Image space projection for low-rank signal estimation: Modified Gauss-Newton method

TL;DR

This paper introduces a modified Gauss-Newton method utilizing image space projection for low-rank signal estimation, offering improved numerical stability and computational efficiency for Hankel structured problems.

Contribution

It proposes a novel, stable, and fast algorithm for low-rank signal estimation using image space projection within a weighted Gauss-Newton framework.

Findings

The method achieves stable and accurate low-rank signal estimation.

It has computational complexity of O(N r^2 + N p^2 + r N log N).

Compared to state-of-the-art, it improves stability and efficiency.

Abstract

The paper is devoted to the solution of a weighted nonlinear least-squares problem for low-rank signal estimation, which is related to Hankel structured low-rank approximation problems. A modified weighted Gauss-Newton method, which uses projecting on the image space of the signal, is proposed to solve this problem. The advantage of the proposed method is the possibility of its numerically stable and fast implementation. For a weight matrix, which corresponds to an autoregressive process of order $p$, the computational cost of iterations is $O(N r^2 + N p^2 + r N \log N)$, where $N$ is the time series length, $r$ is the rank of the approximating time series. For developing the method, some useful properties of the space of time series of rank $r$ are studied. The method is compared with state-of-the-art methods based on the variable projection approach in terms of numerical stability, accuracy and computational cost.