3,116 research outputs found
Space Time MUSIC: Consistent Signal Subspace Estimation for Wide-band Sensor Arrays
Wide-band Direction of Arrival (DOA) estimation with sensor arrays is an
essential task in sonar, radar, acoustics, biomedical and multimedia
applications. Many state of the art wide-band DOA estimators coherently process
frequency binned array outputs by approximate Maximum Likelihood, Weighted
Subspace Fitting or focusing techniques. This paper shows that bin signals
obtained by filter-bank approaches do not obey the finite rank narrow-band
array model, because spectral leakage and the change of the array response with
frequency within the bin create \emph{ghost sources} dependent on the
particular realization of the source process. Therefore, existing DOA
estimators based on binning cannot claim consistency even with the perfect
knowledge of the array response. In this work, a more realistic array model
with a finite length of the sensor impulse responses is assumed, which still
has finite rank under a space-time formulation. It is shown that signal
subspaces at arbitrary frequencies can be consistently recovered under mild
conditions by applying MUSIC-type (ST-MUSIC) estimators to the dominant
eigenvectors of the wide-band space-time sensor cross-correlation matrix. A
novel Maximum Likelihood based ST-MUSIC subspace estimate is developed in order
to recover consistency. The number of sources active at each frequency are
estimated by Information Theoretic Criteria. The sample ST-MUSIC subspaces can
be fed to any subspace fitting DOA estimator at single or multiple frequencies.
Simulations confirm that the new technique clearly outperforms binning
approaches at sufficiently high signal to noise ratio, when model mismatches
exceed the noise floor.Comment: 15 pages, 10 figures. Accepted in a revised form by the IEEE Trans.
on Signal Processing on 12 February 1918. @IEEE201
DOA Estimation in Partially Correlated Noise Using Low-Rank/Sparse Matrix Decomposition
We consider the problem of direction-of-arrival (DOA) estimation in unknown
partially correlated noise environments where the noise covariance matrix is
sparse. A sparse noise covariance matrix is a common model for a sparse array
of sensors consisted of several widely separated subarrays. Since interelement
spacing among sensors in a subarray is small, the noise in the subarray is in
general spatially correlated, while, due to large distances between subarrays,
the noise between them is uncorrelated. Consequently, the noise covariance
matrix of such an array has a block diagonal structure which is indeed sparse.
Moreover, in an ordinary nonsparse array, because of small distance between
adjacent sensors, there is noise coupling between neighboring sensors, whereas
one can assume that nonadjacent sensors have spatially uncorrelated noise which
makes again the array noise covariance matrix sparse. Utilizing some recently
available tools in low-rank/sparse matrix decomposition, matrix completion, and
sparse representation, we propose a novel method which can resolve possibly
correlated or even coherent sources in the aforementioned partly correlated
noise. In particular, when the sources are uncorrelated, our approach involves
solving a second-order cone programming (SOCP), and if they are correlated or
coherent, one needs to solve a computationally harder convex program. We
demonstrate the effectiveness of the proposed algorithm by numerical
simulations and comparison to the Cramer-Rao bound (CRB).Comment: in IEEE Sensor Array and Multichannel signal processing workshop
(SAM), 201
Blind MultiChannel Identification and Equalization for Dereverberation and Noise Reduction based on Convolutive Transfer Function
This paper addresses the problems of blind channel identification and
multichannel equalization for speech dereverberation and noise reduction. The
time-domain cross-relation method is not suitable for blind room impulse
response identification, due to the near-common zeros of the long impulse
responses. We extend the cross-relation method to the short-time Fourier
transform (STFT) domain, in which the time-domain impulse responses are
approximately represented by the convolutive transfer functions (CTFs) with
much less coefficients. The CTFs suffer from the common zeros caused by the
oversampled STFT. We propose to identify CTFs based on the STFT with the
oversampled signals and the critical sampled CTFs, which is a good compromise
between the frequency aliasing of the signals and the common zeros problem of
CTFs. In addition, a normalization of the CTFs is proposed to remove the gain
ambiguity across sub-bands. In the STFT domain, the identified CTFs is used for
multichannel equalization, in which the sparsity of speech signals is
exploited. We propose to perform inverse filtering by minimizing the
-norm of the source signal with the relaxed -norm fitting error
between the micophone signals and the convolution of the estimated source
signal and the CTFs used as a constraint. This method is advantageous in that
the noise can be reduced by relaxing the -norm to a tolerance
corresponding to the noise power, and the tolerance can be automatically set.
The experiments confirm the efficiency of the proposed method even under
conditions with high reverberation levels and intense noise.Comment: 13 pages, 5 figures, 5 table
Source localization via time difference of arrival
Accurate localization of a signal source, based on the signals collected by a number of receiving sensors deployed in the source surrounding area is a problem of interest in various fields. This dissertation aims at exploring different techniques to improve the localization accuracy of non-cooperative sources, i.e., sources for which the specific transmitted symbols and the time of the transmitted signal are unknown to the receiving sensors. With the localization of non-cooperative sources, time difference of arrival (TDOA) of the signals received at pairs of sensors is typically employed.
