136 research outputs found
Sparse Active Rectangular Array with Few Closely Spaced Elements
Sparse sensor arrays offer a cost effective alternative to uniform arrays. By
utilizing the co-array, a sparse array can match the performance of a filled
array, despite having significantly fewer sensors. However, even sparse arrays
can have many closely spaced elements, which may deteriorate the array
performance in the presence of mutual coupling. This paper proposes a novel
sparse planar array configuration with few unit inter-element spacings. This
Concentric Rectangular Array (CRA) is designed for active sensing tasks, such
as microwave or ultra-sound imaging, in which the same elements are used for
both transmission and reception. The properties of the CRA are compared to two
well-known sparse geometries: the Boundary Array and the Minimum-Redundancy
Array (MRA). Numerical searches reveal that the CRA is the MRA with the fewest
unit element displacements for certain array dimensions.Comment: 4+1 pages, 5 figures, 1 tabl
Robust Signal Restoration and Local Estimation of Image Structure
A class of nonlinear regression filters based on robust theory is introduced. The goal of the filtering is to restore the shape and preserve the details of the original noise-free signal, while effectively attenuating both impulsive and nonimpulsive noise. The proposed filters are based on robust Least Trimmed Squares estimation, where very deviating samples do not contribute to the final output. Furthermore, if there is more than one statistical population present in the processing window the filter is very likely to select adaptively the samples that represent the majority and uses them for computing the output. We apply the regression filters on geometric signal shapes which can be found, for example, in range images. The proposed methods are also useful for extracting the trend of the signal without losing important amplitude information. We show experimental results on restoration of the original signal shape using real and synthetic data and both impulsive and nonimpulsive noise. In addition, we apply the robust approach for describing local image structure. We use the method for estimating spatial properties of the image in a local neighborhood. Such properties can be used for example, as a uniformity predicate in the segmentation phase of an image understanding task. The emphasis is on producing reliable results even if the assumptions on noise, data and model are not completely valid. The experimental results provide information about the validity of those assumptions. Image description results are shown using synthetic and real data, various signal shapes and impulsive and nonimpulsive noise
Complex Random Vectors and ICA Models: Identifiability, Uniqueness and Separability
In this paper the conditions for identifiability, separability and uniqueness
of linear complex valued independent component analysis (ICA) models are
established. These results extend the well-known conditions for solving
real-valued ICA problems to complex-valued models. Relevant properties of
complex random vectors are described in order to extend the Darmois-Skitovich
theorem for complex-valued models. This theorem is used to construct a proof of
a theorem for each of the above ICA model concepts. Both circular and
noncircular complex random vectors are covered. Examples clarifying the above
concepts are presented.Comment: To appear in IEEE TR-IT March 200
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