629 research outputs found
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 a Bayesian steering vector error model
We propose a Bayesian approach to robust adaptive beamforming which entails considering the steering vector of interest as a random variable with some prior distribution. The latter can be tuned in a simple way to reflect how far is the actual steering vector from its presumed value. Two different priors are proposed, namely a Bingham prior distribution and a distribution that directly reveals and depends upon the angle between the true and presumed steering vector. Accordingly, a non-informative prior is assigned to the interference plus noise covariance matrix R, which can be viewed as a means to introduce diagonal loading in a Bayesian framework. The minimum mean square distance estimate of the steering vector as well as the minimum mean square error estimate of R are derived and implemented using a Gibbs sampling strategy. Numerical simulations show that the new beamformers possess a very good rate of convergence even in the presence of steering vector errors
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
Quadratically Constrained Beamforming Robust Against Direction-of-Arrival Mismatch
It is well known that the performance of the minimum variance distortionless response (MVDR) beamformer is very sensitive to steering vector mismatch. Such mismatches can occur as a result of direction-of-arrival (DOA) errors, local scattering, near-far spatial signature mismatch, waveform distortion, source spreading, imperfectly calibrated arrays and distorted antenna shape. In this paper, an adaptive beamformer that is robust against the DOA mismatch is proposed. This method imposes two quadratic constraints such that the magnitude responses of two steering vectors exceed unity. Then, a diagonal loading method is used to force the magnitude responses at the arrival angles between these two steering vectors to exceed unity. Therefore, this method can always force the gains at a desired range of angles to exceed a constant level while suppressing the interferences and noise. A closed-form solution to the proposed minimization problem is introduced, and the diagonal loading factor can be computed systematically by a proposed algorithm. Numerical examples show that this method has excellent signal-to-interference-plus-noise ratio performance and a complexity comparable to the standard MVDR beamformer
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