CN106452548A - Adaptive robust beamforming method - Google Patents

Abstract

The invention relates to an adaptive robust beamforming method. The method comprises the following steps: calculating a covariance matrix Rx of a received signal of a sensor array; planning a spatial domain power sampling point of the Rx; calculating another four groups of sampling points; calculating five groups of sampling matrixes; reconstructing the covariance matrix by using the five groups of sampling matrixes; and obtaining beamforming vectors, namely array element complex weighted values. By adoption of the adaptive robust beamforming method provided by the invention, the anti-interference and anti-array system error performance of adaptive robust beamforming can be improved.

Description

A kind of self adaptation robust ada- ptive beamformer method

Technical field

The invention belongs to adaptive array antenna control field, the sane wave beam of more particularly, to a kind of anti-array system error Forming method.

Background technology

Adaptive beamformer technology is an important research contents in Array Signal Processing field, radio communication, The fields such as Underwater acoustic signal processing, imaging of medical, space radio, Radar Signal Processing have a wide range of applications [1] [2] [3] [4] [5].However, the performance of adaptive beam former is easy to be affected [6] by steering vector error.Steering vector error Produce and generally caused by DOA estimate error and formation error.When there is error in steering vector, traditional adaptive beam Shaper can suppress desired signal as interference, and its performance can drastically decline, and this phenomenon is referred to as desired signal and offsets. Therefore, the sane beam-forming technology overcoming steering vector error becomes the study hotspot [6] [7] [8] [9] of this problem.? In these researchs, minimum variance is undistorted (Minimum Variance Distortionless Response:MVDR) wave beam Formation technology gets most of the attention, and document [10] summarizes the design criteria of some robust M VDR beam-forming technologies, such as sidelobe cancellation, Diagonal loading, principal space projection etc..

Based on MVDR beam-forming technology, for improving its robustness, some outstanding innovatory algorithm are had to be proposed [7] successively [8][9].A kind of robust ada- ptive beamformer algorithm being optimized thought based on worst condition is proposed, to multiple mismatch feelings in document [7] Condition has robustness.Document [8] proposes a kind of method of iterated revision steering vector, orthogonal by optimum choice steering vector Vector, overcomes steering vector error.Document [9] is based on document [8], and research is using minimum information realization robust ada- ptive beamformer. These beam-forming technologies based on MVDR utilize the blur estimation to desired signal direction of arrival, by revising leading of desired signal Robustness to vector strengthening system.

Although the method revising steering vector can overcome the error that steering vector mismatch causes to a certain extent, current When hoping that Signal-to-Noise is larger, the Beamforming Method revising steering vector is unable to reach optimum efficiency.Meanwhile, to steering vector Correction pass through optimization problem iterative, amount of calculation is larger.For these problems, [11] propose a kind of interference plus noise association side The Beamforming Method that difference matrix (INC) reconstructs, by removing the information of desired signal in covariance matrix, realizes desired signal Offset the thorough solution of phenomenon.But because the method is related to energy Power estimation and integral operation, operand is still larger.

Bibliography：

[1]VAN VEEN B.D.and BUCKLEY K.M.,Beamforming:a versatile approach to spatial filtering[J].IEEE ASSP Magazine,19885(2):4-24.

[2] Liu Fengcong. robust adaptive beamforming algorithm [M]. Xi'an:Publishing house of Xian Electronics Science and Technology University, 2012.

[3] Yan Shefeng, Ma Yuanliang. the design of sensor array beam optimization and application [M]. Beijing:Science Press, 2009.

[4] Cheng Jing, Tang Liang, Zheng Min, the robust adaptive beamforming algorithm [J] based on angle spread. computer application, 2014,34(S1):15-17.

[5] Zeng Hao, Zheng Fang, Yuan Angfei, Huang Tiancong, the target identification method [J] in multi-user digital Wave beam forming, calculate Machine is applied, and 201131 (1):229-231.

[6]VOROBYOV S.A.,GERSHMAN A.B.,and LUO Z.Q.,Robust adaptive beamforming using worst-case performance optimization:A solution to the signal mismatch problem[J],IEEE Transactions on Signal Processing,200351(2): 313-324.

[7]STOICA P.,WANG Z.and LI J.,Robust capon beamforming[J].IEEE Signal processing letters,200310(6):172-175.

[8]HASSANIEN A.,VOROBYOV S.A.,and WONG K.M.,Robust Adaptive Beamforming Using Sequential Quadratic Programming:An Iterative Solution to the Mismatch Problem[J].IEEE Signal Processing Letters,200815:733-736.

[9]KHABBAZIBAMENJ A.,VOROBYOV S.A.,and HASSANIEN A.,Robust Adaptive Beamforming Based on Steering Vector Estimation With as Little as Possible Prior Information[J].IEEE Transactions on Signal Processing,201260(6):2974- 2987

[10]VOROBYOV S.A.,Principles of minimum variance robust adaptive beamforming design[J].Signal Processing,201393:3264-3277.

