GEESE (GEneralized Eigenvalues Utilizing Signal Subspace Eignevectors) - A New Technique For Direction Finding

Pillai, S.U.; Kwon, Byung Ho

doi:10.1109/acssc.1988.754606

Cited by 13 publications

(2 citation statements)

References 4 publications

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“…A number of techniques to extract the SC with high resolution have been studied for overcoming the Fbin limitation. The TLS (total least squares)‐Prony, matrix‐pencil, and generalized eigenvalues utilizing signal subspace eigenvectors (GEESE) methods are commonly used as model‐based SC extraction techniques with high resolution . These techniques require knowing the number of SCs on the target when extracting the SC.…”

Section: Introductionmentioning

confidence: 99%

A novel fast clean algorithm using the gradient descent method

Choi

2017

Micro & Optical Tech Letters

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An evolutionary programming (EP)‐based CLEAN and particle swarm optimization (PSO)‐based CLEAN have better accuracy than the FFT‐based CLEAN. EP‐ and PSO‐based CLEAN have a higher computational burden, because they must solve a three‐dimensional optimization problem using the stochastic search method. To overcome this problem, we employ gradient descent to the CLEAN algorithm. We then compare the performance of the proposed method with that of the PSO‐based CLEAN and the FFT‐based CLEAN. Experimental results show that the proposed algorithm is faster and more accurate than conventional methods. © 2017 Wiley Periodicals, Inc. Microwave Opt Technol Lett 59:1018–1022, 2017

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Section: Introductionmentioning

confidence: 99%

A novel fast clean algorithm using the gradient descent method

Choi

2017

Micro & Optical Tech Letters

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“…There are two main strategies to these algorithms. First are model based methods, such as Prony's method, matrix pencil (MP), estimation of signal parameters via rotational invariance techniques (ESPRIT), generalized eigenvalues utilizing signal subspace eigenvectors (GEESE), and multiple signal classification (MUSIC) [3][4][5][6][7][8][9]. Model-based methods can be computed quickly and have high resolutions, but are sensitive to noise and require the initial value estimation or model order estimation.…”

Section: Introductionmentioning

confidence: 99%

Drop Algorithm for Super Resolution Scattering Center Extraction

Choi¹,

Choi²

2018

PIER

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The scattering center extraction algorithm is a method to estimate the scattering center from the backscattered field. Superior scattering center extraction algorithms should be robust to noise, independent of the model order, and automatically and quickly operated. In this paper, we propose a novel super resolution scattering center extraction algorithm that satisfies the conditions mentioned above, which has been named the dimension reduced optimization problem (DROP). Using DROP, we determined a one-dimensional scattering center from a high resolution range profile and a two-dimensional scattering center from an inverse synthetic aperture radar image.

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