Efficient Covariance Matrix Reconstruction with Iterative Spatial Spectrum Sampling

TL;DR

This paper introduces an efficient covariance matrix reconstruction method using iterative spatial spectrum sampling for robust adaptive beamforming, improving interference suppression with reduced computational cost.

Contribution

It proposes a novel covariance matrix reconstruction technique based on maximum entropy spectral density and an adaptive algorithm for weight updates, enhancing beamforming robustness.

Findings

Effective suppression of interferers near the signal of interest

Reduced computational complexity compared to existing methods

Simulation results confirm improved beamforming performance

Abstract

This work presents a cost-effective technique for designing robust adaptive beamforming algorithms based on efficient covariance matrix reconstruction with iterative spatial power spectrum (CMR-ISPS). The proposed CMR-ISPS approach reconstructs the interference-plus-noise covariance (INC) matrix based on a simplified maximum entropy power spectral density function that can be used to shape the directional response of the beamformer. Firstly, we estimate the directions of arrival (DoAs) of the interfering sources with the available snapshots. We then develop an algorithm to reconstruct the INC matrix using a weighted sum of outer products of steering vectors whose coefficients can be estimated in the vicinity of the DoAs of the interferences which lie in a small angular sector. We also devise a cost-effective adaptive algorithm based on conjugate gradient techniques to update the beamforming weights and a method to obtain estimates of the signal of interest (SOI) steering vector from the spatial power spectrum. The proposed CMR-ISPS beamformer can suppress interferers close to the direction of the SOI by producing notches in the directional response of the array with sufficient depths. Simulation results are provided to confirm the validity of the proposed method and make a comparison to existing approaches