17 research outputs found
Efficient Covariance Matrix Reconstruction with Iterative Spatial Spectrum Sampling
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 approachesComment: 14 pages, 8 figure
Study of Robust Adaptive Beamforming Algorithms Based on Power Method Processing and Spatial Spectrum Matching
Robust adaptive beamforming (RAB) based on interference-plus-noise covariance
(INC) matrix reconstruction can experience performance degradation when model
mismatch errors exist, particularly when the input signal-to-noise ratio (SNR)
is large. In this work, we devise an efficient RAB technique for dealing with
covariance matrix reconstruction issues. The proposed method involves INC
matrix reconstruction using an idea in which the power and the steering vector
of the interferences are estimated based on the power method. Furthermore,
spatial match processing is computed to reconstruct the desired
signal-plus-noise covariance matrix. Then, the noise components are excluded to
retain the desired signal (DS) covariance matrix. A key feature of the proposed
technique is to avoid eigenvalue decomposition of the INC matrix to obtain the
dominant power of the interference-plus-noise region. Moreover, the INC
reconstruction is carried out according to the definition of the theoretical
INC matrix. Simulation results are shown and discussed to verify the
effectiveness of the proposed method against existing approaches.Comment: 7 pages, 2 figure