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 PDF

Info

Publication number
CN107342836A
CN107342836A CN201710159663.1A CN201710159663A CN107342836A CN 107342836 A CN107342836 A CN 107342836A CN 201710159663 A CN201710159663 A CN 201710159663A CN 107342836 A CN107342836 A CN 107342836A
Authority
CN
China
Prior art keywords
matrix
array
vector
signal
weight vector
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.)
Granted
Application number
CN201710159663.1A
Other languages
Chinese (zh)
Other versions
CN107342836B (en
Inventor
阳召成
汪小叶
黄建军
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Shenzhen University
Original Assignee
Shenzhen University
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Shenzhen University filed Critical Shenzhen University
Priority to CN201710159663.1A priority Critical patent/CN107342836B/en
Publication of CN107342836A publication Critical patent/CN107342836A/en
Application granted granted Critical
Publication of CN107342836B publication Critical patent/CN107342836B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • 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
    • 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
    • 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
    • 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

Landscapes

  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Radio Transmission System (AREA)
  • Variable-Direction Aerials And Aerial Arrays (AREA)

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

Weighted sparse constraint robust beam forming method and device under impulse noise
Technical Field
The invention belongs to the technical field of communication, and particularly relates to Weighted sparse constraint robust beam forming (Weighted l) under impulse noise1-norm Sparse Constraint Robust Beamforming，Wl1-RBF) method and apparatus.
Background
In the field of radar and communication technologies, array signal processing technology has been an important technology for multi-antenna systems, and Beam Forming (BF) technology is the most important technology in array signal processing technology. The basic idea of beamforming technology is to enhance the target signal from the desired direction and suppress the interfering signals and noise from other directions to maximize the output signal-to-interference-plus-noise (SINR).
Beamformers fall into two main categories, one being a traditional beamformer independent of the data and the other being a modern beamformer dependent on the data. Conventional beamformers are independent of the received signal and have limited interference rejection capabilities. Modern beamformers are data dependent, the most classical of which is the Minimum Variance Distortionless Response (MVDR) beamformer which maximizes the array output power while maintaining the array gain at 1 for the desired signal direction. Since this type of beamformer assumes that the signal follows a gaussian distribution, while impulse noise is a more common signal type than gaussian, this type of impulse noise can be modeled with an alpha stationary distribution. MVDR and other second order statistics based beamformers severely degrade in impulse noise environments because the alpha plateau is free of second and higher order statistics.
In recent years, researchers have proposed a series of beamforming algorithms for α (0 < α ≦ 2) under stable distributed impulse noise, such as FLOS-based beamforming methods with Fractional Low Order Moments (MD), Minimum Dispersion criteria (MD), geometric Power minimization (GP), etc. FLOS-based beamforming methods utilize Fractional p (0 < p < 2) Order Statistics of the array output as the objective function, which have the disadvantage of requiring 0 < p < α, i.e., requiring characteristic index α for known or pre-estimated α stable distributionpThe GP-based beam forming method utilizes the zeroth order statistic based on the logarithmic moment to design the objective function, the characteristic index α of α stable distribution does not need to be known a priori, and the corresponding order p value does not need to be set, but the method requires more samples, and when the number of the samples is insufficient, the side lobe is higher.
With the development of the compressive sensing theory and the wide application thereof in signal processing, a series of beam forming methods based on sparse constraint appear. E.g. based on1Norm sparsity constrained minimum variance undistorted response (l)1-MVDR) based on l1Norm sparsity constrained minimum divergence undistorted response (l)1-MDDR), weighting l1Norm sparsity constrained minimum variance distortionless response (Wl)1MVDR), etc. For the Gaussian model, /)1MVDR method adds l to the objective function of the MVDR beamformer1Sparse constraint terms are used for reducing the sidelobe level, but the method applies the same sparse constraint to all signals in different angle directions, and the performance of beam forming is greatly influenced by the selection of sparse regular parameters under a non-Gaussian α stable distribution model1The MDDR method introduces l in the minimum absolute statistical mean1Sparsity constraints, the purpose of which is to improve beamforming performance. Wl1The MVDR method utilizes a subspace idea based on covariance matrix characteristic decomposition to construct a weighting matrix, artificially imposes a large constraint on interference signals, and obviously improves the interference suppression capability, but the method aims at the situation that the interference signals are Gaussian signals and a signal covariance matrix exists.
