Research on the Dynamic Sparse Bayesian Recovery of Multi-task Observed Streaming Signals in Time Domain

Daoguang DONG; Guosheng RUI; Wenbiao TIAN

doi:10.11999/JEIT190558

Volume 42 Issue 7

Jul. 2020

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Article Navigation > Journal of Electronics & Information Technology > 2020 > 42(7): 1758-1765

Daoguang DONG, Guosheng RUI, Wenbiao TIAN. Research on the Dynamic Sparse Bayesian Recovery of Multi-task Observed Streaming Signals in Time Domain[J]. Journal of Electronics & Information Technology, 2020, 42(7): 1758-1765. doi: 10.11999/JEIT190558

Citation:

Daoguang DONG, Guosheng RUI, Wenbiao TIAN. Research on the Dynamic Sparse Bayesian Recovery of Multi-task Observed Streaming Signals in Time Domain[J]. Journal of Electronics & Information Technology, 2020, 42(7): 1758-1765. doi: 10.11999/JEIT190558

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Research on the Dynamic Sparse Bayesian Recovery of Multi-task Observed Streaming Signals in Time Domain

doi: 10.11999/JEIT190558

Signal and Information Processing Key Laboratory in Shandong, Navy Aviation University, Yantai 264001, China

Funds: The National Natural Science of China (41606117, 41476089, 61671016)

Received Date: 2019-07-25
Rev Recd Date: 2020-03-26

Available Online: 2020-04-24

Publish Date: 2020-07-23

Abstract

Abstract

To eliminate the blocking effects in the dynamic recovery of the streaming signals observed from multiple tasks in time domain, a streaming multi-task sparse Bayesian learning based algorithm and its robust enhanced version are proposed in this paper, where the former extends Lapped Orthogonal Transform (LOT) sliding window in time domain to multi-task condition, and decouples the estimation of unknown noise accuracy from signal reconstruction by Bayesian probability modeling and omits it, the latter further introduces the measurement of reconstructed uncertainty, which improves the robustness of the algorithm and the ability to suppress the accumulation of errors. Experimental results based on measured meteorological data shows that the proposed algorithms have significantly higher reconstruction accuracy, success rate and running speed than the representative algorithms in the field of compressed sensing from multiple measurement vectors, namely, the Temporal Multiple Sparse Bayesian Learning (TMSBL) algorithm and the Multi-Task-Compressed Sensing (MT-CS) algorithm, under different conditions of Signal-to-Noise Ratios, number of observations and tasks.
- Signal processing,
- Streaming signals,
- Multi-task,
- Sparse Bayesian,
- Blocking effects

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References(22)

References

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