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When Randomness Helps in Undersampling

Signals cannot always be sampled at their full desired resolution. In this Education article, we explore the benefits of randomly subsampling a signal's frequency spectrum. Whereas uniform subsampling introduces structural artifacts in the time series, random subsampling introduces a type of noise whose behavior we quantify. This analysis gives insight into the reasons why random sampling is employed in more sophisticated processing techniques such as compressive sensing. Our treatment involves topics such as frequency analysis, aliasing, and convolution, which are commonly encountered in undergraduate courses on signal processing or engineering mathematics. Meanwhile, we also draw from concepts in probability and statistics which are rarely discussed at the undergraduate level in the contexts of frequency analysis, aliasing, and convolution. The signal processing codes and data used in this work can be downloaded from https://mines.edu/~mwakin/software.

Ketersediaan

Informasi Detail

Judul Seri

SIAM REVIEW; Vol.64 No.4 December 2022

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 1062-1080

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

STATISTICS
DIGITAL SIGNAL PROCESSING
COMPRESSIVE SENSING

Info Detail Spesifik

https://doi.org/10.1137/21M1441006

Pernyataan Tanggungjawab

Roel Snieder, Michael B. Wakin

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