Efficient Covariance Matrix Reconstruction with Iterative Spatial Spectrum Sampling
This work addresses interference suppression in beamforming for applications like radar or communications, but it appears incremental as it builds on existing covariance matrix reconstruction methods.
The paper tackles robust adaptive beamforming by proposing a cost-effective covariance matrix reconstruction technique with iterative spatial spectrum sampling (CMR-ISPS), which suppresses interferers near the signal of interest by creating notches in the array's directional response, as validated through simulations.
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