582 research outputs found
Interference-plus-Noise Covariance Matrix Reconstruction via Spatial Power Spectrum Sampling for Robust Adaptive Beamforming
Recently, a robust adaptive beamforming (RAB) technique based on interference-plus-noise covariance (INC) matrix reconstruction has been proposed, which utilizes the Capon spectrum estimator integrated over a region separated from the direction of the desired signal. Inspired by the sampling and reconstruction idea, in this paper, a novel method named spatial power spectrum sampling (SPSS) is proposed to reconstruct the INC matrix more efficiently, with the corresponding beamforming algorithm developed, where the covariance matrix taper (CMT) technique is employed to further improve its performance. Simulation results are provided to demonstrate the effectiveness of the proposed method
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
URGLQ: An Efficient Covariance Matrix Reconstruction Method for Robust Adaptive Beamforming
The computational complexity of the conventional adaptive beamformer is
relatively large, and the performance degrades significantly due to both the
model mismatch errors and the unwanted signals in received data. In this paper,
an efficient unwanted signal removal and Gauss-Legendre quadrature
(URGLQ)-based covariance matrix reconstruction method is proposed. Different
from the prior covariance matrix reconstruction methods, a projection matrix is
constructed to remove the unwanted signal from the received data, which
improves the reconstruction accuracy of the covariance matrix. Considering that
the computational complexity of most matrix reconstruction algorithms are
relatively large due to the integral operation, we proposed a Gauss-Legendre
quadrature-based method to approximate the integral operation while maintaining
the accuracy. Moreover, to improve the robustness of the beamformer, the
mismatch in the desired steering vector is corrected by maximizing the output
power of the beamformer under a constraint that the corrected steering vector
cannot converge to any interference steering vector. Simulation results and
prototype experiment demonstrate that the performance of the proposed
beamformer outperforms the compared methods and is much closer to the optimal
beamformer in different scenarios.Comment: 11 pages, 16 figure
Robust Adaptive Beamforming Using k-means Clustering: A Solution to High Complexity of the Reconstruction-Based Algorithm
Recently, a new robust adaptive beamforming (RAB) algorithm has been proposed to reconstruct the interference-plus-noise covariance matrix (IPNCM) based on narrowing the interference angular domain and using an annular uncertainty set (NIAD-AUS). The method is robust against unknown arbitrary-type mismatches. However, its computational complexity will increase exponentially with the number of array sensors. In this paper, a novel method is proposed to solve this problem. First, k-means clustering (KMC) algorithm is utilized to estimate the annulus uncertainty set with fewer clustering weight points rather than whole sampling. Second, the KMC Capon spectrum is used to reconstruct the IPNCM. Compared with the previous reconstruction-based algorithms, the proposed approach can retain the high performance of the state-of-the-art NIAD-AUS algorithm. More importantly, it can also obtain the IPNCM more quickly. Lastly, simulation results demonstrate the effectiveness and robustness of the proposed algorithm
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