Fast and Accurate Rank Selection Methods for Multistage Wiener Filter

摘要

Reduced-rank adaptive processing (RRAP) has received considerable attention in recent years. One of the key problems with this technique is how to determine an appropriate dimension for the reduced-rank subspace. In this paper we study in detail four types of rank selection methods for the widely used RRAP approach—multistage Wiener filter (MWF). All of these methods have a computational complexity of order , compared with other existing methods with an order of or even at the th stage of the MWF. The main idea underlying these methods is to find a recursive algorithm to calculate the stopping criterion. The first two algorithms, based on the generalized discrepancy principle (GDP) and error estimation (EE) respectively, are fast versions of existing approaches. The last two algorithms, based on fast Ritz values estimation (RVE) and new information (NI) respectively, are new and proposed in this paper. In addition to requiring less computation, simulation results show that the proposed algorithms have a higher accuracy in adaptive beamforming applications for both narrowband and broadband signals.