Least-squares regenerative hybrid array for adaptive beamforming

The least-squares method is applied to adaptive beamforming in a regenerative hybrid array that utilizes both the steering vector and reference signal acquired from the array output to preserve the desired signal. It is shown that like the gradient-search-based regenerative hybrid array, the proposed array converges to the steady state of the Applebaum-type array adapted without the desired signal present. The array transient behavior is simulated with weights updated by the QR decomposition algorithm. Results show that the least-squares regenerative hybrid array converges much faster than the original regenerative hybrid array on which it is based. Simulations of the steady-state performance show that the regenerative hybrid array performs better than high-order derivative constraint arrays