Robust Adaptive Beamforming Based On Interference Noise Covariance Matrix Reconstruction

In the rapid development of communication and information technology,robust adaptive beamforming technology has been widely used in radar,sonar,medical imaging,radio astronomy,wireless communication and so on,and has become a hot topic in the field of communication.At the same time,the increasingly updated communication environment also makes more demanding requirements for the robustness of information processing.In order to improve the robustness of the algorithm,this paper mainly studies and discusses the performance of robust adaptive beamforming algorithm from two aspects:interference noise covariance matrix reconstruction and steering vector estimation.Firstly,the basic knowledge,the history and present situation of array processing,the mathematical model of robust adaptive beamforming and the Capon algorithm and MUSIC algorithm are introduced.Then,using the Matlab simulation the noise matrix reconstruction algorithm of common beam pattern,SNR and snapshot number and output signal to noise ratio of the relationship between customers comparing and analyzing their advantages and disadvantages,it is concluded that the factors affecting the performance of the algorithm and reduce the reason.In the factors of steering vector mismatch and the number of snapshots and little environment,developed a robust adaptive beam interference noise covariance matrix estimation using MUSIC algorithm and parameters to reconstruct the beam forming algorithm,the adaptive formation of the main idea of the algorithm is to calculate the noise covariance matrix does not include the desired signal in the pass when using the MUSIC algorithm and the parameter estimation of knowledge,for the steering vector of the desired signal correlation matrix is derived.Then,use feature hypothesis steering vector and the signal subspace in value to calculate.Orthogonal projection reconstruction algorithm is derived in this paper analysis algorithm of beam simulation diagram of the algorithm have higher 2° precision,and the precision of steering vector estimation algorithm to reconstruct the same,but the complexity of the algorithm is better than the steering vector estimation is an order of magnitude lower algorithm.In the simulation output of SINR varies with the SNR above analysis,SINR output and ideal results of the algorithm is only about 0.3 dB,compared with the other two algorithms in this paper,the output of SINR algorithm is higher,and varies with the SNR output SINR will not produce large deviations and ideal results,and the other two algorithms with large difference in the ideal output SINR output SINR under the condition of higher.In the diagram of simulation output SINR and snapshot change points can be obtained by the analysis of the output of the SINR orthogonal projection reconstruction algorithm is low,the output SINR output SINR algorithm with the ideal algorithm are not quite,When the snapshot points is twenty can be seenthe output SINR algorithm just below the reconstruction algorithm to estimate the steering vector,but the algorithm complexity is lower than that of the steering vector estimation algorithm to reconstruct an order of magnitude lower.Therefore,the overall performance of the algorithm is higher than other reconstruction algorithms in this paper.Additionally,this paper presents a new algorithm for the estimation of the steering vector with less prior information,which only needs to estimate the angle of the desired signal to estimate the direction of the desired signal.The output of SINR simulation with SNR and the variation of points can be obtained in the snapshot,the output of SINR algorithm is higher about 6 dB compared with the other algorithms,and the output of the algorithm and the SINR algorithm of the ideal output SINR size is maintained at about 0.4 dB,which is more robust,so the performance of the algorithm is higher than that of the other algorithm.