Journal Article10.1109/97.988716
Robust array interpolation using second-order cone programming
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TL;DR: A new robust interpolation approach is developed by minimizing the interpolation error inside the sectors of interest while setting multiple "stopband" constraints outside these sectors to prevent performance degradation effects caused by out-of-sector sources.
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Abstract: We study Friedlander's (1993) array interpolation technique, whose main shortcoming in multisource scenarios is that it does not provide sufficient robustness against sources arriving outside specified interpolation sectors. In this letter, we develop a new robust interpolation approach by minimizing the interpolation error inside the sectors of interest while setting multiple "stopband" constraints outside these sectors to prevent performance degradation effects caused by out-of-sector sources. Computationally efficient convex formulations of the robust interpolation matrix design problem using second-order cone programming are derived.
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Convex optimization based beam-space preprocessing with improved robustness against out-of-sector sources
TL;DR: A new approach to beam-space preprocessing with an improved robustness against out-of-sector interfering sources is developed, and Computationally efficient convex formulations for these beam- space matrix filter design problems are derived using second-order cone (SOC) programming.
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ESPRIT-estimation of signal parameters via rotational invariance techniques
R. Roy,Thomas Kailath +1 more
TL;DR: Although discussed in the context of direction-of-arrival estimation, ESPRIT can be applied to a wide variety of problems including accurate detection and estimation of sinusoids in noise.
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ESPIRT-estimation of signal parameters via rotational invariance techniques
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TL;DR: A novel approach to the general problem of signal parameter estimation is described, and although discussed in the context of direction-of-arrival estimation, ESPRIT can be applied to a wide variety of problems.
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Improving the resolution performance of eigenstructure-based direction-finding algorithms
A. Barabell
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Maximum likelihood methods for direction-of-arrival estimation
Petre Stoica,Ken Sharman +1 more
TL;DR: Five methods of direction-of-arrival (DOA) estimation which can be derived from the maximum-likelihood (ML) principle are considered and one of them, MODE-2, is obtained by using the ML principle on the statistics of certain linear combinations of the sample eigenvectors.
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