A two-stage localization method in multipath environments is proposed. During the first stage, TDOA of the signals received at pairs of sensors is estimated. In the second stage, the actual location is computed from the TDOA estimates. This later stage is referred to as hyperbolic localization and it generally involves a non-convex optimization. For the first stage, a TDOA estimation method that exploits the sparsity of multipath channels is proposed. This is formulated as an f1-regularization problem, where the f1-norm is used as channel sparsity constraint. For the second stage, three methods are proposed to offer high accuracy at different computational costs. The first method takes a semi-definite relaxation (SDR) approach to relax the hyperbolic localization to a convex optimization. The second method follows a linearized formulation of the problem and seeks a biased estimate of improved accuracy. A third method is proposed to exploit the source sparsity. With this, the hyperbolic localization is formulated as an an f1-regularization problem, where the f1-norm is used as source sparsity constraint. The proposed methods compare favorably to other existing methods, each of them having its own advantages. The SDR method has the advantage of simplicity and low computational cost. The second method may perform better than the SDR approach in some situations, but at the price of higher computational cost. The l1-regularization may outperform the first two methods, but is sensitive to the choice of a regularization parameter. The proposed two-stage localization approach is shown to deliver higher accuracy and robustness to noise, compared to existing TDOA localization methods.
A single-stage source localization method is explored. The approach is coherent in the sense that, in addition to the TDOA information, it utilizes the relative carrier phases of the received signals among pairs of sensors. A location estimator is constructed based on a maximum likelihood metric. The potential of accuracy improvement by the coherent approach is shown through the Cramer Rao lower bound (CRB). However, the technique has to contend with high peak sidelobes in the localization metric, especially at low signal-to-noise ratio (SNR). Employing a small antenna array at each sensor is shown to lower the sidelobes level in the localization metric.
Finally, the performance of time delay and amplitude estimation from samples of the received signal taken at rates lower than the conventional Nyquist rate is evaluated. To this end, a CRB is developed and its variation with system parameters is analyzed. It is shown that while with noiseless low rate sampling there is no estimation accuracy loss compared to Nyquist sampling, in the presence of additive noise the performance degrades significantly. However, increasing the low sampling rate by a small factor leads to significant performance improvement, especially for time delay estimation
Rotated Spectral Principal Component Analysis (rsPCA) for Identifying Dynamical Modes of Variability in Climate Systems.
Spectral PCA (sPCA), in contrast to classical PCA, offers the advantage of identifying organized spatiotemporal patterns within specific frequency bands and extracting dynamical modes. However, the unavoidable trade-off between frequency resolution and robustness of the PCs leads to high sensitivity to noise and overfitting, which limits the interpretation of the sPCA results. We propose herein a simple nonparametric implementation of sPCA using the continuous analytic Morlet wavelet as a robust estimator of the cross-spectral matrices with good frequency resolution. To improve the interpretability of the results, especially when several modes of similar amplitude exist within the same frequency band, we propose a rotation of the complex-valued eigenvectors to optimize their spatial regularity (smoothness). The developed method, called rotated spectral PCA (rsPCA), is tested on synthetic data simulating propagating waves and shows impressive performance even with high levels of noise in the data. Applied to global historical geopotential height (GPH) and sea surface temperature (SST) daily time series, the method accurately captures patterns of atmospheric Rossby waves at high frequencies (3-60-day periods) in both GPH and SST and El Niño-Southern Oscillation (ENSO) at low frequencies (2-7-yr periodicity) in SST. At high frequencies the rsPCA successfully unmixes the identified waves, revealing spatially coherent patterns with robust propagation dynamics
Estimation of Severity of Speech Disability through Speech Envelope
In this paper, envelope detection of speech is discussed to distinguish the
pathological cases of speech disabled children. The speech signal samples of
children of age between five to eight years are considered for the present
study. These speech signals are digitized and are used to determine the speech
envelope. The envelope is subjected to ratio mean analysis to estimate the
disability. This analysis is conducted on ten speech signal samples which are
related to both place of articulation and manner of articulation. Overall
speech disability of a pathological subject is estimated based on the results
of above analysis.Comment: 8 pages,4 Figures,Signal & Image Processing Journal AIRC
Bayesian detection of unmodeled bursts of gravitational waves
The data analysis problem of coherently searching for unmodeled
gravitational-wave bursts in the data generated by a global network of
gravitational-wave observatories has been at the center of research for almost
two decades. As data from these detectors is starting to be analyzed, a renewed
interest in this problem has been sparked. A Bayesian approach to the problem
of coherently searching for gravitational wave bursts with a network of
ground-based interferometers is here presented. We demonstrate how to
systematically incorporate prior information on the burst signal and its source
into the analysis. This information may range from the very minimal, such as
best-guess durations, bandwidths, or polarization content, to complete prior
knowledge of the signal waveforms and the distribution of sources through
spacetime. We show that this comprehensive Bayesian formulation contains
several previously proposed detection statistics as special limiting cases, and
demonstrate that it outperforms them.Comment: 18 pages, 3 figures, revisions based on referee comment
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