Low-Complexity Iterative Sinusoidal Parameter Estimation

TL;DR

This paper introduces a low-complexity iterative method for sinusoidal parameter estimation that reduces computational load and improves accuracy over existing methods like matching pursuits and time-frequency reassignment.

Contribution

It presents a novel iterative approach based on linearisation around initial frequency estimates, significantly lowering complexity and enhancing accuracy.

Findings

Complexity is reduced to O(LN), much less than matching pursuits.

The method outperforms matching pursuits and time-frequency reassignment in accuracy.

Experimental results confirm improved estimation performance.

Abstract

Sinusoidal parameter estimation is a computationally-intensive task, which can pose problems for real-time implementations. In this paper, we propose a low-complexity iterative method for estimating sinusoidal parameters that is based on the linearisation of the model around an initial frequency estimate. We show that for N sinusoids in a frame of length L, the proposed method has a complexity of O(LN), which is significantly less than the matching pursuits method. Furthermore, the proposed method is shown to be more accurate than the matching pursuits and time frequency reassignment methods in our experiments.