An Efficient Dimensional Reduction of the Blocking Matrix for the Multistage Wiener Filter

The multistage Wiener filter (MWF) is a powerful adaptive processing in low sample support environments. Because the MWF solution is provided by a stage-by-stage decomposition, the computational load increases depending on degrees-of-freedom (DOF). In this paper, we propose two efficient approaches by reducing dimensions of the blocking matrix. One approach is to delete some rows of the blocking matrix at the 1st stage, and following stages are calculated by the normal method. The second approach is to delete some rows at all stages consistently. Although the MWF adaptive process of the proposed approaches must be stopped at the optimum stage to avoid a performance degradation, it was solved by applying a simple stopping criterion based on a cross-correlation coefficient. The performance was evaluated by simulation examples, examining the effectiveness.