Adaptive linearly constrained inverse QRD-RLS beamforming algorithm for moving jammers suppression

In this paper, a general, linearly constrained (LC) recursive least squares (RLS) array-beamforming algorithm, based on an inverse QR decomposition, is developed for suppressing the moving jammers, efficiently. In fact, by using the inverse QR decomposition-recursive least squares (QRD-RLS) algorithm approach, the lease-squares (LS) weight vector can be computed without back substitution and is suitable to be implemented using the systolic array to achieve fast convergence and good numerical properties. The merits of this new constrained algorithm is verified by evaluating the performance, in terms of the learning curve, to investigate the convergence property and numerical efficiency, and the output signal to interference and noise ratio. We show that our proposed algorithm outperforms the conventional linearly constrained LMS (LCLMS) algorithm, and the one using the fast linear constrained RLS algorithm and its modified version.