Array processing with neural networks for estimation of near-field source position

Fujii, Satoshi; Manabe, Tetsuya

doi:10.1109/aps.1991.174856

Cited by 4 publications

(5 citation statements)

References 4 publications

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“…where n(t) is an N -dimensional complex white noise vector with zero mean and variance s 2 , T is the transpose, and a(θ i , r i ) is the N -dimensional steering vector defined by [11], [2],…”

Section: Problem Formulationmentioning

confidence: 99%

“…Note that the power degradation at the ith array output is denoted by 1/r in in (2). Several conventional DOA estimation methods [1], [7]- [10] either do not consider the power degradation or assume it to be constant for all array outputs.…”

Section: Problem Formulationmentioning

confidence: 99%

“…( P t ) nm cos g nm + p (1) nm ε + q (1) nm ε 2 + j sin g nm + p (2) nm ε + q (2) nm ε 2 × 2 + 3(N − 1) cos θε + 2(N − 1)(2N − 1) 3 (4 cos 2 θ − 1)ε 2 .…”

Section: Appendix a Derivation Of (18)mentioning

confidence: 99%

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Second-Order Approximation for DOA Estimation of Near-Field Sources

Adachi

Iiguni

Maeda

2003

Circuits Syst Signal Process

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The direction-of-arrival estimation of near-field sources can be formulated as a multidimensional nonlinear optimization problem, where a performance index is minimized with respect to azimuth, range, and source power. For the single source case, under the assumption that the range is relatively larger than the interelement distance, we use the second-order approximation to derive a simpler performance index parameterized by azimuth only. The minimization of the new index is easier than that of the original one parameterized by azimuth, range, and source power. Moreover, the proposed method considers the degradation of signal powers, giving more accurate estimation results. Also for the multiple source case, an efficient computation method is developed by using the second-order approximation.

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Section: Problem Formulationmentioning

confidence: 99%

Section: Problem Formulationmentioning

confidence: 99%

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Second-Order Approximation for DOA Estimation of Near-Field Sources

Adachi

Iiguni

Maeda

2003

Circuits Syst Signal Process

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“…The method is based on the array processing method which gave high-resolution direction-of-arrival (DOA) spectra in the previous works [1] [2].…”

Section: Neural Network Processingmentioning

confidence: 99%

“…We proposed a array processing method using a Hopfield neural network, which yields high-resolution direction-of-arrival (DOA) spectra', in the previous works [1] [2]. The method is also applied to spectral estimations of the step-frequency radar [3].…”

Section: Introductionmentioning

confidence: 99%

<title>Neural network processing of FMCW Doppler radar</title>

Fujii¹

1994

SPIE Proceedings

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Iii this paper, we propose the spectral ariaJysis method for FMCW Doppler radar using a Hopfield neural network, which can yield high-resolution spectra. In this method, the spectral analysis is reformulated as a minimization problem of difference between the covariance matrixes calculated from observed data and theoretical values. This minimization problem is mapped on the energy function of the Hopfield neural network and then letting the network converge to the minimum state of that energy function. Highresolution spectra are caused by the non-linear function of neurons and the global connectivity of the network.

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Fused Heterocyclo-Quinolines Containing One Nitrogen Atom at Ring Junction: Part 1. Four and Five Membered Heterocyclo-Quinolines

El-Ashry

Ibrahim

2003

Advances in Heterocyclic Chemistry

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Array processing with neural networks for estimation of near-field source position

Cited by 4 publications

References 4 publications

Second-Order Approximation for DOA Estimation of Near-Field Sources

Second-Order Approximation for DOA Estimation of Near-Field Sources

<title>Neural network processing of FMCW Doppler radar</title>

Fused Heterocyclo-Quinolines Containing One Nitrogen Atom at Ring Junction: Part 1. Four and Five Membered Heterocyclo-Quinolines

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