Disclosure of Invention
The technical problem to be solved by the embodiments of the present invention is to provide a method and an apparatus for forming a weighted sparse constraint robust beam under impulse noise, and aims to solve the problems of weak interference suppression capability and low output signal-to-interference-and-noise ratio in the prior art.
The first aspect of the embodiments of the present invention provides a method for forming a weighted sparse constraint robust beam under impulse noise, where the method includes:
establishing an optimization formula according to the array output absolute value statistical average and the sparsity of a beam directional diagram;
constructing a weighting matrix through infinite norm normalization and a characteristic subspace method;
and solving an optimal weight vector based on an iterative complex weighted least square method, and calculating a signal-to-interference-and-noise ratio according to the optimal weight vector.
A second aspect of the embodiments of the present invention provides a weighted sparse constraint robust beamforming device under impulse noise, where the device includes:
the formula establishing module is used for establishing an optimization formula according to the array output absolute value statistical average and the sparsity of a beam directional diagram;
the matrix construction module is used for constructing a weighting matrix through infinite norm normalization and a characteristic subspace method;
the vector solving module is used for solving an optimal weight vector based on an iterative complex weighted least square method;
and the signal-to-interference-and-noise ratio calculation module is used for calculating the signal-to-interference-and-noise ratio according to the optimal weight vector.
According to the embodiment of the invention, an optimization formula is established according to the array output absolute value statistical average and the sparsity of a beam directional diagram, a weighting matrix is constructed through infinite norm normalization and a characteristic subspace method, an optimal weight vector is solved based on an iterative complex weighted least square method, and the signal-to-interference-and-noise ratio is calculated according to the optimal weight vector. Compared with the prior art, the invention adaptively imposes larger constraint on the interference signal, obviously improves the interference suppression capability and improves the output signal-to-interference-and-noise ratio.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without inventive exercise.
Fig. 1 is a schematic flow chart of an implementation of a weighted sparse constraint robust beamforming method under impulse noise according to a first embodiment of the present invention;
fig. 2 is a schematic structural diagram of a weighted sparse constraint robust beamforming device under impulse noise according to a second embodiment of the present invention;
FIG. 3 shows M when α is 1.6VDR method, l1Method of MADR and Wl according to the invention1-a beam pattern of the RBF method;
FIG. 4 shows the MVDR method, when α takes 11Method of MADR and Wl according to the invention1-a beam pattern of the RBF method;
FIG. 5 shows the MVDR method, l1Method of MADR and Wl according to the invention1-output SINR versus different characteristic indexes of the RBF method;
FIG. 6 shows the MVDR method, l1Method of MADR and Wl according to the invention1-output SINR versus different fast beat number of RBF method;
FIG. 7 shows the MVDR method, l1Method of MADR and Wl according to the invention1Output SINR versus different input SNR for the RBF method.
Detailed Description
In order to make the objects, features and advantages of the embodiments of the present invention more obvious and understandable, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, fig. 1 is a schematic flow chart illustrating an implementation of a weighted sparse constraint robust beamforming method under impulse noise according to a first embodiment of the present invention. As shown in fig. 1, the method mainly comprises the following steps:
s101, establishing an optimization formula according to array output absolute value statistical average and sparsity of a beam pattern;
an objectThe signal and P interference signals are incident on a uniform linear array containing M array elements from a far field, and the minimum absolute average sum l is output by combining beam forming by utilizing the sparsity of a beam pattern1Norm minimization establishes an optimization formula:
s.t.wHv(θ0)＝1
