- Title
- Maximum a posteriori estimation approach to sparse recovery
- Creator
- Hyder, Md Mashud; Mahata, Kaushik
- Relation
- 2011 17th International Conference on Digital Signal Processing. Proceedings of 2011 17th International Conference on Digital Signal Processing (Corfu, GR 6-8 July, 2011)
- Publisher Link
- http://dx.doi.org/10.1109/ICDSP.2011.6004892
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Resource Type
- conference paper
- Date
- 2011
- Description
- We adopt a maximum a posteriori (MAP) estimation based approach for recovering sparse signals from a small number of measurements formed by computing the inner products of the signal with rows of a matrix. We assume that each component of the sparse signal is independent and identically distributed (i.i.d) random variable drawn from a Gaussian mixture model. We then develop a suitable MAP formulation which results in an iterative algorithm. Simulations are performed to study the performance of the algorithm. We observe that our approach has a number of advantages over other sparse recovery techniques, including robustness to noise, increased performance with limited measurements and lower computation time.
- Subject
- maximum a posteriori estimation; Gaussian mixture model; sparse signal; basis pursuit
- Identifier
- http://hdl.handle.net/1959.13/1325372
- Identifier
- uon:25255
- Identifier
- ISBN:9781457702730
- Language
- eng
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