1 Department of Electronic Systems, The Faculty of Engineering and Science (ENG), Aalborg University, VBN2 Technology Platforms Section, The Faculty of Engineering and Science (ENG), Aalborg University, VBN3 The Faculty of Engineering and Science (TECH), Aalborg University, VBN4 Signal and Information Processing, The Faculty of Engineering and Science (ENG), Aalborg University, VBN5 Agilent Technologies
This paper investigates the performance of different reconstruction algorithms in discrete blind multi-coset sampling. Multi-coset scheme is a promising compressed sensing architecture that can replace traditional Nyquist-rate sampling in the applications with multi-band frequency sparse signals. The performance of the existing compressed sensing reconstruction algorithms have not been investigated yet for the discrete multi-coset sampling. We compare the following algorithms – orthogonal matching pursuit, multiple signal classification, subspace-augmented multiple signal classification, focal under-determined system solver and basis pursuit denoising. The comparison is performed via numerical simulations for different sampling conditions. According to the simulations, focal under-determined system solver outperforms all other algorithms for signals with low signal-to-noise ratio. In other cases, the multiple signal classification algorithm is more beneficial.
Proceedings of the 12th Ieee International Symposium on Signal Processing and Information Technology, 2012, p. 147-152
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IEEE International Symposium on Signal Processing and Information Technology, 2012