DeepFPC: A deep unfolded network for sparse signal recovery from 1-Bit measurements with application to DOA estimation

Xiao, Peng; Liao, Bin; Deligiannis, Nikos

doi:10.1016/j.sigpro.2020.107699

Cited by 31 publications

(22 citation statements)

References 34 publications

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“…Unlike the so-called deep unrolling strategy [16], the proposed network acts as an optimizer but not an unrolling model. The iteration of original optimization is replaced by the training procedure but not the layers.…”

Section: Unsupervised Learning For Dl-based Estimation Networkmentioning

confidence: 99%

See 1 more Smart Citation

Unsupervised Learning Strategy for Direction-of-Arrival Estimation Network

Yuan¹,

Wu²,

Wu³

et al. 2021

IEEE Signal Process. Lett.

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In this letter, we proposed a novel unsupervised learning strategy for direction-of-arrival (DOA) estimation network. Inspired by the sparse power spectrum and 1-norm optimization, we develop a novel loss function to cooperate with the estimation network. Unlike the prior DL-based methods, the proposed method does not need any manual annotations for training and validation datasets. Compared with state-of-art methods, the proposed method can automatically increase the degree of freedom of the array without further pre-processing on the covariance matrix of array observation data. Moreover, the proposed method can obtain clear spectrum and precise DOAs under harsh estimation environments.

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Section: Unsupervised Learning For Dl-based Estimation Networkmentioning

confidence: 99%

“…With the rapid development of artificial intelligence (AI), researchers have designed many deep-learning-based (DLbased) networks to achieve data-driven estimation approaches [16]- [18]. There are generally two kinds of models to realize DL-based estimation: the classification model and the regression model [19], [20].…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Learning Strategy for Direction-of-Arrival Estimation Network

Yuan¹,

Wu²,

Wu³

et al. 2021

IEEE Signal Process. Lett.

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“…Experiments have confirmed that methods based on deep learning can achieve better DOA estimation than traditional methods. Xiao et al [41] proposed a DeepFPC network structure similar to the deep residual network. DeepFPC has high sparse signal recovery performance and good DOA estimation performance under low SNR.…”

Section: Doa Estimation In Signalmentioning

confidence: 99%

“…Table 8 shows the relationship between the ratio of the number of signal sources to the number of antennas and the evaluation criteria. If the ratio in [41] increases from 1/20 to 5/20, the MAE increases from 0.35 to 1.02. In [51], if the ratio increases from 1/20 to 1/10, the accuracy will drop from 100% to 97%; if the ratio in [44] increases from 1/8 to 3/8, the RMSE increases from 0.01 to 0.018.…”

Section: E Impact Of the Number Of Antennas And The Number Of Signal Sources On Doa Estimationmentioning

confidence: 99%

Deep Learning Approach in DOA Estimation: A Systematic Literature Review

Li²,

Rum

2021

Mobile Information Systems

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In array signal processing, the direction of arrival (DOA) of the signal source has drawn broad research interests with its wide applications in fields such as sonar, radar, communications, medical detection, and electronic countermeasures. In recent years, the application of deep learning (DL) to DOA estimation has achieved great success. This study provides a systematic review of research on DOA estimation using deep neural network methods. We manually selected twenty-five papers related to this research from five prominent databases (SpringerLink, IEEE Xplore, ScienceDirect, Scopus, and Google Scholar) for exploration. Six questions describing the overall trend of DOA estimation using deep learning are put forward. Then, we answered these questions by reviewing the literature. This review is helpful for researchers in this field because it provides more specific and comprehensive information needed for future research. Specifically, we first analyzed the background of the selected papers, including the type of publication, the number of citations, and the country of origin. Then, the DL technology used in DOA estimation is systematically analyzed, including the purpose of using DL in DOA estimation, various DL models (convolutional neural network, deep neural network, and combination network), and various DOA estimation schemes. Finally, various evaluation criteria (root-mean-squared error, accuracy, and mean absolute error) are used to evaluate the DL technology in DOA estimation, and various factors (signal-to-noise ratio, number of snapshots, number of antennas, and number of signal sources) affecting DOA estimation are analyzed. Based on our findings, we believe that deep learning can perform DOA estimation well, and there is still room for improvement in deep learning technology. In this study, the factors affecting DOA estimation can be used as the direction for researchers to conduct in-depth research.

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“…Interestingly, the analogous connections between GSP and classical signal processing could help one to understand the core ideas of GSP [8]-such as graph filters, graph Fourier transform, graph frequency, spectral decomposition, etc. GSP is also well fitted in modeling and analyzing the complex data and interactions on the sensor arrays [9]. By designing an appropriate graph, or an adjacency matrix, that reveals the intrinsic data structure, GSP can be employed to estimate the direction of arrival (DOA).…”

Section: Introductionmentioning

confidence: 99%

Graph-based Matched Field Localization for an Underwater Source

Xiao¹,

Xu²,

Xu³

et al. 2021

Preprint

Self Cite

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Matched Field Processing (MFP) locates the underwater sources by matching the received data with the replica vectors, which could be regarded as a generalized beamformer. In this paper, the MFP method is combined with a recently developed framework-Graph Signal Processing (GSP) method. Following the paradigm of GSP, a spatial adjacency matrix is constructed for the arbitrary distributed sensors based on the Green's function, then the source is located by utilizing the graph Fourier transform. The simulation results illustrate that the Graph-based MFP outperforms the the conventional MFP processors-the Bartlett processor and the Minimum Variance processor-for its good accuracy and robustness.

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DeepFPC: A deep unfolded network for sparse signal recovery from 1-Bit measurements with application to DOA estimation

Cited by 31 publications

References 34 publications

Unsupervised Learning Strategy for Direction-of-Arrival Estimation Network

Unsupervised Learning Strategy for Direction-of-Arrival Estimation Network

Deep Learning Approach in DOA Estimation: A Systematic Literature Review

Graph-based Matched Field Localization for an Underwater Source

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