Convolution Neural Networks for Localization of Near-Field Sources via Symmetric Double-Nested Array
نویسندگان
چکیده
We present the convolution neural networks (CNNs) to achieve localization of near-field sources via symmetric double-nested array (SDNA). Considering that incoherent can be separated in frequency spectrum, we first calculate phase difference matrices and consider typical elements as inputs networks. In order guarantee precision angle-of-arrival (AOA) estimation, implement autoencoders divide AOA subregions construct corresponding classification CNNs obtain AOAs sources. Then, a particular range vector without estimated utilize regression CNN parameters The proposed algorithm is robust off-grid suitable for scenarios with different number Moreover, method outperforms existing source localization.
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ژورنال
عنوان ژورنال: Wireless Communications and Mobile Computing
سال: 2021
ISSN: ['1530-8669', '1530-8677']
DOI: https://doi.org/10.1155/2021/9996780