Robust Adaptive Beamforming Based on Low-Rank and Cross-Correlation Techniques
Hang Ruan,Rodrigo C. de Lamare +1 more
TLDR
This paper presents cost-effective low-rank techniques for designing robust adaptive beamforming algorithms based on the exploitation of the cross-correlation between the array observation data and the output of the beamformer, resulting in the proposed orthogonal Krylov subspace projection mismatch estimation (OKSPME) method.Abstract:
This paper presents cost-effective low-rank techniques for designing robust adaptive beamforming (RAB) algorithms. The proposed algorithms are based on the exploitation of the cross-correlation between the array observation data and the output of the beamformer. First, we construct a general linear equation considered in large dimensions whose solution yields the steering vector mismatch. Then, we employ the idea of the full orthogonalization method (FOM), an orthogonal Krylov subspace based method, to iteratively estimate the steering vector mismatch in a reduced-dimensional subspace, resulting in the proposed orthogonal Krylov subspace projection mismatch estimation (OKSPME) method. We also devise adaptive algorithms based on stochastic gradient (SG) and conjugate gradient (CG) techniques to update the beamforming weights with low complexity and avoid any costly matrix inversion. The main advantages of the proposed low-rank and mismatch estimation techniques are their cost-effectiveness when dealing with high-dimension subspaces or large sensor arrays. Simulations results show excellent performance in terms of the output signal-to-interference-plus-noise ratio (SINR) of the beamformer among all the compared RAB methods.read more
Citations
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Journal ArticleDOI
Robust Adaptive Beamforming Based on Conjugate Gradient Algorithms
TL;DR: This paper derives a diagonal loading CGLS algorithm (CG applied to normal equations) and proposes a simple method to choose the loading level based on a coarse estimation of the desired signal power, which can effectively reduce the signal self-cancellation at high signal-to-noise ratio.
Journal ArticleDOI
Robust Adaptive Beamforming via Simplified Interference Power Estimation
TL;DR: Simulation results demonstrate that the overestimation of interference powers hardly degrades the performance of adaptive beamforming, and the proposed algorithm achieves nearly optimal performance across a wide range of signal-to-noise ratios.
Journal ArticleDOI
New Designs on MVDR Robust Adaptive Beamforming Based on Optimal Steering Vector Estimation
TL;DR: In this paper, the robust adaptive beamforming design problem based on estimation of the signal-of-interest (SOI) steering vector is considered, and a beamformer output power maximization problem is formulated and solved subject to a double-sided norm perturbation constraint, a similarity constraint, and an inhomogeneous constraint that guarantees that the direction of arrival (DOA) of the SOI is away from the DOA region of all linear combinations of the interference steering vectors.
Journal ArticleDOI
Maximum Entropy-Based Interference-Plus-Noise Covariance Matrix Reconstruction for Robust Adaptive Beamforming
TL;DR: A new low-complexity RAB approach based on interference-plus-noise covariance matrix (IPNC) reconstruction and steering vector (SV) estimation is proposed, which can provide superior performance to several previously proposed beamformers.
Journal ArticleDOI
Distributed Robust Beamforming Based on Low-Rank and Cross-Correlation Techniques: Design and Analysis
Hang Ruan,Rodrigo C. de Lamare +1 more
TL;DR: The proposed RDB approach mitigates the effects of channel errors in wireless networks equipped with relays based on the exploitation of the cross-correlation between the received data from the relays at the destination and the system output and low-rank techniques.
References
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