Title
Adaptive sub-linear Fourier algorithms
Abstract
We present a new deterministic algorithm for the sparse Fourier transform problem, in which we seek to identify k << N significant Fourier coefficients from a signal of bandwidth N. Previous deterministic algorithms exhibit quadratic runtime scaling, while our algorithm scales linearly with k in the average case. Underlying our algorithm are a few simple observations relating the Fourier coefficients of time-shifted samples to unshifted samples of the input function. This allows us to detect when aliasing between two or more frequencies has occurred, as well as to determine the value of unaliased frequencies. We show that empirically our algorithm is orders of magnitude faster than competing algorithms.
Year
Venue
Field
2012
CoRR
Discrete-time Fourier transform,Mathematical optimization,Algorithm,Quadratic equation,Fourier transform,Aliasing,Fourier series,Bandwidth (signal processing),Deterministic algorithm,Discrete Fourier transform,Mathematics
DocType
Volume
Citations 
Journal
abs/1207.6368
8
PageRank 
References 
Authors
0.56
9
3
Name
Order
Citations
PageRank
David Lawlor1382.36
Yang Wang25910.33
Andrew J. Christlieb318419.03