A Study Of Robust Adaptive Beamforming Under Nonideal Conditions

Adaptive beamforming is an important part of array signal processing which has been widely used in many areas,such as radar,sonar,wireless communication,medical imaging and others.Compared with the data-independent beamformers,adaptive beamformers have better resolution and interference rejection capability.However,the conditions are usually nonideal in practical applications,which will lead to a performance degradation of adaptive beamformers.These nonideal conditions contain nonuniform transmit medium,distorted antenna and the unknown statistical distribution of the samples.Therefore,the beamformers must be valid and robust under different conditions.However,all of proposed robust adaptive beamformers have drawbacks.In this dissertation,focusing on the problems that the performances of many robust beamformers degrade dramatically if large steering vector mismatch occurs,and the beamformers based on the minimum variance criterion is not statistically optimal under non-Gaussian signal assumption,novel robust beamforming methods have been proposed.In addition,applying the robust beamforming methods in array signal processing into the space-time adaptive processing(STAP),a robust STAP method has been proposed.The main contributions and innovative points of this dissertation can be briefly summarized as follows:1.Focused on the problem that traditional robust beamformers based on one uncertainty set suffer from difficulty in selecting an appropriate size of the uncertainty set,and their performances degrade dramatically if large steering vector mismatch occurs,a robust adaptive beamformer using multiple uncertainty sets has been proposed.Multiple small uncertainty sets have been utilized,instead of using a single large set,to cover the whole large uncertainty region.The original problem is nonlinear and nonconvex.To tackle this problem,two solutions have been devoted.The former method first recasts the original problem in high dimension,then the problem is tackled with semidefinite relaxation(SDR)by dropping the rank-one constraint.Finally,calculate the weight vector of the beamformer iteratively.Another method uses auxiliary variables.This method does not impose the zero constraint on the imaginary part of the array response at the desired direction.Firstly,introduce auxiliary variables and utilize the SDR technique to reformulate the problem.Then,convert the nonlinear term to a linear one by using first-order Taylor approximation.Therefore,the original nonconvex problem has been transformed to a convex one.The optimal Taylor expansion point is obtained by using iterative approach.The proposed method offers a significant performance improvement in case of large steering vector mismatch.2.Focused on the problems that the beamformers based on the minimum variance(MV)criterion is not statistically optimal under non-Gaussian signal assumption,and large steering vector mismatch occurs,a robust beamformer based on minimum dispersion variance distortionless is proposed.This method minimizes the dispersion of the output of the array and utilizes multiple small uncertainty sets.The?_p-normminimization problem turns to be nonconvex because of these multi-uncertainty-set constraints.To solve the problem,a projected gradient algorithm is utilized to transform the original problem into a nonconvex quadratically constrained quadratic programming problem.In each iteration,introduce an auxiliary variable firstly and recast the problem in high dimension.Then,use the semidefinite programming relaxation technique to deal with the transformed nonconvex quadratic programming problem.The proposed beamformer offers a significant performance improvement in case of large steering vector mismatch for non-Gaussian signals compared with the conventional schemes.3.Focused on the problem that a supper-low-attitude target results in target detection performance loss or target tracking error due to the secondary interaction between the moving and environment clutter,applying the robust beamforming methods in array signal processing into STAP,a robust STAP method based on an optimized joint magnitude and phase constraints in the temporal domains is proposed.First,the general radar signal model under the supper-low-attitude target environment is presented.Then,the Doppler spread characteristics caused by the supper-low-attitude target and its false image counterpart are analysed.With joint magnitude and phase constraints imposed on and around the target,the mainlobe is well maintained and the performance loss due to Doppler spread is avoided.The robustness and detection performance is significantly improved with the proposed method.