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• W4379116652 abstract "Multipath interference while tracking sea-skimming targets can significantly distort the estimated height of the target. If accounted for however, this interference can be used to obtain more accurate estimates. In this study we accomplish this with a convolutional neural network (CNN) used as a parameter estimator. The performance of this network is compared with maximum likelihood and least squares methods. We found that the CNN performs well in comparison to these methods with only a fraction of the computations." @default.
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• W4379116652 date "2023-01-01" @default.
• W4379116652 modified "2023-10-12" @default.
• W4379116652 title "Low-Angle Target Tracking in Sea Surface Multipath Using Convolutional Neural Networks" @default.
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• W4379116652 doi "https://doi.org/10.1109/taes.2023.3282191" @default.
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