wherein E | wHx (n) is the statistical average of the absolute values of the array output, and λ | | | wHAQ||1Is 11Sparse constraint term, w is M × 1 dimension weight vector, x (n) is M × 1 dimension signal received by array at n moment, E {. cndot.) represents calculating statistical mean, | | | · | | survival |1The expression is given by 1 norm, A ═ v (theta)1),v(θ2),…,v(θL)]An M × L-dimension steering vector matrix formed by spatial sampling in a side lobe angle area, wherein L is the number of samples in the angle area, Q is a diagonal weighting matrix of L × L dimension, lambda is a regularization parameter for balancing sparsity and array output geometric power,the parameter w corresponding to the minimum value is taken as the expression,for arrays at thetaiThe direction of the guide vector, d is the array element spacing, zeta is the wavelength, theta0S.t. represents the constraint for the target signal direction.
S102, constructing a weighting matrix through infinite norm normalization and a characteristic subspace method;
n (N ≧ 1) is the number of snapshots received by the array, and its received signal is represented by X ═ X (1), X (2), …, X (N)]Wherein the snapshot signal x (n) received by the array at time n is ═ x1(n),x2(n),…,xM(n)]，(1≤n≤N)。
Making infinite norm normalization for x (n)The signal after the conversion processing and the infinite norm normalization processing is
Calculating signals after infinite norm normalization processingOf the sampling covariance matrix
Covariance matrix to be sampledDecomposing the characteristic value to obtain noise subspace Un；
The spatial domain sampling M × L dimension guide vector matrix A in the side lobe angle area is conjugated and transposed with the noise subspace UnMultiplying to obtain a matrix E;
take l for each row of matrix E2And taking the reciprocal of the norm as an element on a corresponding angular line on the diagonal matrix to obtain a weighting matrix Q.
S103, solving the optimal weight vector based on an iterative complex weighted least square method, and calculating the signal-to-interference-and-noise ratio according to the optimal weight vector.
Defining l of a complex vector1Norm is:
|gi|1＝|Re(gi)|+|Im(gi)|
according to a complex vector l1The norm definition of (1) is to express a complex variable into a real part and an imaginary part, expand the complex variable into a real variable, and solve the optimization formula by using an iterative complex weighted least square methodThe iterative formula for the optimal weight vector is:
and the optimal weight vector is obtained through the following processing:
a＝[1,0]T
Π(wr)＝diag{|η(1)|-1,…,|η(2N+2L)|-1}
η＝Drwr∈R2(N×L)
wherein, wrIs initialized to
The optimal weight vector w is wr(1:M)+j·wr(M+1:2M)。
The SINR is a professional evaluation index and is defined as
E {. is used for solving the statistical mean value, w is the optimal weight vector, {. times }HDenotes the conjugate transpose, v (θ)0) For a target signal steering vector, the snapshot signal x (n) received at time n is arrayed, [ x ]1(n)，x2(n),…,xM(n)]Is shown as
Wherein,which is representative of the interference-plus-noise signal,representing P interference signals, n (k) representing noise signals, v (theta)0) s (n) represents a target signal.
When w is selected to be optimal, the value of the denominator term in the SINR calculation formula is the minimum value, and the SINR value becomes the maximum. That is, when w is selected optimally, interference and noise suppression capabilities are best.
FIG. 3 and FIG. 4 show the MVDR method,/, when α is taken as 1.6 and 1, respectively1Method of MADR and Wl according to the invention1-beam pattern of the RBF method.
Let M be 6, and d be 0.5 ζ (ζ is wavelength). For l1MADR method, λ ═ 0.01, maximum number of iterations 20; for the Wl proposed by the present invention1-RBF method, λ1The maximum number of iterations is still 20, 0.01. A is [ -90 °,0 °) and (0 °,90 ° ]]A guide vector matrix formed by spatial sampling in an angle range, the sampling interval is 1 degree, a target signal and two interference signals are modeled as symmetrical α stable distribution, the position parameter is 0, and the characteristic function is(as a scale parameter). Signal-to-noise ratioRatio is defined as 10logs ^α/n ^α(sAndnscale parameters respectively representing signals and noise), the definition of the dry-to-noise ratio is the same as the signal-to-noise ratio, the arrival direction of a target signal is 5 degrees, the arrival directions of two interference signals are-40 degrees and 40 degrees respectively, the signal-to-noise ratio is 10dB, the dry-to-noise ratio is 10dB, the additive impulse noise is modeled as a complex symmetrical α stable distribution, the fast beat number N is 300, the ordinate in the figure represents the gain of a normalized directional diagram, and the abscissa represents the airspace range of beam scanning of [ -90 degrees ], 90 degrees]。
As can be seen from FIGS. 3 and 4, as the characteristic index becomes smaller, the proposed Wl of the present invention1The RBF method exhibits a specific MVDR method and1better beamforming effect of the MADR method.
FIG. 5 shows the MVDR method, l1Method of MADR and Wl according to the invention1-output SINR versus different characteristic indexes of the RBF method; FIG. 6 shows the MVDR method, l1Method of MADR and Wl according to the invention1-output SINR versus different fast beat number of RBF method; FIG. 7 shows the MVDR method, l1Method of MADR and Wl according to the invention1Output SINR versus different input SNR for the RBF method.
