Ramanujan subspace pursuit for signal periodic decomposition

TL;DR

This paper introduces the Ramanujan subspace pursuit (RSP), a novel greedy algorithm for decomposing signals into exactly periodic components, with a faster version (FRSP) that reduces computational cost and outperforms existing methods in period estimation.

Contribution

The paper proposes the RSP and FRSP algorithms for signal periodic decomposition, introducing a new periodicity metric and improving computational efficiency.

Findings

RSP accurately decomposes signals into periodic components.

FRSP reduces computational complexity to O(K N log N).

RSP outperforms existing algorithms in period estimation.

Abstract

The period estimation and periodic decomposition of a signal are the long-standing problems in the field of signal processing and biomolecular sequence analysis. To address such problems, we introduce the Ramanujan subspace pursuit (RSP) based on the Ramanujan subspace. As a greedy iterative algorithm, the RSP can uniquely decompose any signal into a sum of exactly periodic components, by selecting and removing the most dominant periodic component from the residual signal in each iteration. In the RSP, a novel periodicity metric is derived based on the energy of the exactly periodic component obtained by orthogonally projecting the residual signal into the Ramanujan subspace, and is then used to select the most dominant periodic component in each iteration. To reduce the computational cost of the RSP, we also propose the fast RSP (FRSP) based on the relationship between the periodic subspace and the Ramanujan subspace, and based on the maximum likelihood estimation of the energy of the periodic component in the periodic subspace. The fast RSP has a lower computational cost and can decompose a signal of length $N$ into the sum of $K$ exactly periodic components in $ \mathcal{O}(K N\log N)$. In addition, our results show that the RSP outperforms the current algorithms for period estimation.