lot, and are tended towards stability with the increase of fast umber of beats, algorithm, and stable state
Lower output SINR of the invention about 2dB higher than other two methods.From accompanying drawing 7, in the case of identical input SNR, this hair
The Wl of bright proposition1- RBF methods are than MVDR method and l1The output SINR of-MADR methods will height, and with input SNR increase,
The output SINR of MVDR methods tends to restrain, and the output SINR of the present invention increases with SNR increase, Wave beam forming performance
It is obviously improved.Therefore, compared to other two method, Wl proposed by the present invention1The Wave beam forming performance of-RBF methods has larger change
It is kind.
Weighting sparse constraint robust ada- ptive beamformer method under impulsive noise provided in an embodiment of the present invention, it is defeated according to array
Go out openness, the foundation optimization formula of absolute value statistical average and beam pattern, pass through Infinite Norm and normalize and feature
Space law, weighting matrix is built, optimal weight vector is solved based on iteration complex weighting least square method, and according to optimal weight vector meter
Calculate Signal to Interference plus Noise Ratio.Compared to prior art, the present invention adaptively applies larger constraint to interference signal, significantly improved dry
Rejection ability is disturbed, improves output Signal to Interference plus Noise Ratio.
Accompanying drawing 2 is referred to, accompanying drawing 2 is that the weighting sparse constraint under the impulsive noise that second embodiment of the invention provides is sane
The structural representation of beam-forming device, for convenience of description, it illustrate only the part related to the embodiment of the present invention.Accompanying drawing 2
Weighting sparse constraint robust ada- ptive beamformer device under the impulsive noise of example, mainly includes：Formula establishes module 201, matrix
Build module 202, vector solves module 203, Signal to Interference plus Noise Ratio computing module 204.
Formula establishes module 201, for exporting the openness of absolute value statistical average and beam pattern according to array, builds
Vertical optimization formula；
Matrix builds module 202, for by Infinite Norm normalization and proper subspace method, building weighting matrix；
Vector solves module 203, for based on iteration complex weighting least square method, solving optimal weight vector.
Signal to Interference plus Noise Ratio computing module 204, for calculating Signal to Interference plus Noise Ratio according to optimal weight vector.
The detailed process of the respective function of above-mentioned each Implement of Function Module, the pulse for referring to aforementioned first embodiment offer are made an uproar
The related content of weighting sparse constraint robust ada- ptive beamformer method under sound, here is omitted.
Weighting sparse constraint robust ada- ptive beamformer device under impulsive noise provided in an embodiment of the present invention, it is defeated according to array
Go out openness, the foundation optimization formula of absolute value statistical average and beam pattern, pass through Infinite Norm and normalize and feature
Space law, weighting matrix is built, optimal weight vector is solved based on iteration complex weighting least square method, and according to optimal weight vector meter
Calculate Signal to Interference plus Noise Ratio.Compared to prior art, the present invention adaptively applies larger constraint to interference signal, significantly improved dry
Rejection ability is disturbed, improves output Signal to Interference plus Noise Ratio.
It should be noted that for foregoing each method embodiment, in order to which simplicity describes, therefore it is all expressed as a series of
Combination of actions, but those skilled in the art should know, the present invention is not limited by described sequence of movement because
According to the present invention, some steps can use other orders or carry out simultaneously.Secondly, those skilled in the art should also know
Know, embodiment described in this description belongs to preferred embodiment, and involved action and module might not all be this hairs
Necessary to bright.
In the above-described embodiments, the description to each embodiment all emphasizes particularly on different fields, and does not have the portion being described in detail in some embodiment
Point, it may refer to the associated description of other embodiments.
It is to the weighting sparse constraint robust ada- ptive beamformer method and device under impulsive noise provided by the present invention above
Description, for those skilled in the art, according to the thought of the embodiment of the present invention, in specific embodiments and applications
There will be changes, to sum up, this specification content should not be construed as limiting the invention.
