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Proceedings Paper

MAP recovery of polynomial splines from compressive samples and its application to vehicular signals
Author(s): Akira Hirabayashi; Satoshi Makido; Laurent Condat
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Paper Abstract

We propose a stable reconstruction method for polynomial splines from compressive samples based on the maximum a posteriori (MAP) estimation. The polynomial splines are one of the most powerful tools for modeling signals in real applications. Since such signals are not band-limited, the classical sampling theorem cannot be applied to them. However, splines can be regarded as signals with finite rate of innovation and therefore be perfectly reconstructed from noiseless samples acquired at, approximately, the rate of innovation. In noisy case, the conventional approach exploits Cadzow denoising. Our approach based on the MAP estimation reconstructs the signals more stably than not only the conventional approach but also a maximum likelihood estimation. We show the effectiveness of the proposed method by applying it to compressive sampling of vehicular signals.

Paper Details

Date Published: 26 September 2013
PDF: 7 pages
Proc. SPIE 8858, Wavelets and Sparsity XV, 88580U (26 September 2013); doi: 10.1117/12.2024039
Show Author Affiliations
Akira Hirabayashi, Ritsumeikan Univ. (Japan)
Satoshi Makido, Toyota Central R&D Labs., Inc. (Japan)
Laurent Condat, GIPSA-lab (France)


Published in SPIE Proceedings Vol. 8858:
Wavelets and Sparsity XV
Dimitri Van De Ville; Vivek K. Goyal; Manos Papadakis, Editor(s)

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