As can be seen from the attached figure 5, when the characteristic index α takes any value from 1 to 2, the Wl provided by the invention1Pulse noise output SINR ratio MVDR method of RBF method and I1The MADR method is high, namely Wl proposed by the invention1The RBF method is more robust to impulse noise. As can be seen from FIG. 6, at low fast beat, Wl proposed by the present invention1-beamforming performance ratio MVDR method and/of RBF method1The MADR method is much better and the algorithm tends to be stable as the number of fast beats increases, and the output SINR of the present invention is about 2dB higher than the other two methods in a stable state. As can be seen from FIG. 7, under the same input SNR, the proposed Wl of the present invention1RBF method ratio MVDR method and1the output SINR of the MADR method is high, and as the input SNR increases, the output SINR of the MVDR method tends to be highConvergence, while the output SINR of the present invention increases with increasing SNR, the beamforming performance improves significantly. Therefore, compared with the other two methods, the present invention proposes the Wl1The beam forming performance of the RBF method is greatly improved.
According to the method for forming the weighted sparse constraint robust beam under the impulse noise, provided by the embodiment of the invention, an optimization formula is established according to the array output absolute value statistical average and the sparsity of a beam directional diagram, a weighting matrix is constructed through infinite norm normalization and a characteristic subspace method, an optimal weight vector is solved based on an iterative complex weighted least square method, and the signal-to-interference-and-noise ratio is calculated according to the optimal weight vector. Compared with the prior art, the invention adaptively imposes larger constraint on the interference signal, obviously improves the interference suppression capability and improves the output signal-to-interference-and-noise ratio.
Referring to fig. 2, fig. 2 is a schematic structural diagram of a weighted sparse constraint robust beamforming apparatus under impulse noise according to a second embodiment of the present invention, and for convenience of description, only the relevant portions of the apparatus according to the second embodiment of the present invention are shown. The weighted sparse constraint robust beam forming device under impulse noise illustrated in fig. 2 mainly comprises: the system comprises a formula establishing module 201, a matrix establishing module 202, a vector solving module 203 and a signal-to-interference-and-noise ratio calculating module 204.
A formula establishing module 201, configured to establish an optimization formula according to the array output absolute value statistical average and sparsity of a beam pattern;
the matrix construction module 202 is configured to construct a weighting matrix through infinite norm normalization and a feature subspace method;
and the vector solving module 203 is used for solving the optimal weight vector based on an iterative complex weighted least square method.
And a signal to interference plus noise ratio calculation module 204, configured to calculate a signal to interference plus noise ratio according to the optimal weight vector.
The specific process of each functional module for implementing its function may refer to the related content of the weighted sparse constraint robust beam forming method under impulse noise provided in the first embodiment, and is not described herein again.
The weighted sparse constraint robust beam forming device under the impulse noise provided by the embodiment of the invention establishes an optimization formula according to the array output absolute value statistical average and the sparsity of a beam directional diagram, constructs a weighting matrix through infinite norm normalization and a characteristic subspace method, solves an optimal weight vector based on an iterative complex weighted least square method, and calculates the signal-to-interference-and-noise ratio according to the optimal weight vector. Compared with the prior art, the invention adaptively imposes larger constraint on the interference signal, obviously improves the interference suppression capability and improves the output signal-to-interference-and-noise ratio.
It should be noted that, for the sake of simplicity, the above-mentioned method embodiments are described as a series of acts or combinations, but those skilled in the art should understand that the present invention is not limited by the described order of acts, as some steps may be performed in other orders or simultaneously according to the present invention. Further, those skilled in the art will appreciate that the embodiments described in the specification are presently preferred and that no acts or modules are necessarily required of the invention.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In view of the above description of the weighted sparse constraint robust beamforming method and apparatus under impulse noise according to the present invention, those skilled in the art will appreciate that there are variations in the embodiments and applications of the method and apparatus according to the present invention.