Claims (8)
1. a kind of weighting sparse constraint robust ada- ptive beamformer method under impulsive noise, it is characterised in that methods described includes：
Openness, the foundation optimization formula of absolute value statistical average and beam pattern is exported according to array；
By Infinite Norm normalization and proper subspace method, weighting matrix is built；
Optimal weight vector is solved based on iteration complex weighting least square method, and Signal to Interference plus Noise Ratio is calculated according to the optimal weight vector.
2. the weighting sparse constraint robust ada- ptive beamformer method under impulsive noise as claimed in claim 1, it is characterised in that institute
Openness, the foundation optimization formula that absolute value statistical average and beam pattern are exported according to array is stated, including：
One echo signal and P interference signal are incided on the even linear array comprising M array element from far field, utilize beam direction
Openness, the minimum definitely average and l of united beam formation output of figure1Norm minimum establishes optimization formula：
s.t.wHv(θ0)=1
Wherein, E | wHX (n) | absolute value statistical average is exported for array, λ | | wHAQ||1For l1Sparse constraint item, w are the right-safeguarding of M × 1
Vector, x (n) are M × 1 dimensional signal of the array in n receptions, and E { } represents to seek average statistical, | | | |1Expression takes 1 model
Number, A=[v (θ1),v(θ2),…,v(θL)] it is that M × L that Space domain sampling is formed in secondary lobe angular regions ties up steering vector matrix, L
For number of samples in angular regions, Q is the diagonal weight matrix of L × L dimensions, and λ exports geometric power for balance degree of rarefication and array
Regularization parameter,Expression takes parameter w corresponding to minimum value,For battle array
It is listed in θiThe steering vector in direction, d are array element spacing, and ζ is wavelength, θ0For target signal direction, s.t. represents constraints.
3. the weighting sparse constraint robust ada- ptive beamformer method under impulsive noise as claimed in claim 2, it is characterised in that N
(N >=1) is the number for the snap that the array received arrives, and its reception signal is expressed as X=[x (1), x (2) ..., x (N)], its
In, snap signal of the array in n receptions, (1≤n≤N) is described to pass through infinite model
Number normalization and proper subspace method, weighting matrix is built, including：
Do Infinite Norm normalized to x (n), the signal after Infinite Norm normalized is
Calculate the signal after Infinite Norm normalizedSample covariance matrix
By sample covariance matrixEigenvalues Decomposition is done, tries to achieve noise subspace Un；
Space domain sampling M × L in secondary lobe angular regions is tieed up into steering vector matrix A conjugate transposition and noise subspace UnIt is multiplied, obtains square
Battle array E；
L is taken to matrix E every a line2Norm, and take its inverse to be added as the element on diagonal matrix on corresponding diagonal
Weight matrix Q.
4. the weighting sparse constraint robust ada- ptive beamformer method under impulsive noise as claimed in claim 3, it is characterised in that institute
State and be based on iteration complex weighting least square method, solve optimal weight vector, including：
Define the l of complex vector1Norm is：
|gi|1=| Re (gi)|+|Im(gi)|
According to complex vector l1Norm definition, complex variable is expressed as real and imaginary parts two parts, is extended to real variable, utilize
Iteration complex weighting least square method, the iterative formula for trying to achieve optimal weight vector in the optimization formula are：
And by following processing, try to achieve optimal weight vector：
A=[1,0]T
Π(wr)=diag | η (1) |-1,…,|η(2N+2L)|-1}
η=Drwr∈R2(N×L)
Wherein, wrIt is initialized as
Optimal weight vector w=wr(1:M)+j·wr(M+1:2M)。
5. the weighting sparse constraint robust ada- ptive beamformer device under a kind of impulsive noise, it is characterised in that described device includes：
Formula establishes module, for exporting absolute value statistical average and openness, the foundation optimization of beam pattern according to array
Formula；
Matrix builds module, for by Infinite Norm normalization and proper subspace method, building weighting matrix；
Vector solves module, for based on iteration complex weighting least square method, solving optimal weight vector；
Signal to Interference plus Noise Ratio computing module, for calculating Signal to Interference plus Noise Ratio according to the optimal weight vector.
