Research not for publishing papers, but for fun, for satisfying curiosity, and for revealing the truth.

This blog reports latest progresses in
(1) Signal Processing and Machine Learning for Biomedicine, Neuroimaging, Wearable Healthcare, and Smart-Home
(2) Sparse Signal Recovery and Compressed Sensing of Signals by Exploiting Spatiotemporal Structures
(3) My Works


Sunday, April 17, 2011

Is MMV more suitable for dynamic environment (time-varying sparsity) than KF-CS and LS-CS? (2)

As I said in the previous post, MMV algorithms can be used to dynamical environment (i.e. the problem with time-varying sparse patterns). In the experiment of that post, I used M-SBL as an example (other MMV algorithms can be used also). It was performed on the whole data (Time 0 to Time 60). Here I gave another experiment, in which I segmented the whole data into 6 shorter segments and then performed MSBL on each segment (i.e. step by 10 snapshots), and finally concatenated the recovered data from each segment.

Here is the result (click the picture for large view):


The pink dashed line is the performance curve of the method. The errors in the first few snapshots are obviously reduced, compared to the performance curve of the method that performing MSBL on the whole data (indicated by the red solid line).

Obviously, MMV algorithms can be applied to dynamic compressed sensing problems in this block on-line style. In the above experiment MSBL stepped by 10 snapshots. Here are more experiment results when MSBL stepped by 2, 3 and 5 snapshots (for comparison, I also plotted the results of KF-CS,KF-Genie, LS-CS, and MSBL applied on the whole data from the above experiment):


Referece:




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