Model order selection for sampling FRI signals

Xiaoyao Wei; Pier Luigi Dragotti

ICASSP 2017accepted0 citations

Model order selection for sampling FRI signals

Abstract

Recently it has been shown that specific classes of non-bandlimited signals known as signals with finite rate of innovation (FRI) can be perfectly reconstructed by using appropriate sampling kernels and reconstruction schemes. The knowledge of the model order (i.e. the rate of innovation) is essential for correct reconstruction. In view of this, we devise an algorithm which can robustly identify the rate of innovation prior to the signal reconstruction in different noise levels and this extends the current scheme to a universal one that works with signals with unknown rate of innovation and using arbitrary kernels. We use the `guaranteed performance' criterion to assess the performance and show a success rate close to 100% for SNR up to 10dB.

BibTeX

@inproceedings{icassp2017_modelorderselect,
  title = {Model order selection for sampling FRI signals},
  author = {Xiaoyao Wei and Pier Luigi Dragotti},
  booktitle = {ICASSP 2017},
  year = {2017}
}