DocumentCode :
2762007
Title :
Reconstruction in Compressive Sensing Using Affine Scaling Transformations with Variable-p Diversity Measure
Author :
Domínguez, Rufino J. ; Cabrera, Sergio D. ; Rosiles, J. Gerardo ; Vega-Pineda, Javier
Author_Institution :
Div. de Posgrado en Electron., Inst. Tec. de Chihuahua, Chihuahua
fYear :
2009
fDate :
4-7 Jan. 2009
Firstpage :
708
Lastpage :
713
Abstract :
The Affine Scaling Transformation (AST) family of algorithms can solve the minimization of the l(ples1), p-norm-like diversity measure for an underdetermined linear inverse problem. The AST algorithms can therefore be used to solve the sparse signal recovery problem that arises in Compressive Sensing. In this paper, we continue to investigate the application of the iterative AST family of algorithms with a dynamical adjustment of the p parameter to improve convergence speed and signal recovery accuracy. In our previous work we experimentally determined that any p in can give the sparse solution when exact recovery is possible, however, the behavior of the algorithm is highly dependent on this parameter. In addition, the best-approximation error, for those cases where exact recovery is not possible, is also highly dependent on p. Using various criteria, we propose and evaluate some strategies to vary the values of p as a function of the iteration in the AST algorithm. The goal in these strategies for a variable-p AST algorithm is to capture the benefits of the p=0 and the p=1 fixed-p approaches simultaneously.
Keywords :
affine transforms; inverse problems; iterative methods; minimisation; signal reconstruction; affine scaling transformations; best-approximation error; compressive sensing; iterative AST; minimization; p-norm-like diversity measure; signal reconstruction; signal recovery accuracy; sparse signal recovery problem; underdetermined linear inverse problem; variable-p diversity measure; Convergence; Heuristic algorithms; Inverse problems; Iterative algorithms; Linear systems; Measurement standards; Minimization methods; Performance evaluation; Sparse matrices; Vectors; Affine Scaling Transformation; compressive sensing; diversity measure; sparse signal recovery;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop, 2009. DSP/SPE 2009. IEEE 13th
Conference_Location :
Marco Island, FL
Print_ISBN :
978-1-4244-3677-4
Electronic_ISBN :
978-1-4244-3677-4
Type :
conf
DOI :
10.1109/DSP.2009.4786014
Filename :
4786014
Link To Document :
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