6. the weighting sparse constraint robust ada- ptive beamformer device under impulsive noise as claimed in claim 5, it is characterised in that institute
State formula and establish module and be specifically used for：
One echo signal and P interference signal are incided on the even linear array comprising M array element from far field, utilize beam direction
Openness, the minimum definitely average and l of united beam formation output of figure1Norm minimum establishes optimization formula：
s.t.wHv(θ0)=1
Wherein, E | wHX (n) | absolute value statistical average is exported for array, λ | | wHAQ | |1For l1Sparse constraint item, w are that M × 1 is tieed up
Weight vector, x (n) are M × 1 dimensional signal of the array in n receptions, and E { } represents to seek average statistical, | | | |1Expression takes 1 model
Number, A=[v (θ1),v(θ2),…,v(θL)] it is that M × L that Space domain sampling is formed in secondary lobe angular regions ties up steering vector matrix, L
For number of samples in angular regions, Q is the diagonal weight matrix of L × L dimensions, and λ exports geometric power for balance degree of rarefication and array
Regularization parameter,Expression takes parameter w corresponding to minimum value,For battle array
It is listed in θiThe steering vector in direction, d are array element spacing, and ζ is wavelength, θ0For target signal direction, s.t. represents constraints.
7. the weighting sparse constraint robust ada- ptive beamformer device under impulsive noise as claimed in claim 6, it is characterised in that N
(N >=1) is the number for the snap that the array received arrives, and its reception signal is expressed as X=[x (1), x (2) ..., x (N)], its
In, snap signal of the array in n receptions, (1≤n≤N)；
The matrix structure module is specifically used for：
Do Infinite Norm normalized to x (n), the signal after Infinite Norm normalized is
Calculate the signal after Infinite Norm normalizedSample covariance matrix
By sample covariance matrixEigenvalues Decomposition is done, tries to achieve noise subspace Un；
Space domain sampling M × L in secondary lobe angular regions is tieed up into steering vector matrix A conjugate transposition and noise subspace UnIt is multiplied, obtains square
Battle array E；
L is taken to matrix E every a line2Norm, and take its inverse to be added as the element on diagonal matrix on corresponding diagonal
Weight matrix Q.
8. the weighting sparse constraint robust ada- ptive beamformer device under impulsive noise as claimed in claim 7, it is characterised in that institute
Vector solution module is stated to be specifically used for：
Define the l of complex vector1Norm is：
|gi|1=| Re (gi)|+|Im(gi)|
According to complex vector l1Norm definition, complex variable is expressed as real and imaginary parts two parts, is extended to real variable, utilize
Iteration complex weighting least square method, the iterative formula for trying to achieve optimal weight vector in the optimization formula are：
And by following processing, try to achieve optimal weight vector：
A=[1,0]T
Π(wr)=diag | η (1) |-1,…,|η(2N+2L)|-1}
η=Drwr∈R2(N×L)
Wherein, wr is initialized as
Optimal weight vector w=wr(1:M)+j·wr(M+1:2M)。
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| CN112881973A (en) * | 2021-01-20 | 2021-06-01 | 西北工业大学 | Self-correction beam design method based on RBF neural network |
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108416105A (en) * | 2018-02-05 | 2018-08-17 | 大连理工大学 | Steady adaptive beam-forming algorithm under pulse and Gaussian noise |
| CN108416105B (en) * | 2018-02-05 | 2019-10-29 | 大连理工大学 | Steady adaptive beam-forming algorithm under pulse and Gaussian noise |
| CN109298395A (en) * | 2018-09-28 | 2019-02-01 | 西安建筑科技大学 | A kind of thinned array Beamforming Method based on maximum Signal to Interference plus Noise Ratio |
| CN112881973A (en) * | 2021-01-20 | 2021-06-01 | 西北工业大学 | Self-correction beam design method based on RBF neural network |
| CN115570568A (en) * | 2022-10-11 | 2023-01-06 | 江苏高倍智能装备有限公司 | Multi-manipulator cooperative control method and system |
| CN115570568B (en) * | 2022-10-11 | 2024-01-30 | 江苏高倍智能装备有限公司 | Multi-manipulator cooperative control method and system |
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