Claims (8)

1. A method for weighted sparse constraint robust beamforming under impulse noise, the method comprising:
establishing an optimization formula according to the array output absolute value statistical average and the sparsity of a beam directional diagram;
constructing a weighting matrix through infinite norm normalization and a characteristic subspace method;
and solving an optimal weight vector based on an iterative complex weighted least square method, and calculating a signal-to-interference-and-noise ratio according to the optimal weight vector.
2. The method of claim 1, wherein the establishing an optimization formula based on the array output absolute value statistical mean and the sparsity of the beam pattern comprises:
a target signal and P interference signals are incident on a uniform linear array containing M array elements from a far field, and the minimum absolute average sum l is output by combining beam forming by utilizing the sparsity of a beam pattern1Norm minimization establishes an optimization formula:
s.t.wHv(θ0)＝1
wherein E | wHx (n) is the statistical average of the absolute values of the array output, and λ | | | wHAQ||1Is 11Sparse constraint term, w is M × 1 dimension weight vector, x (n) is M × 1 dimension signal received by array at n moment, E {. cndot.) represents calculating statistical mean, | | | · | | survival |1The expression is given by 1 norm, A ═ v (theta)1),v(θ2),…,v(θL)]An M × L-dimension steering vector matrix formed by spatial sampling in a side lobe angle area, wherein L is the number of samples in the angle area, Q is a diagonal weighting matrix of L × L dimension, lambda is a regularization parameter for balancing sparsity and array output geometric power,the parameter w corresponding to the minimum value is taken as the expression,for arrays at thetaiThe direction of the guide vector, d is the array element spacing, zeta is the wavelength, theta0S.t. represents the constraint for the target signal direction.
3. The impulse-noise-under-weighting sparse-constrained robust beamforming method according to claim 2 wherein N (N ≧ 1) is said arrayThe number of received snapshots is shown as X ═ X (1), X (2), …, X (n)]Wherein the array receives a snapshot signal at time n(N is more than or equal to 1 and less than or equal to N), and constructing a weighting matrix through infinite norm normalization and a characteristic subspace method, wherein the weighting matrix comprises the following steps:
carrying out infinite norm normalization processing on x (n), wherein the signal after the infinite norm normalization processing is
Calculating signals after infinite norm normalization processingOf the sampling covariance matrix
Covariance matrix to be sampledDecomposing the characteristic value to obtain noise subspace Un；
The spatial domain sampling M × L dimension guide vector matrix A in the side lobe angle area is conjugated and transposed with the noise subspace UnMultiplying to obtain a matrix E;
take l for each row of matrix E2And taking the norm and the reciprocal thereof as an element on a corresponding diagonal line on the diagonal matrix to obtain a weighting matrix Q.
4. The method of weighted sparse-constrained robust beamforming under impulse noise according to claim 3 wherein said solving an optimal weight vector based on iterative complex weighted least squares comprises:
defining l of a complex vector1Norm is:
|gi|1＝|Re(gi)|+|Im(gi)|
according to a complex vector l1The norm definition of (2) is to express a complex variable into a real part and an imaginary part, expand the complex variable into a real variable, and obtain an iterative formula of an optimal weight vector in the optimization formula by using an iterative complex weighted least square method, wherein the iterative formula is as follows:
and the optimal weight vector is obtained through the following processing:
a＝[1,0]T
Π(wr)＝diag{|η(1)|-1,…,|η(2N+2L)|-1}
η＝Drwr∈R2(N×L)
wherein, wrIs initialized to
The optimal weight vector w is wr(1:M)+j·wr(M+1:2M)。
5. An apparatus for weighted sparse constraint robust beamforming under impulse noise, the apparatus comprising:
