2,106 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
Array signal processing robust to pointing errors
The objective of this thesis is to design computationally efficient DOA (direction-of-
arrival) estimation algorithms and beamformers robust to pointing errors, by
harnessing the antenna geometrical information and received signals. Initially,
two fast root-MUSIC-type DOA estimation algorithms are developed, which can
be applied in arbitrary arrays. Instead of computing all roots, the first proposed
iterative algorithm calculates the wanted roots only. The second IDFT-based
method obtains the DOAs by scanning a few circles in parallel and thus the
rooting is avoided. Both proposed algorithms, with less computational burden,
have the asymptotically similar performance to the extended root-MUSIC.
The second main contribution in this thesis is concerned with the matched
direction beamformer (MDB), without using the interference subspace. The manifold
vector of the desired signal is modeled as a vector lying in a known linear
subspace, but the associated linear combination vector is otherwise unknown due
to pointing errors. This vector can be found by computing the principal eigen-vector
of a certain rank-one matrix. Then a MDB is constructed which is robust
to both pointing errors and overestimation of the signal subspace dimension.
Finally, an interference cancellation beamformer robust to pointing errors
is considered. By means of vector space projections, much of the pointing error
can be eliminated. A one-step power estimation is derived by using the theory
of covariance fitting. Then an estimate-and-subtract interference canceller beamformer
is proposed, in which the power inversion problem is avoided and the
interferences can be cancelled completely
Sensor array signal processing : two decades later
Caption title.Includes bibliographical references (p. 55-65).Supported by Army Research Office. DAAL03-92-G-115 Supported by the Air Force Office of Scientific Research. F49620-92-J-2002 Supported by the National Science Foundation. MIP-9015281 Supported by the ONR. N00014-91-J-1967 Supported by the AFOSR. F49620-93-1-0102Hamid Krim, Mats Viberg
Real-time Sound Source Separation For Music Applications
Sound source separation refers to the task of extracting individual sound sources from some number of mixtures of those sound sources. In this thesis, a novel sound source separation algorithm for musical applications is presented. It leverages the fact that the vast majority of commercially recorded music since the 1950s has been mixed down for two channel reproduction, more commonly known as stereo. The algorithm presented in Chapter 3 in this thesis requires no prior knowledge or learning and performs the task of separation based purely on azimuth discrimination within the stereo field. The algorithm exploits the use of the pan pot as a means to achieve image localisation within stereophonic recordings. As such, only an interaural intensity difference exists between left and right channels for a single source. We use gain scaling and phase cancellation techniques to expose frequency dependent nulls across the azimuth domain, from which source separation and resynthesis is carried out. The algorithm is demonstrated to be state of the art in the field of sound source separation but also to be a useful pre-process to other tasks such as music segmentation and surround sound upmixing
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