Research On Robust Beamforming Algorithm Based On Interference-plus-noise Covariance Matrix Reconstruction

Beamforming based on interference-plus-noise covariance matrix reconstruction is a novel beamforming method in recent years, which has the advantages of good robustness and fast convergence speed. Therefore, it is a hot spot of recent studies on beamforming algorithm. Although there are many achievement of the robust beamforming based on interference-plus-noise covariance matrix reconstruction to be proposed, they still have some shortcomings, such as low precision and poor robustness. For the shortcoming, two new algorithms are proposed in this thesis. The first algorithm is a robust beamforming algorithms based on fully automatic estimation of interference covariance matrix reconstruction in uniform linear array. The second one is a improved robust beamforming algorithms in nested array. Simulation results demonstrate both the algorithms can improve the performance of beamforming algorithm. The main research content is summarized as follows:1. Introduce the mathematical model of the uniform linear array and the nested array. By theoretical derivation and simulation analysis of the classic minimum variance distortionless response(MVDR) beamforming algorithm, we can get a conclusion that MVDR beamforming algorithm do not have robustness, and we analyze the influence of system error on the beamforming performance. By theoretical derivation and simulation analysis of several classic robust beamforming algorithms, it make sure that beamforming algorithms based on interference covariance matrix reconstruction is a ideal method to improve performance of beamforming algorithm.2. Propose a robust beamforming algorithms based on fully automatic estimation of interference-plus-noise covariance matrix reconstruction in uniform linear array. Analyze the method of improving accuracy of the interference-plus-noise covariance matrix reconstruction and discover that performance of the existing beamforming algorithms based on interference-plus-noise covariance matrix reconstruction will descend in small number of snapshots or variable error of the steering vector case. For this problem, the proposed algorithm firstly correct the steering vector via the Capon algorithm based on uncertain set constraints and method of selecting constraint parameter is given. Then, a new interference-plus-noise covariance matrix can be reconstructed base on both the corrected value and a new beamforming algorithms is obtained to combined the traditional beamforming algorithm with the above. The advantage of the algorithm is that using less prior information automatically correct interference-plus-noise covariance matrix. The simulation analysis shows that the proposed beamforming algorithm can effectively improve beamforming robustness.3. An improved robust beamforming algorithms is proposed in nested array. Analyze the method of beamforming in nested array and point out that spatial smoothing matrix containing the desired signal is the main reason why beamforming algorithm in nested array is sensitive to the steering vector mismatch. For this reason, The proposed algorithm firstly build interference subspace via the vectorized coherent matrix. Then, interference-plus-noise covariance matrix is reconstructed by projecting the vectorized spatially smoothed matrix into interference subspace and with the reconstructed interference-plus-noise covariance matrix, the steering vector of the desired signal can be estimated by solving a optimization function. Finally, combine with the traditional beamforming algorithm, a new beamforming is performed. The advantage of the algorithm is making full use of the spatial degrees of freedom provided by the nested array to improve the accuracy of reconstruction matrix. simulation results demonstrate the proposed algorithm can not only deal with more signals but also obtain better robustness.