Robust Adaptive Beamforming Based on Low-Complexity Discrete Fourier Transform Spatial Sampling

Abstract

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-plusinterference 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-plusinterference 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.

Robust Mainlobe Interference Suppression Based on Joint Oblique Projection for Dual Polarization Conformal Array

“…Robust adaptive beamforming (RAB) has vital importance for ensuring signal receiving quality. It was analysed in depth and improved by interference-plus-noise covariance matrix (IPNC) reconstruction in many papers [13][14][15][16][17][18][19][20]. In [13], the IPNC and desired signal covariance matrix are reconstructed by estimating all interferences and desired signal power using the principle of maximum entropy power spectrum (MEPS).…”

“…Literature [14] reconstructs IPNC by using low-complexity spatial sampling process and virtual received array vector. Te method in [15] constructs the IPNC based on the idea of using discrete Fourier transform (DFT) to estimate the correlated sequence power spectrum.…”

Maximum Entropy-Based Interference-Plus-Noise Covariance Matrix Reconstruction for Robust Adaptive Beamforming

“…In addition, the accuracy of the Capon spatial spectrum degrades severely when coherent signals (with line spectra) exist [46]. To avoid this problem, a recent efficient robust adaptive beamforming technique has been introduced in [47] based on the autocorrelation sequence of a random process in which the INC matrix is reconstructed directly and without the need to estimate the power of the interferers and their arrays. In [31], an algorithm that employs the gradient vector and the INC matrix reconstruction by estimating the interference steering vectors and their powers is proposed.…”