1,721,039 research outputs found
Optimal quiescent vectors for wideband ML beamforming in reverberant fields
No full textIn this work, the behavior of the recently developed wideband maximum likelihood steered beamformer (ML-STBF) is analyzed under multipath conditions. In particular, the importance of an optimal choice of the quiescent vector to initialize the ML-STBF is pointed out. Some quiescent vectors, optimized on the basis of the previous analysis, are developed for the joint use with the ML-STBF and compared in simulation. It is shown that their use can significantly improve the output signal-to-noise ratio, the multipath suppression capability and the robustness with respect to focusing errors of the ML-STBF. © 2004 Elsevier B.V. All rights reserved
Asymptotically perfect wideband focusing of multi-ring circular arrays
No full textThe concept of coherent focusing of sensor arrays, introduced by Wang and Kaveh, led to the development of high-performance and computationally efficient algorithms for wideband direction finding and beamforming. Nonetheless, the quality of focusing depends on the understanding and the proper exploitation of specific array manifold properties. Circular arrays exhibit uniform (isotropic) performance over the entire azimuthal range and allow the use of fast algorithms, originally developed for uniform linear arrays, by decomposing the manifold into circular harmonics. Coherent wideband focusing of circular arrays suffers from ambiguities, noise warping, and numerical ill conditioning. In this work, it is shown that a fast orthonormal beamspace transformation combining the responses of two concentric rings can perfectly focus wideband sources at all azimuth angles in the circular harmonic domain in the limit of infinite number of sensors. The information loss after focusing is minimized through an analytical approach. The proposed focusing scheme is computationally very efficient and can be directly extended to multiring circular arrays that are arranged into a set of nested two-ring subarrays to cover very large bandwidths
WAVES: Weighted average of signal subspaces for robust wideband direction finding
No full textExisting algorithms for wideband direction finding are mainly based on local approximations of the Gaussian log-likelihood around the true directions of arrival (DOAs), assuming negligible array calibration errors. Suboptimal and costly algorithms, such as classical or sequential beamforming, are required to initialize a local search that eventually furnishes DOA estimates. This multistage process may be nonrobust in the presence of even small errors in prior guesses about angles and number of sources generated by inherent limitations of the preprocessing and may lead to catastrophic errors in practical applications. In this paper, a new approach to wideband direction finding is introduced and described. The proposed strategy combines a robust near-optimal data-adaptive statistic, called the weighted average of signal subspaces (WAVES), with an enhanced design of focusing matrices to ensure a statistically robust preprocessing of wideband data. The overall sensitivity of WAVES to various error sources, such as imperfect array focusing, is also reduced with respect to traditional CSSM algorithms, as demonstrated by extensive Monte Carlo simulations
Robust ML wideband beamforming in reverberant fields
No full textAdaptive beamforming of sensor arrays immersed into reverberant fields can easily result in the cancellation of the useful signal because of the temporal correlation existing among the direct and the reflected path signals. Wideband beamforming can somewhat mitigate this phenomenon, but adaptive solutions based on the minimum variance (MV) criterion remain nonrobust in many practical applications, such as multimedia systems, underwater acoustics, and seismic prospecting. In this paper, a steered wideband adaptive beamformer, optimized by a novel concentrated maximum likelihood (NIL) criterion in the frequency domain, is presented and discussed in the light of a very general reverberation model. It is shown that NIL beamforming can alleviate the typical cancellation problems encountered by adaptive MV beamforming and preserve the intelligibility of a wideband and colored source signal under interference, reverberation, and propagation mismatches. The difficult optimization of the NIL cost function, which incorporates a robustness constraint to prevent signal cancellation, is recast as an iterative least squares problem through the concept of descent in the neuron space, which was originally developed for the training of multilayer neural networks. Finally, experiments with computer-generated and real-world data demonstrate the superior performance of the proposed beamformer with respect to its MV counterpart
Space-Time Signal Subspace Estimation for wide-band acoustic arrays
No full textAcoustic array applications are generally characterized by
very large signal bandwidth. Most existing wide-band
direction of arrival (DOA) estimators are based on
binning in the frequency domain, so that within each bin
the signal model is considered approximately narrow-
band. In this work the basic inconsistency of the
commonly used binning is first shown. It is shown
that the
recent Space Time MUSIC (ST-MUSIC) method, which
estimates a set of narrow-band signal subspaces directly
from the space-time array covariance and combines them
within a Weighted Subspace Fitting paradigm, can restore
wide-band DOA estimation consistency in most scenarios,
obtaining a large variance improvement at high signal to
noise ratio (SNR). In addition, a refined ST-MUSIC
subspace weighting is proposed to improve accuracy,
especially at low SNR
Image Quality Assessment Based on Detail Differences
Full text linkThis paper presents a novel Full Reference method for
image quality assessment based on two indices measuring
respectively detail loss and spurious detail addition. These
indices define a two dimensional (2D) state in a Virtual
Cognitive State (VCS) space. The quality estimation is
obtained as a 2D function of the VCS, empirically
determined via polynomial fitting of DMOS values of
training images. The method provides at the same time
highly accurate DMOS estimates, and a quantitative
account of the causes of quality degradation
Enhancement of time delay estimation in reverberant environments by signal prefiltering
No full textSpatial diversity is an important issue in many practical applications, such as acoustic source localization, audio signal enhancement, speech recognition in the presence of interference. In particular, source localization by time delay estimation and triangulation represents a popular class o approaches. In this work, the basic time delay estimation technique via the generalized crosscorrelation function and an improved version using cepstral analysis are briefly reviewed. Then a novel approach is proposed, which exploits the physics of sound propagation in closed environments. Specifically, the common acoustical mode approximation of room transfer functions is udes to devise a simple and effective prefiltering technique. Finally, the performance of all proposed solutions is compared at different reverberation levels
Prefiltering approaches for time delay estimation in reverberant environments
No full textIn recent years much interest has been focused on time delay estimation in reverberant environments, for a variety of practical purposes. Algorithms based on generalized cross correlation are commonly used, but show clear limitations even in the presence of low reverberation levels. More sophisticated approaches, like cepstral prefiltering, have been proposed, but they can be computationally expensive and inadequate for real-time applications. In this paper a novel prefiltering approach, based on the common acoustical pole modeling of room transfer functions, is described and compared to existing techniques. Experimental tests show its effectiveness in combating reverberation while maintaining the simplicity requirement needed in many practical situations
Application of the block recursive least squares algorithm to adaptive neural beamforming
No full textSpatial beamforming using a known training sequence is a well-understood technique for canceling uncorrelated interferences from telecommunication signals. Most of on-line adaptive beamforming algorithms are based on linear algebra and linear signal models. Anyway both in the transmitter amplifier and in the array receiver nonlinearities may arise, producing distorted waveforms and reducing the performance of the demodulation process. A nonlinear spatial beamformer with sensor arrays may use a neural network to cope with communication system nonlinearities. In this work we show that a feedforward neural network trained with a LS-based algorithm may get the convergence in a time suitable to most applications
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