[11]Gu Y.,LESHEM A.,Robust adaptive beamforming based on interference covariance matrix reconstruction and steering vector estimation[J].IEEE Transactions on Signal Processing,201260:3881-3885.

[12]FUEREI J.R.,Theory and application of covariance matrix tapers for robust adaptive beamforming[J].IEEE Transactions on Signal Processing, 199947:977-985.

Content of the invention

It is an object of the invention to provide one kind can improve, adaptive antenna Wave beam forming is anti-interference, anti-array system error The quickly sane Beamforming Method of performance.Technical scheme is as follows：

A kind of self adaptation robust ada- ptive beamformer method, comprises the following steps：

Step one：Calculate receipt signal x sampled by n times snap_nBuild the covariance square of sensor array receipt signal Battle array R_x.

Step 2：Planning is to R_xSpatial domain power samples point：The central point of incoming wave signal angle scope Θ of priori is set Initial point for point sequence of samplingIf element number of array is M, except initial pointPlan M-1 sampled point again, be designated asAnd meet

Step 3：Calculate another 4 groups of sampled points：Make Δ=Θ/5, thenK=1,2,3,4, Meet

Step 4：Calculate 5 groups of sampling matrixs：Steering vector by each sampled point in step 2 and threeI=0, 1 ..., M, k=0,1,2,3,4 are arranged in matrixSampling matrix then can be obtainedK=0,1,2,3,4, wherein, ()^HIt is Hermitian transposition computing.

Step 5：Reconstruct covariance matrix using this 5 groups of sampling matrixsIt is shown below.

Step 6：Obtain Wave beam forming vector, that is, each array element is added with weights.

The invention has the beneficial effects as follows：

The present invention is directed to the problem of [11] institute extracting method, proposes a kind of method avoiding power Spectral Estimation and integral operation, Not only remain former method to scattering interference, direction of arrival obscures the problems such as robustness, and it is low to have a computation complexity, realizes Simple advantage.Its performance is consistent with the performance of [11] institute extracting method, but computation complexity substantially reduces, and has very strong engineering Practicality.

The present invention can be according to the fuzzy ranges of desired signal direction of arrival, adaptively formed wave beam, and self adaptation is in disturber To forming null, suppression interference signal is it is not necessary to know the azimuth of interference.The present invention is to the situation that there is desired signal scattering And the inaccurate situation of DOA estimate has good robustness.The application of medium-scale array more than 22 for the present invention Middle energy obtains best effect.

Brief description

Fig. 1 is the structural representation of M for array number.

Specific embodiment

Step one：Receipt signal x sampled by n times snap_nBuild the covariance matrix of sensor array receipt signal：

Step 2：Planning is to R_xSpatial domain power samples point.The central point of incoming wave signal angle scope Θ of priori is set Initial point for point sequence of samplingIf element number of array is M, except initial pointPlan M-1 sampled point again, be designated asAnd meet

Step 3：Calculate another 4 groups of sampled points.Make Δ=Θ/5, thenK=1,2,3,4. Meet

Step 4：Calculate 5 groups of " sampling " matrixes：Steering vector by each sampled point in step 2 and threeI= 0,1 ..., M, k=0,1,2,3,4 are arranged in matrix" sampling " matrix then can be obtainedK=0,1,2,3,4, wherein, ()^HIt is Hermitian transposition computing.

Step 5：Obtain the covariance matrix reconstructing using this 5 groups of sampling matrixsIt is shown below：

Step 6：Obtain Wave beam forming vector using MVDR-SMI Beam-former, that is, each array element is added with weights.

Claims (1)

1. a kind of self adaptation robust ada- ptive beamformer method, comprises the following steps：

Step one：Calculate receipt signal x sampled by n times snap_nBuild the covariance matrix R of sensor array receipt signal_x.

Step 2：Planning is to R_xSpatial domain power samples point：The central point of incoming wave signal angle scope Θ of priori is set to sample The initial point of point sequenceIf element number of array is M, except initial pointPlan M-1 sampled point again, be designated asAnd Meet

Step 3：Calculate another 4 groups of sampled points：Make Δ=Θ/5, thenK=1,2,3,4,Meet

Step 4：Calculate 5 groups of sampling matrixs：Steering vector by each sampled point in step 2 and three K=0,1,2,3,4 is arranged in matrixSampling matrix then can be obtainedK=0,1,2,3,4, wherein, ()^HIt is Hermitian transposition computing.

Step 5：Reconstruct covariance matrix using this 5 groups of sampling matrixsIt is shown below.

${\tilde{R}}_{i+n}=\underset{k=0}{\overset{4}{\Σ}}{S}_k\·R_x\·S_k.$

Step 6：Obtain Wave beam forming vector, that is, each array element is added with weights.

$w=\frac{{\tilde{R}}_{i+n}^{-1}d\left({\mathrm{\θ}}_0^{\left(0\right)}\right)}{d^H\left({\mathrm{\θ}}_0^{\left(0\right)}\right){\tilde{R}}_{i+n}^{-1}d\left({\mathrm{\θ}}_0^{\left(0\right)}\right)}$