Retrieving sinusoids from nonuniformly sampled data using recursive formulation

TL;DR

This paper introduces a recursive formulation-based method for decomposing both uniformly and nonuniformly sampled 1D signals into sparse sinusoids, effectively estimating parameters and number of sinusoids even with limited or irregular data.

Contribution

The paper presents a novel recursive sinusoid formulation combined with regression and optimization for accurate sparse sinusoid decomposition from irregular samples.

Findings

Effective in identifying sinusoids from partial cycles

Capable of detecting linear trends when frequency approaches zero

Performs well on irregularly sampled signals in noise

Abstract

A heuristic procedure based on novel recursive formulation of sinusoid (RFS) and on regression with predictive least-squares (LS) enables to decompose both uniformly and nonuniformly sampled 1-d signals into a sparse set of sinusoids (SSS). An optimal SSS is found by Levenberg-Marquardt (LM) optimization of RFS parameters of near-optimal sinusoids combined with common criteria for the estimation of the number of sinusoids embedded in noise. The procedure estimates both the cardinality and the parameters of SSS. The proposed algorithm enables to identify the RFS parameters of a sinusoid from a data sequence containing only a fraction of its cycle. In extreme cases when the frequency of a sinusoid approaches zero the algorithm is able to detect a linear trend in data. Also, an irregular sampling pattern enables the algorithm to correctly reconstruct the under-sampled sinusoid. Parsimonious nature of the obtaining models opens the possibilities of using the proposed method in machine learning and in expert and intelligent systems needing analysis and simple representation of 1-d signals. The properties of the proposed algorithm are evaluated on examples of irregularly sampled artificial signals in noise and are compared with high accuracy frequency estimation algorithms based on linear prediction (LP) approach, particularly with respect to Cramer-Rao Bound (CRB).