Adaptive linearly constrained inverse QRD-RLS beamformer for multiple jammers suppression

Abstract

A new general linearly constrained recursive least squares (RLS) array beamforming algorithm, based on an inverse QR decomposition, is developed for multiple jammer suppression. It is known the LS weight vector can be computed without back substitution in the inverse QRD-based algorithms and is suitable to be implemented using the systolic array. Also, the problem of the unacceptable numerical performance in limited-precision environments, occurring in the "fast" RLS filtering algorithms, can be avoided. To document the advantage of this new constrained algorithm, performance, in terms of convergence property of the learning curve and the capability of jammer suppression, is investigated. We show that our proposed algorithm outperforms the LCLMS algorithm and the linearly constraint fast LS algorithm (LCFLS) and its robust version (LCRFSL) algorithm.