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math.ST2015★ 49 cited
Dynamic Filtering of Time-Varying Sparse Signals via l1 Minimization
Adam Charles, Aurele Balavoine, Christopher Rozell
Despite the importance of sparsity signal models and the increasing prevalence of high-dimensional streaming data, there are relatively few algorithms for dynamic filtering of time…
math.ST2012★ 6 cited
Re-Weighted l_1 Dynamic Filtering for Time-Varying Sparse Signal Estimation
Adam S. Charles, Christopher J. Rozell
Signal estimation from incomplete observations improves as more signal structure can be exploited in the inference process. Classic algorithms (e.g., Kalman filtering) have exploit…