6,323 research outputs found
Soft Consistency Reconstruction: A Robust 1-bit Compressive Sensing Algorithm
A class of recovering algorithms for 1-bit compressive sensing (CS) named
Soft Consistency Reconstructions (SCRs) are proposed. Recognizing that CS
recovery is essentially an optimization problem, we endeavor to improve the
characteristics of the objective function under noisy environments. With a
family of re-designed consistency criteria, SCRs achieve remarkable
counter-noise performance gain over the existing counterparts, thus acquiring
the desired robustness in many real-world applications. The benefits of soft
decisions are exemplified through structural analysis of the objective
function, with intuition described for better understanding. As expected,
through comparisons with existing methods in simulations, SCRs demonstrate
preferable robustness against noise in low signal-to-noise ratio (SNR) regime,
while maintaining comparable performance in high SNR regime
Spectral Compressive Sensing with Model Selection
The performance of existing approaches to the recovery of frequency-sparse
signals from compressed measurements is limited by the coherence of required
sparsity dictionaries and the discretization of frequency parameter space. In
this paper, we adopt a parametric joint recovery-estimation method based on
model selection in spectral compressive sensing. Numerical experiments show
that our approach outperforms most state-of-the-art spectral CS recovery
approaches in fidelity, tolerance to noise and computation efficiency.Comment: 5 pages, 2 figures, 1 table, published in ICASSP 201
- …