the formula establishing module is used for establishing an optimization formula according to the array output absolute value statistical average and the sparsity of a beam directional diagram;
the matrix construction module is used for constructing a weighting matrix through infinite norm normalization and a characteristic subspace method;
the vector solving module is used for solving an optimal weight vector based on an iterative complex weighted least square method;
and the signal-to-interference-and-noise ratio calculation module is used for calculating the signal-to-interference-and-noise ratio according to the optimal weight vector.
6. The apparatus according to claim 5, wherein the formula building module is specifically configured to:
a target signal and P interference signals are incident on a uniform linear array containing M array elements from a far field, and the minimum absolute average sum l is output by combining beam forming by utilizing the sparsity of a beam pattern1Norm minimization establishes an optimization formula:
s.t.wHv(θ0)＝1
wherein E | wHx (n) is the statistical average of the absolute values of the array output, lambda wHAQ1Is 11Sparse constraint term, w is M × 1 dimension weight vector, x (n) is M × 1 dimension signal received by array at n moment, E {. cndot.) represents calculating statistical mean, | | | · | | survival |1The expression is given by 1 norm, A ═ v (theta)1),v(θ2),…,v(θL)]An M × L-dimension steering vector matrix formed by spatial sampling in a side lobe angle area, wherein L is the number of samples in the angle area, Q is a diagonal weighting matrix of L × L dimension, lambda is a regularization parameter for balancing sparsity and array output geometric power,the parameter w corresponding to the minimum value is taken as the expression,for arrays at thetaiThe direction of the guide vector, d is the array element spacing, zeta is the wavelength, theta0S.t. represents the constraint for the target signal direction.
7. The impulse-noise weighted sparsely-constrained robust beamforming device according to claim 6 wherein N (N ≧ 1) is the number of snapshots received by the array whose received signals are denoted X ═ X (1), X (2), …, X (N)]Wherein the array receives a snapshot signal at time n，(1≤n≤N)；
The matrix construction module is specifically configured to:
carrying out infinite norm normalization processing on x (n), wherein the signal after the infinite norm normalization processing is
Calculating signals after infinite norm normalization processingOf the sampling covariance matrix
Covariance matrix to be sampledDecomposing the characteristic value to obtain noise subspace Un；
The spatial domain sampling M × L dimension guide vector matrix A in the side lobe angle area is conjugated and transposed with the noise subspace UnMultiplying to obtain a matrix E;
take l for each row of matrix E2And taking the norm and the reciprocal thereof as an element on a corresponding diagonal line on the diagonal matrix to obtain a weighting matrix Q.
8. The apparatus according to claim 7, wherein the vector solving module is specifically configured to:
defining l of a complex vector1Norm is:
|gi|1＝|Re(gi)|+|Im(gi)|
according to a complex vector l1The norm definition of (2) is to express a complex variable into a real part and an imaginary part, expand the complex variable into a real variable, and obtain an iterative formula of an optimal weight vector in the optimization formula by using an iterative complex weighted least square method, wherein the iterative formula is as follows:
and the optimal weight vector is obtained through the following processing:
a＝[1,0]T
Π(wr)＝diag{|η(1)|-1,…,|η(2N+2L)|-1}
η＝Drwr∈R2(N×L)
wherein wr is initialized to
The optimal weight vector w is wr(1:M)+j·wr(M+1:2M)。
CN201710159663.1A 2017-03-17 2017-03-17 Weighting sparse constraint robust ada- ptive beamformer method and device under impulsive noise Active CN107342836B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201710159663.1A CN107342836B (en) 2017-03-17 2017-03-17 Weighting sparse constraint robust ada- ptive beamformer method and device under impulsive noise

