Applications of Machine Learning 2020 2020
DOI: 10.1117/12.2568302
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Forward and inverse scattering in synthetic aperture radar using machine learning

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“…( 73), which arises from sampling the scattering field. The approach employed by [282] uses a NN architecture that consists of a single layer of neurons, with no activation function, to find the sensing matrix, A, or its pseudo-inverse B = (A H A) −1 A H , where H is the Hermetian (complex transpose) matrix operator. The authors note that knowing A is sufficient to solve the scattering problem when the observation space sampling is chosen so that A H A is approximately a diagonal matrix; however, their experimental results using the CIFAR-10 image dataset [283] show better performance using the estimated pseudo-inverse.…”
Section: Intelligent Sa Systemsmentioning
confidence: 99%
“…( 73), which arises from sampling the scattering field. The approach employed by [282] uses a NN architecture that consists of a single layer of neurons, with no activation function, to find the sensing matrix, A, or its pseudo-inverse B = (A H A) −1 A H , where H is the Hermetian (complex transpose) matrix operator. The authors note that knowing A is sufficient to solve the scattering problem when the observation space sampling is chosen so that A H A is approximately a diagonal matrix; however, their experimental results using the CIFAR-10 image dataset [283] show better performance using the estimated pseudo-inverse.…”
Section: Intelligent Sa Systemsmentioning
confidence: 99%