Adaptive beam-forming algorithm with iterative subspace tracking and structural constraint

The maximum output signal to interference plus noise ration (SINR) can be achieved as weighted the array data’s by adopting adaptive beam-forming (ABF) algorithm. Therefore, the deviation between the estimated sample matrix and the ideally correlation matrix will deteriorate the performance of the linearly constrained minimum variance (LCMV) beam-former. To alleviate this decreasing in output SINR with secondary data deficient scenario and channel mismatch coexistence, a new method based on iterative subspace tracking and structural constraint is presented. The approach is performed in two stages. First, we employ clearing operation at training data set to calculate each principal eigenvector in sequentially, then, the adaptive beam-forming algorithm based on subspace projection can be obtained by constraining its weight vector to a specific form. The high output signal to interference plus noise ration of subspace processing is remained and the robustness against small sample support and in the presence of channel mismatch is improved. Numerical simulation indicate that its performance is better than that of several well-known adaptive beam-former.