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- W3046873241 abstract "The use of recurrent neural networks (RNNs) to utilize measurements from commercial microwave links (CMLs) has recently gained attention. Whereas previous studies focused on the performance of methods for wet-dry classification, here we propose an RNN algorithm for estimating the rain-rate. We empirically analyzed the proposed algorithm, using real data, and compared it with the traditional power-law (PL)-based algorithm, commonly used for estimating rain from CML attenuation measurements. Our analysis shows that the data-driven RNN algorithm, when properly trained, outperforms the PL algorithm in terms of accuracy. On the other hand, the PL algorithm is simpler and more robust when dealing with a large variety of corruptions and adverse conditions. We then introduced a time normalization (TN) layer for controlling the trade-off between performance and robustness of the RNN methods, and demonstrated its performance." @default.
- W3046873241 created "2020-08-07" @default.
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- W3046873241 date "2021-05-01" @default.
- W3046873241 modified "2023-10-10" @default.
- W3046873241 title "Recurrent Neural Network for Rain Estimation Using Commercial Microwave Links" @default.
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- W3046873241 doi "https://doi.org/10.1109/tgrs.2020.3010305" @default.
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