Orthogonality is superiority in piecewise-polynomial signal segmentation and denoising

0500658 - ÚTIA 2020 RIV US eng J - Journal Article
Novosadová, M. - Rajmic, P. - Šorel, Michal
Orthogonality is superiority in piecewise-polynomial signal segmentation and denoising.
EURASIP Journal on Advances in Signal Processing. Roč. 2019, č. 1 (2019), č. článku 6. ISSN 1687-6180. E-ISSN 1687-6180
R&D Projects: GA ČR(CZ) GA16-13830S
Institutional support: RVO:67985556
Keywords : Signal segmentation * Signal smoothing * Signal approximation
OECD category: Computer hardware and architecture
Impact factor: 1.140, year: 2019
Method of publishing: Open access
http://library.utia.cas.cz/separaty/2019/ZOI/sorel-0500658.pdf https://asp-eurasipjournals.springeropen.com/articles/10.1186/s13634-018-0598-9

Segmentation and denoising of signals often rely on the polynomial model which assumes that every segment is a polynomial of a certain degree and that the segments are modeled independently of each other. Segment borders (breakpoints) correspond to positions in the signal where the model changes its polynomial representation. Several signal denoising methods successfully combine the polynomial assumption with sparsity. In this work, we follow on this and show that using orthogonal polynomials instead of other systems in the model is beneficial when segmenting signals corrupted by noise. The switch to orthogonal bases brings better resolving of the breakpoints, removes the need for including additional parameters and their tuning, and brings numerical stability. Last but not the least, it comes for free!
Permanent Link: http://hdl.handle.net/11104/0293326