Estimate the spectrum of affine dynamical systems from partial observations of a single trajectory data

TL;DR

This paper addresses the challenge of estimating the spectrum of a system matrix in affine dynamical systems from limited single-trajectory data, providing theoretical guarantees and practical algorithms for eigenvalue recovery.

Contribution

It introduces new conditions for eigenvalue recoverability, generalizes classical spectral estimation methods, and validates algorithms through diverse real-world applications.

Findings

Unique determination of a subset of eigenvalues from data

Conditions linking observation points to eigenvalue recoverability

Algorithms effective even with non-ideal data

Abstract

In this paper, we study the nonlinear inverse problem of estimating the spectrum of a system matrix, that drives a finite-dimensional affine dynamical system, from partial observations of a single trajectory data. In the noiseless case, we prove an annihilating polynomial of the system matrix, whose roots are a subset of the spectrum, can be uniquely determined from data. We then study which eigenvalues of the system matrix can be recovered and derive various sufficient and necessary conditions to characterize the relationship between the recoverability of each eigenvalue and the observation locations. We propose various reconstruction algorithms, with theoretical guarantees, generalizing the classical Prony method, ESPIRIT, and matrix pencil method. We test the algorithms over a variety of examples with applications to graph signal processing, disease modeling and a real-human motion dataset. The numerical results validate our theoretical results and demonstrate the effectiveness of the proposed algorithms, even when the data did not follow an exact linear dynamical system.