1,401 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
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
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
Deep Learning for Environmentally Robust Speech Recognition: An Overview of Recent Developments
Eliminating the negative effect of non-stationary environmental noise is a
long-standing research topic for automatic speech recognition that stills
remains an important challenge. Data-driven supervised approaches, including
ones based on deep neural networks, have recently emerged as potential
alternatives to traditional unsupervised approaches and with sufficient
training, can alleviate the shortcomings of the unsupervised methods in various
real-life acoustic environments. In this light, we review recently developed,
representative deep learning approaches for tackling non-stationary additive
and convolutional degradation of speech with the aim of providing guidelines
for those involved in the development of environmentally robust speech
recognition systems. We separately discuss single- and multi-channel techniques
developed for the front-end and back-end of speech recognition systems, as well
as joint front-end and back-end training frameworks
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