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• W974511872 abstract "In this chapter we provide an overview of subspace-based parameter estimation schemes for uniform arrays, non-uniform arrays, and other specific array structures. The popularity of many of these special array structures is due to the availability of search-free low computational complexity direction of arrival (or spatial frequency) estimation algorithms to exploit the particularly structure of the array. More precisely, we mainly focus on the study and the comparison between several subspace-based algorithms. The latter can be classified into spectral search-based and search-free techniques. The spectral searching schemes include MUSIC, weighted subspace fitting algorithms, and rank reduction schemes divised for sensor arrays composed of multiple fully-calibrated subarrays with unknown subarray displacements. On the other hand, the search-free schemes can be partitioned into two subclasses: (i) Polynomial-rooting techniques, in which, we describe and compare MODE, the root-MUSIC algorithm and its variants for the uniform linear array (ULA) configuration, and the interpolated root-MUSIC, the manifold separation, and the Fourier domain root-MUSIC schemes in the non-uniform array context. (ii) Matrix-shifting techniques, in which, we present and compare the ESPRIT algorithm adapted for array geometries that exhibit a shift invariance structure and its variants, as the GESPRIT and Unitary ESPRIT algorithms. By numerical simulations, we show that in the one-dimensional case, the threshold of the root-MUSIC algorithm occurs at a higher SNR than for ESPRIT-based algorithms, in which the Unitary ESPRIT scheme performs best among all ESPRIT-based schemes. For the case of multidimensional parameter estimation, we introduce R-D matrix-based and tensor-based algorithms. We demonstrate that multidimensional signals can be represented by tensors which provide a natural formulation of the R-dimensional signals and their properties (such as the R-D shift invariances needed for matrix shifting techniques). Based on this representation, an improved HOSVD-based signal subspace estimate is proposed. We show that this subspace estimate performs a more efficient denoising of the data which leads to a tensor gain in terms of an enhanced estimation accuracy. This subspace estimate can be combined with arbitrary existing multidimensional subspace-based parameter estimation schemes. Then we discuss the tensor-based schemes R-D Standard Tensor-ESPRIT and R-D Unitary Tensor-ESPRIT. They outperform the matrix based R-D ESPRIT-type algorithms due to the enhanced subspace estimate obtained from the HOSVD. We also show that strict-sense non-circular sources can be exploited to virtually double the number of available sensors by an augmentation of the measurement matrix. Based on this idea, the R-D NC Standard ESPRIT and the R-D NC Unitary ESPRIT algorithm are derived. As a result, the number of resolvable wavefronts is doubled and the achievable estimation accuracy is improved. Finally, the family of NC Tensor-ESPRIT-type algorithms is introduced to combine both benefits, the strict-sense non-circular source symbols and the multidimensional structure of the signals. This is a non-trivial task, since the augmentation of the measurement matrix performed for R-D NC Unitary ESPRIT destroys the structure needed for the Tensor-ESPRIT-type algorithms. This challenge can be solved by defining a mode-wise augmentation of the measurement tensor." @default.
• W974511872 created "2016-06-24" @default.
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• W974511872 date "2014-01-01" @default.
• W974511872 modified "2023-10-16" @default.
• W974511872 title "Subspace Methods and Exploitation of Special Array Structures" @default.
• W974511872 cites W1491758811 @default.
• W974511872 cites W1963659194 @default.
• W974511872 cites W1963826206 @default.
• W974511872 cites W1967486001 @default.
• W974511872 cites W1968079322 @default.
• W974511872 cites W1982975813 @default.
• W974511872 cites W1991042426 @default.
• W974511872 cites W1994851389 @default.
• W974511872 cites W1995331363 @default.
• W974511872 cites W1997240173 @default.
• W974511872 cites W2003216148 @default.
• W974511872 cites W2012270713 @default.
• W974511872 cites W2013912476 @default.
• W974511872 cites W2018189982 @default.
• W974511872 cites W2019473268 @default.
• W974511872 cites W2019833178 @default.
• W974511872 cites W2023771239 @default.
• W974511872 cites W2033332370 @default.
• W974511872 cites W2041050174 @default.
• W974511872 cites W2055838441 @default.
• W974511872 cites W2078966899 @default.
• W974511872 cites W2083671377 @default.
• W974511872 cites W2087543940 @default.
• W974511872 cites W2096229075 @default.
• W974511872 cites W2097610668 @default.
• W974511872 cites W2100868949 @default.
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• W974511872 cites W2101658087 @default.
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• W974511872 cites W2109522725 @default.
• W974511872 cites W2109958193 @default.
• W974511872 cites W2114474878 @default.
• W974511872 cites W2118686117 @default.
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• W974511872 cites W2121025916 @default.
• W974511872 cites W2122046351 @default.
• W974511872 cites W2122579903 @default.
• W974511872 cites W2123930706 @default.
• W974511872 cites W2126767504 @default.
• W974511872 cites W2128131274 @default.
• W974511872 cites W2130739159 @default.
• W974511872 cites W2130897259 @default.
• W974511872 cites W2136023375 @default.
• W974511872 cites W2136172079 @default.
• W974511872 cites W2136210023 @default.
• W974511872 cites W2136274821 @default.
• W974511872 cites W2139748108 @default.
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• W974511872 doi "https://doi.org/10.1016/b978-0-12-411597-2.00015-1" @default.
• W974511872 hasPublicationYear "2014" @default.
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