| 1. |
- Movahed, A., et al.
(author)
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A robust {RFPI}-based 1-bit compressive sensing reconstruction algorithm
- 2012
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In: IEEE Information Theory Workshop (ITW), Lausanne, 3-7 September 2012. - 9781467302234 ; , s. 567-571
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Conference paper (peer-reviewed)abstract
- n this paper, we introduce a 1-bit compressive sensing reconstruction algorithm that is not only robust against bit flips in the binary measurement vector, but also does not require a priori knowledge of the sparsity level of the signal to be reconstructed. Through numerical experiments, we show that our algorithm outperforms state-of-the-art reconstruction algorithms for the 1-bit compressive sensing problem in the presence of random bit flips and when the sparsity level of the signal deviates from its estimated value.
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| 2. |
- Movahed, A., et al.
(author)
-
Recovering signals with variable sparsity levels from the noisy 1-bit compressive measurements
- 2014
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In: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. - 1520-6149. - 9781479928927 ; , s. 6454-6458
-
Conference paper (peer-reviewed)abstract
- In this paper, we consider the 1-bit compressive sensing reconstruction problem in a scenario that the sparsity level of the signal is unknown and time variant, and the binary measurements are contaminated with the noise. We introduce a new reconstruction algorithm which we refer to as Noise-Adaptive Restricted Step Shrinkage (NARSS). NARSS is superior in terms of performance, complexity and speed of convergence to the algorithms already introduced in the literature for 1-bit compressive sensing reconstruction from the noisy binary measurements.
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