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- W4312096619 abstract "This paper addresses the direction-of-arrival (DOA) estimation-based source localization problem by using the convolutional neural network (CNN) and root-MUltiple SIgnal Classification (MUSIC) technique. Existing grid-less neural network-based approach employs a LeNet-based CNN, where its network complexity depends on the number of sensors. To overcome this issue, we propose a LeDIM-net CNN that works for a uniform linear array with an arbitrary number of sensors. The proposed LeDIM-net architecture maintains spatial resolution throughout the network while exploiting non-local spatial information. Simulation results demonstrate the effectiveness of the proposed LeDIM-net over the existing grid-less LeNet-based approach and root-MUSIC at low SNRs for arrays with different sensors by maintaining the same network complexity." @default.
- W4312096619 created "2023-01-04" @default.
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- W4312096619 date "2022-11-07" @default.
- W4312096619 modified "2023-09-27" @default.
- W4312096619 title "A Dilated Inception Convolutional Neural Network for Gridless DOA Estimation Under Low SNR Scenarios" @default.
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- W4312096619 doi "https://doi.org/10.23919/apsipaasc55919.2022.9980340" @default.
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