Online recovery of time-varying signals based on sparse Bayesian learning

Daoguang Dong; Guosheng Rui; Wenbiao Tian; Ge Liu; Yang Bao; Song Zhang

doi:10.1117/12.2559450

31 December 2019 Online recovery of time-varying signals based on sparse Bayesian learning

Daoguang Dong, Guosheng Rui, Wenbiao Tian, Ge Liu, Yang Bao, Song Zhang

Proceedings Volume 11384, Eleventh International Conference on Signal Processing Systems; 113840C (2019) https://doi.org/10.1117/12.2559450
Event: Eleventh International Conference on Signal Processing Systems, 2019, Chengdu, China

Abstract

In order to improve the accuracy of on-line dynamic reconstruction of time-varying signals, a dynamic compressed sensing algorithm based on sparse Bayesian learning named Support-DCS is proposed in this paper. Since there is no need to assume any time-varying law of signal and no need to adjust any model parameters artificially, the algorithm has good adaptability. Three time-varying signal types with different correlation levels are set up for experiments. The experimental results showed that, compared with several main existing algorithms, the proposed algorithm always has remarkable advantages in signal-to-error ratio, while the other algorithms can only be effective when the support set changes slowly and the signal amplitude correlation is strong.

Citation Download Citation

Daoguang Dong, Guosheng Rui, Wenbiao Tian, Ge Liu, Yang Bao, and Song Zhang "Online recovery of time-varying signals based on sparse Bayesian learning", Proc. SPIE 11384, Eleventh International Conference on Signal Processing Systems, 113840C (31 December 2019); https://doi.org/10.1117/12.2559450

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