Data Modeling, Estimation and Recovery of Dynamic and Static Sparse Signals-A Review
Sulthana Shafi1, George M Joseph2
1Sulthana Shafi, Electronics and Communication Engineering Department, Sree Chitra Thirunal College Of Engineering, Pappanamcode, Trivandrum (Kerala), India.
2George M Joseph, Electronics and Communication Engineering Department, Sree Chitra Thirunal College Of Engineering, Pappanamcode, Trivandrum (Kerala), India.
Manuscript received on 13 June 2016 | Revised Manuscript received on 20 June 2016 | Manuscript Published on 30 June 2016 | PP: 68-74 | Volume-5 Issue-5, June 2016 | Retrieval Number: E4606065516/16©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: For sparse signal, compressed sensing is the present dogma, using only fewer measurements for sampling, compression and reconstruction of signals satisfying the Nyquist theorm. Here the outgrowth of compressive sensing using different algorithms for time invariant till time varying sparse signals and its recovery are surveyed. Thus these algorithms are effective in recovering dynamic and static sparse signal vectors. Algorithms exhibiting correlation and optimization approaches are reviewed. Also different mathematical models are reviewed which improves the quality of estimated solutions to best optimal solution.
Keywords: Compressed Sensing, Multiple Measurement Vector, OFDM, Lasso, Homotopy, Kalman Filter, Expectation Maximization.
Scope of the Article: Big Data Quality Validation