Research On Interference Suppression Methods Based On Robust Beamforming

Adaptive beamforming,as a significant branch of array signal processing,adaptively changes the array weight vector according to the environment to enhance the desired signal in a specific direction of arrival,while suppressing interference and noise.In practice,guidance vector estimation errors and covariance matrix uncertainty can affect the performance of adaptive beamformer.Therefore,robust adaptive beamforming algorithms have been a hot research topic for scholars.Most traditional robust adaptive beamforming algorithms use the array output signal to interference noise ratio as a performance indicator,but when optimizing the output signal to interference noise ratio,compromises usually occur in interference suppression,resulting in some interference reaching the output through the beamformer.In some application scenarios where directional interference needs to be eliminated,this can cause serious consequences.Therefore,it is necessary to study interference suppression methods based on robust beamforming.In order to ensure the robustness of beamforming algorithms while better achieving interference suppression,the following research has been carried out in this thesis:Firstly,a reconstruction method of Covariance matrix based on oblique projection is proposed.The Capon beamforming algorithm is used to estimate the interference signal and reconstruct the covariance matrix of the interference signal within its angular range.This method is suitable for scenarios with strong interference and weak SOI.Then,the subspace projection method is used to reconstruct the expected signal covariance matrix.By constructing an oblique projection operator,the sample covariance matrix is projected into the desired signal subspace.Thus,all interference components in the sample covariance matrix are eliminated,while the components of the desired signal are fully preserved,making the reconstruction of the desired signal covariance matrix more accurate.Then,based on the maximum signal-to-noise ratio criterion,an optimization problem under interference suppression constraints was proposed.Limiting the weight vector to the orthogonal complementary space of the interference signal,we strive to maximize the output signal-to-noise ratio of the beamformer,thereby ensuring maximum interference suppression.On the basis of the traditional Lagrange multiplier method,the generalized positive semi definite feature decomposition method is used to solve the optimization problem.Through the above methods,the complexity of solving optimization problems is reduced to a certain extent.Finally,the interference suppression algorithm based on robust beamforming proposed in the thesis is simulated and analyzed under three typical error scenarios.Compared with other robust adaptive beamforming algorithms under the same conditions,the results suggest that the algorithm proposed in the thesis exhibits significant advantages in interference suppression.In most simulation cases,the output signal-tointerference ratio performance of the algorithm proposed in the thesis is superior to similar algorithms.Especially in the simulation of incident angle error,compared to traditional methods,the output signal-to-interference ratio of the algorithm proposed in the thesis is improved by 20 d B.It exhibits good output performance while possessing good algorithm robustness.