Proc. SPIE. 11384, Eleventh International Conference on Signal Processing Systems
KEYWORDS: Signal to noise ratio, Detection and tracking algorithms, Data modeling, Signal processing, Reconstruction algorithms, Dynamical systems, Signal detection, Space based lasers, Filtering (signal processing), Compressed sensing
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.