  In this paper, a novel and robust algorithm is proposed for adaptive
beamforming based on the idea of reconstructing the autocorrelation sequence
(ACS) of a random process from a set of measured data. This is obtained from
the first column and the first row of the sample covariance matrix (SCM) after
averaging along its diagonals. Then, the power spectrum of the correlation
sequence is estimated using the discrete Fourier transform (DFT). The DFT
coefficients corresponding to the angles within the noise-plus-interference
region are used to reconstruct the noise-plus-interference covariance matrix
(NPICM), while the desired signal covariance matrix (DSCM) is estimated by
identifying and removing the noise-plus-interference component from the SCM. In
particular, the spatial power spectrum of the estimated received signal is
utilized to compute the correlation sequence corresponding to the
noise-plus-interference in which the dominant DFT coefficient of the
noise-plus-interference is captured. A key advantage of the proposed adaptive
beamforming is that only little prior information is required. Specifically, an
imprecise knowledge of the array geometry and of the angular sectors in which
the interferences are located is needed. Simulation results demonstrate that
compared with previous reconstruction-based beamformers, the proposed approach
can achieve better overall performance in the case of multiple mismatches over
a very large range of input signal-to-noise ratios.
