Research On Robust Receive/Transmit Beamforming Method

As a fundamental technique in array signal processing,adaptive beamforming has been widely used in radar,sonar,wireless communications,and medical-imaging.The beamforming methods are of two kinds: data-independent and data-dependent/adaptive.The standard Capon beamformer,as one of the well-known adaptive beamformers,adaptively selects the weight vector to minimize the array output power,subject to the linear constraint that the signal-of-interest(SOI)does not suffer from any distortion.The Capon beamformer has better resolution and much better interference rejection capability than the data-independent beamformer.However,when the steering vector of the desired signal is imprecise,the Capon beamformer may suppress the SOI as an interference,which results in significantly underestimated SOI power and dramatically reduced array output signal-to-interference-plus-noise ratio(SINR).In the last three decades,robust adaptive beamforming algorithms have been developed to improve the robustness of the Capon beamformer.In this paper,for different application scenarios and form of signal(including circular signal and non-circular signal),we propose some corresponding robust adaptive beamforming algorithms.The main innovative points and contributions of this dissertation are summarized as follows.1.The performance of the sample covariance matrix inversion(SMI)based beamformer is very sensitive to the signal-of-interest's(SOI's)steering vector(SV)mismatch and covariance matrix mismatch,especially when the desired signal is present in the training data.To account this problem,we have proposed a novel interference-plus-noise covariance matrix reconstruction(IPNCM)based robust adaptive beamforming algorithm.Different from the previous IPNCM reconstruction by utilising the Capon spectral estimator integrated over an angular sector,we estimate each interference's SV first,and then reconstruct the IPNCM according to its definition.It means that the proposed method avoids the integration process and has a much lower complexity.To be specific,there are three main steps in estimating the SV of each interference.First,the presumed SV of the interference is determined through the Capon spatial spectrum estimator.Second,we construct the interference subspace by calculating the correlation coefficient between the interference's presumed SV and the sample covariance matrix(SCM)eigenvectors or by using the information theoretic criteria approach.Third,based on the interference's presumed SV and subspace,the iterative robust Capon beamformer(RCB)method is adopted to estimate the actual SV of interference.Moreover,for different application scenarios,three new methods are developed to estimate the SOI's SV.One approach is to estimate the mismatch vector of the desired signal by solving a quadratically constrained quadratic progarmming(QCQP)problem.Moreover,for the other two methods,the SOI's SV is directly calculated by utilising the techniques of the covariance matrix eigendecomposition(ED)and the oracle approximating shrinkage(OAS).Finally,based on the reconstructed IPNCM and the estimated SV of the SOI,three new IPNCM reconstruction-based beamformer's weighting vectors are calculated.Importantly,the proposed beamformers have much lower complexities than the beamforming algorithms,and they are robust against a variety of SV mismatches of both the SOI and interference.2.For non-circular signals,in order to further improve the beamformer's output SINR by exploing the second-order noncircularity of interferences and the SOI,we propose two new augmented interference-plus-noise covariance matrix reconstruction(AIPNCM)based widely linear beamforming algorithms.One is based on the techniques of the iterative adaptive approach(IAA)and the iterative robust Capon beamformer with adaptive uncertainty level(AU-IRCB).Firstly,different from the conventional IAA algorithm,we determine the positions of interferences first and then adopt the IAA algorithm to obtain the spatial spectrum.Secondly,the A-IPNCM is reconstructed based on the acquired spatial spectrum.Thirdly,for noncircular signals,a modified AU-IRCB algorithm is developed to estimate the extended SV of the desired signal.The main advantage of the proposed method is that it can suppress the coherent interferences.For the second algorithm,Different from the A-IPNCM reconstruction in the first method,we adopt a new method to calculate the spatial spectrum of the non-circularity coefficient,and then reconstruct the A-IPNCM by using the Capon spatial spectrum.Meanwhile,three methods are developed to estimate the extended steering vector(ESV)of the desired signal for different application scenarios.One is simultaneously correct the SOI's SV and estimate the SOI's non-circularity coefficient by the proposed iterative quadratically constrained quadratic programming(IQCQP)procedure.For the other two methods,based on the cross correlation between the observation vector and the WL beamformer's output,the SOI's ESV is directly estimated by the modified Rao-Blackwell Ledoit-Wolf(RBLW)estimator and oracle approximating shrinkage(OAS)estimator,respectively.Importantly,the proposed algorithms are robust against large look direction mismatch of the desired signal.3.For the conventional MIMO radar beamformers,they cannot form a flap-top pattern and may not be able to provide sufficient robustness against large look direction mismatch.To account this problem,we propose a new robust beamforming algorithm for multiple-input multiple-output(MIMO)radar based on uncertainty set.Unlike the existing methods,steering vectors(SVs)mismatches are taken into account in the proposed algorithm,which results in a more complicated non-convex problem.To cope with this problem,the non-convex term is linearized first based on the technique of first-order Taylor series expansion.Then,the optimal weight vector is obtained by using the method of semidefinite programming(SDP).Moreover,in order to further reduce the computational complexity,we propose a dimension-reduced algorithm to convert the original non-convex problem into two low dimension SDP problems,and achieve the final solution by using the bi-iterative method.4.Traditional adaptive beamformers are vulnerable to severely degraded in the presence of interference nonstationarity and array steering vector(ASV)mismatch,which often occurred in the situation of the antenna platform motion or propagation channel variability.To solve these problems,we propose a new robust adaptive beamforming algorithm,which can broaden interference nulls.Firstly,we analyze the reasons of traditional algorithm's performance degradation when they in the presence of interference nonstationarity.Secondly,for the proposed algorithm,a new projection matrix with null broadening ability is constructed and then projects the array received data onto the projection matrix.Simulation results demonstrated that the proposed method can effectively broaden the interference nulls.