DeepFPC: A deep unfolded network for sparse signal recovery from 1-Bit measurements with application to DOA estimation
DeepFPC: A deep unfolded network for sparse signal recovery from 1-Bit measurements with application to DOA estimation
复制标题
DeepFPC：一种深度展开网络，用于从 1 位测量中恢复稀疏信号并应用于 DOA 估计
DOI：
10.1016/j.sigpro.2020.107699
复制
发表时间：
2020-11
影响因子：
4.4
通讯作者：
Deligiannis Nikos
中科院分区：
文献类型：
--
作者：
Xiao Peng;Liao Bin;Deligiannis Nikos
In this paper, we introduce a novel deep neural network, coined DeepFPC, and investigate its application to tackling the problem of direction-of-arrival (DOA) estimation. DeepFPC is designed by unfolding the iterations of the fixed-point continuation algorithm with one-sided l(1)-norm (FPC-l(1)), which has been proposed for solving the 1-bit compressed sensing problem. The network architecture resembles that of deep residual learning and incorporates prior knowledge about the signal structure (i.e., sparsity), thereby offering interpretability by design. Once DeepFPC is properly trained, a sparse signal can be recovered fast and accurately from quantized measurements. The proposed model is then applied in DOA estimation and is shown to outperform state-of-the-art solutions; namely, the iterative FPC-l(1) algorithm and the deep convolution network (DCN) model. (C) 2020 Elsevier B.V. All rights reserved.
登录
查看更多内容
DOI：
--
发表时间：
2008
期刊：
ArXiv
影响因子：
--
作者：
Wei Dai;O. Milenkovic
通讯作者：
Wei Dai;O. Milenkovic
DOI：
10.1016/j.sigpro.2019.06.027
发表时间：
2019-11
期刊：
Signal Process.
影响因子：
--
作者：
Peng Xiao;B. Liao
通讯作者：
Peng Xiao;B. Liao
影响因子：
3.9
作者：
Tao Chen;Muran Guo;Xiangsong Huang
通讯作者：
Tao Chen;Muran Guo;Xiangsong Huang
DOI：
10.1109/26.848564
发表时间：
2000-06
期刊：
IEEE Trans. Commun.
影响因子：
--
作者：
Z. Lei;Teng Joon Lim
通讯作者：
Z. Lei;Teng Joon Lim
DOI：
10.1016/j.sigpro.2018.09.026
发表时间：
2019-02
期刊：
Signal Process.
影响因子：
--
作者：
Zeeshan Asghar Sayed;B. Ng
通讯作者：
Zeeshan Asghar Sayed;B. Ng