Dynamic Iterative Pursuit

Abstract

For compressive sensing of dynamic sparse signals, we develop an iterative pursuit algorithm. A dynamic sparse signal process is characterized by varying sparsity patterns over time/space. For such signals, the developed algorithm is able to incorporate sequential predictions, thereby providing better compressive sensing recovery performance, but not at the cost of high complexity. Through experimental evaluations, we observe that the new algorithm exhibits a graceful degradation at deteriorating signal conditions while capable of yielding substantial performance gains as conditions improve.

6 pages, 7 figures. Accepted for publication in IEEE Transactions on Signal Processing

References in corpus (3)

• Kalman Filtered Compressed Sensing
• LS-CS-residual (LS-CS): Compressive Sensing on Least Squares Residual
• Projection-Based and Look Ahead Strategies for Atom Selection

Cited by in corpus (5)

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• Re-Weighted l_1 Dynamic Filtering for Time-Varying Sparse Signal Estimation
• Combined modeling of sparse and dense noise for improvement of Relevance Vector Machine