Title :
Sparsity adaptive matching pursuit algorithm for practical compressed sensing
Author :
Do, Thong T. ; Gan, Lu ; Nguyen, Nam ; Tran, Trac D.
Author_Institution :
Dept. of Electr. & Comput. Eng., Johns Hopkins Univ., Baltimore, MD
Abstract :
This paper presents a novel iterative greedy reconstruction algorithm for practical compressed sensing (CS), called the sparsity adaptive matching pursuit (SAMP). Compared with other state-of-the-art greedy algorithms, the most innovative feature of the SAMP is its capability of signal reconstruction without prior information of the sparsity. This makes it a promising candidate for many practical applications when the number of non-zero (significant) coefficients of a signal is not available. The proposed algorithm adopts a similar flavor of the EM algorithm, which alternatively estimates the sparsity and the true support set of the target signals. In fact, SAMP provides a generalized greedy reconstruction framework in which the orthogonal matching pursuit and the subspace pursuit can be viewed as its special cases. Such a connection also gives us an intuitive justification of trade-offs between computational complexity and reconstruction performance. While the SAMP offers a comparably theoretical guarantees as the best optimization-based approach, simulation results show that it outperforms many existing iterative algorithms, especially for compressible signals.
Keywords :
adaptive signal processing; computational complexity; greedy algorithms; iterative methods; signal reconstruction; EM algorithm; compressed sensing; computational complexity; iterative greedy reconstruction algorithm; optimization-based approach; signal reconstruction; sparsity adaptive matching pursuit algorithm; Algorithm design and analysis; Compressed sensing; Computational complexity; Gallium nitride; Greedy algorithms; Iterative algorithms; Matching pursuit algorithms; Pursuit algorithms; Reconstruction algorithms; Sampling methods; Sparsity adaptive; compressed sensing; compressive sampling; greedy pursuit; sparse reconstruction;
Conference_Titel :
Signals, Systems and Computers, 2008 42nd Asilomar Conference on
Conference_Location :
Pacific Grove, CA
Print_ISBN :
978-1-4244-2940-0
Electronic_ISBN :
1058-6393
DOI :
10.1109/ACSSC.2008.5074472