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201710159663.1A CN107342836B (en) 2017-03-17 2017-03-17 Weighting sparse constraint robust ada- ptive beamformer method and device under impulsive noise

Publications (2)

Publication Number Publication Date
CN107342836A true CN107342836A (en) 2017-11-10
CN107342836B CN107342836B (en) 2019-04-23

Family

ID=60222594

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201710159663.1A Active CN107342836B (en) 2017-03-17 2017-03-17 Weighting sparse constraint robust ada- ptive beamformer method and device under impulsive noise

Country Status (1)

Country Link
CN (1) CN107342836B (en)

Cited By (4)

* Cited by examiner, † Cited by third party
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
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

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101783776A (en) * 2009-01-15 2010-07-21 华为技术有限公司 Precoding feedback method, system, user equipment and base station
CN101908918A (en) * 2010-07-26 2010-12-08 重庆大学 Beam synthesizing method in wireless communication receiver
CN102547953A (en) * 2010-12-09 2012-07-04 普天信息技术研究院有限公司 Method for obtaining beam forming gain

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101783776A (en) * 2009-01-15 2010-07-21 华为技术有限公司 Precoding feedback method, system, user equipment and base station
CN101908918A (en) * 2010-07-26 2010-12-08 重庆大学 Beam synthesizing method in wireless communication receiver
CN102547953A (en) * 2010-12-09 2012-07-04 普天信息技术研究院有限公司 Method for obtaining beam forming gain

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
刘振: "一种加权稀疏约束稳健Capon波束形成方法", 《物理学报》 *

Cited By (6)

* Cited by examiner, † Cited by third party
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

Also Published As

Publication number Publication date
CN107342836B (en) 2019-04-23

Similar Documents

Publication Publication Date Title
CN105302936B (en) The Adaptive beamformer method reconstructed based on correlation computations and covariance matrix
CN105137399B (en) The radar self-adaption Beamforming Method filtered based on oblique projection
CN106569181A (en) Algorithm for reconstructing robust Capon beamforming based on covariance matrix
CN107342836B (en) Weighting sparse constraint robust ada- ptive beamformer method and device under impulsive noise
CN107276658B (en) Beam forming method based on covariance matrix reconstruction under color noise
CN110045323B (en) Matrix filling-based co-prime matrix robust adaptive beamforming algorithm
CN110196410B (en) Array antenna main lobe interference suppression method and system
CN106353738B (en) A kind of robust adaptive beamforming method under new DOA mismatch condition
CN105306123A (en) Robust beamforming method with resistance to array system errors
CN108631851B (en) Self-adaptive beam forming method based on uniform linear array null deepening
CN106788655A (en) The relevant robust ada- ptive beamformer method of the interference of unknown mutual coupling information under array mutual-coupling condition
CN113884979A (en) Robust adaptive beam forming method for interference plus noise covariance matrix reconstruction
CN106842135B (en) Adaptive beamformer method based on interference plus noise covariance matrix reconstruct
CN106680779B (en) Beam-forming method and device under impulsive noise
Shuai et al. Fast and accurate covariance matrix reconstruction for adaptive beamforming using Gauss-Legendre quadrature
CN104346532B (en) MIMO (multiple-input multiple-output) radar dimension reduction self-adaptive wave beam forming method
CN111551892B (en) Steady self-adaptive beam forming method and device
CN110361697B (en) Robust beam forming method based on covariance matrix hybrid reconstruction
WO2018165971A1 (en) Weighted i1-norm sparse constraint robust beamforming method and device under impulse noise
CN107135026B (en) Robust beam forming method based on matrix reconstruction in presence of unknown mutual coupling
CN115453450A (en) Robust DOA estimation method for resisting mutual coupling under nested array
CN110208830B (en) Navigation anti-interference method based on space-time two-dimensional sparse array
CN107894597B (en) Self-adaptive zeroing method based on linear virtual array element
CN117478186B (en) Beam forming method based on covariance matrix estimation and subspace construction
Jiang et al. An efficient null-broadening adaptive beamformer based on magnitude response constraints

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant